International Youth Conference on Electronics, Telecommunications and Information Technologies: Proceedings of the YETI 2020, St. Petersburg, Russia [1st ed.] 9783030588670, 9783030588687

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Springer Proceedings in Physics 255

Elena Velichko Maksim Vinnichenko Victoria Kapralova Yevgeni Koucheryavy   Editors

International Youth Conference on Electronics, Telecommunications and Information Technologies Proceedings of the YETI 2020, St. Petersburg, Russia

Springer Proceedings in Physics Volume 255

Indexed by Scopus The series Springer Proceedings in Physics, founded in 1984, is devoted to timely reports of state-of-the-art developments in physics and related sciences. Typically based on material presented at conferences, workshops and similar scientific meetings, volumes published in this series will constitute a comprehensive up-to-date source of reference on a field or subfield of relevance in contemporary physics. Proposals must include the following: – name, place and date of the scientific meeting – a link to the committees (local organization, international advisors etc.) – scientific description of the meeting – list of invited/plenary speakers – an estimate of the planned proceedings book parameters (number of pages/ articles, requested number of bulk copies, submission deadline).

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Elena Velichko Maksim Vinnichenko Victoria Kapralova Yevgeni Koucheryavy •

•

•

Editors

International Youth Conference on Electronics, Telecommunications and Information Technologies Proceedings of the YETI 2020, St. Petersburg, Russia

123

Editors Elena Velichko Peter the Great St. Petersburg Polytechnic University St. Petersburg, Russia Victoria Kapralova Peter the Great St. Petersburg Polytechnic University St. Petersburg, Russia

Maksim Vinnichenko Peter the Great St. Petersburg Polytechnic University St. Petersburg, Russia Yevgeni Koucheryavy Tampere University of Technology Tampere, Finland

ISSN 0930-8989 ISSN 1867-4941 (electronic) Springer Proceedings in Physics ISBN 978-3-030-58867-0 ISBN 978-3-030-58868-7 (eBook) https://doi.org/10.1007/978-3-030-58868-7 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The International Youth Conference on Electronics, Telecommunications and Information Technologies (YETI 2020) was organized by Peter the Great St. Petersburg Polytechnic University (Saint Petersburg, Russia) and took place on July 10–11, 2020. The YETI 2020 conference provided an opportunity to young researchers and early career scientists to share recent achievements, current trends and knowledge as well as future prospects in the fields of development and design of nanoelectronic and telecommunication devices, signal processing, material science and nanotechnology, photonics, optical and information technologies. The conference attracted 82 papers from eight countries, which generated lively discussions and fruitful debates. As usual at Polytechnic University conferences, a contest for the best paper was held to encourage the commitment of young scientists in conference activities. The conference was supported by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020”. The official conference Web site is http://yeti.spbstu.ru/. July 2020

Elena Velichko Maksim Vinnichenko Victoria Kapralova Yevgeni Koucheryavy

v

Organization

Program Chair Elena Velichko

Peter the Great St. Petersburg Polytechnic University

Program Committee Ge Dong Viktoria Kapralova Yevgeny Koucheryavy Hayk Sarkisyan Elena Velichko Maxim Vinnichenko Wei Xue Sergey Zavjalov

Tsinghua University Peter the Great St. Petersburg Polytechnic University Tampere University Russian-Armenian University Peter the Great St. Petersburg Polytechnic University Peter the Great St. Petersburg Polytechnic University Harbin Engineering University Peter the Great St. Petersburg Polytechnic University

Technical Committee Viktoria Kapralova Elena Velichko Maxim Vinnichenko

Peter the Great St. Petersburg Polytechnic University Peter the Great St. Petersburg Polytechnic University Peter the Great St. Petersburg Polytechnic University

vii

viii

Elina Nepomnyashchaya Ekaterina Savchenko Maksim Baranov Andrei Medvedev Aleksandr Gelgor Ilya Lavrenyuk

Organization

Peter the Great University Peter the Great University Peter the Great University Peter the Great University Peter the Great University Peter the Great University

St. Petersburg Polytechnic St. Petersburg Polytechnic St. Petersburg Polytechnic St. Petersburg Polytechnic St. Petersburg Polytechnic St. Petersburg Polytechnic

Contents

Part I 1

2

3

4

5

Electronics and Nanotechnologies

Integrated Ultra-Low PowerRF-DC Converter for Wireless Passive Microdevices . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander S. Sinyukin and Boris G. Konoplev Multistage Depressed Collector with Azimuthal Magnetic Field for the DEMO Prototype Gyrotron . . . . . . . . . . . . . . . . . . . . . . . . . Oleg I. Louksha, Pavel A. Trofimov, Vladimir N. Manuilov, and Mikhail Yu. Glyavin

3

11

Improvement of the Microwave Strip Devices Prototyping Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexey S. Podstrigaev, Andrey V. Smolyakov, and Igor V. Maslov

19

Peculiar Properties of Nuclear Magnetic Resonance in Dispersed Magnetically Ordered Nanostructures and Requirements for Radiospectroscopic Equipment for Its Observation . . . . . . . . . . Artem Khudyakov, Ivan Pleshakov, Yurii Kuzmin, Anton Mazur, Efim Bibik, and Mikhail Shlyagin

29

The Statistical Description of de Haas–van Alphen Oscillations in Silicon Nanosandwich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladimir Romanov, Vadim Kozhevnikov, Vladimir Grigorev, and Mariia Filianina

6

Infralow Frequency Dielectric Spectroscopy of PMN Relaxor . . . . . Aleksandr Vakulenko, Sergei Vakhrushev, and Ekaterina Koroleva

7

Plasma Thrusters for In-Space Propulsion; New Trends and Physical Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dmitry Dyubo, Jorge González, Oleg Tsybin, and Luis Conde

37

45

55

ix

x

8

9

Contents

Optical Absorption and Photoluminescence of Cylindrical Quantum Dot with Modified Pöschl-Teller and Morse Confining Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tigran V. Kotanjyan and Hovhannes Kh. Tevosyan The Band Gap Controllability of Boron Nitride Nanotube with Carbon Atoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irena M. Danglyan

10 Investigation of Selectivity and Reproducibility Characteristics of Gas Capacitive MIS Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maya Etrekova, Artur Litvinov, Nikolay Samotaev, Dmitry Filipchuk, Konstantin Oblov, and Alexey Mikhailov 11 Printed Miniaturized Platinum Heater on Ultra-Thin Ceramic Membrane for MOX Gas Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Fritsch, Sindy Mosch, Mykola Vinnichenko, Nikolai Trofimenko, Mihails Kusnezoff, Franz-Martin Fuchs, Lena Wissmeier, Nikolay Samotaev, Maya Etrekova, and Dmitry Filipchuk

65

79

87

97

12 SOI Based Micro-Bead Catalytic Gas Sensor . . . . . . . . . . . . . . . . . 105 Nikolay Samotaev, Alexander Pisliakov, Dmitry Filipchuk, Maya Etrekova, Ferenc Biro, Csaba Ducso, and István Bársony 13 Precision Spectrometric Search Dosimeter-Radiometer Based on a Matrix SiPM, Designed to Restore the Geometry of Ionizing Radiation Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Vitalii Florentsev, Gennady Baryshev, Aleksandr Berestov, Anastasia Kondrateva, and Aleksandr Biryukov 14 Flexible Piezoelectric Nanogenerator: PVDF-CsPbBr6 Nanocomposite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 B. A. Darshan, Kumar E. Dushyantha, H. S. Jithendra, A. M. Raghavendra, Kumar M. S. Praveen, and B. S. Madhukar 15 Formation of Functional Conductive Carbon Coating on Si by C60 Ion Beam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Vladimir Pukha, Julia Popova, Mahdi Khadem, Dae-Eun Kim, Igor Khodos, Alexander Shakhmin, Maxim Mishin, Kirill Krainov, Andrei Titov, and Platon Karaseov 16 Degradation of GaN Conductivity Under Irradiation with Swift Ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Platon A. Karaseov, Ashish Kumar, Andrei I. Struchkov, Andrei I. Titov, Kandasami Asokan, Dinakar Kanjilal, and Ambuj Tripathi

Contents

xi

17 Impact of Chemical Effects on Topography and Thickness of Modified GaN Surface Layers Bombarded by F and Ne Ions . . . 151 Andrei I. Struchkov, Konstantin V. Karabeshkin, Alexander V. Arkhipov, Viktor A. Filatov, Platon A. Karaseov, Alexander Yu. Azarov, and Andrei I. Titov 18 A Symmetrical Design of a Microstrip Tunable Bandpass Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Victoria Karpova and Nikita Ivanov 19 Implementation of Moshinsky Atom Model for Electron Gas in Quantum Dots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Mher A. Mkrtchyan, David B. Hayrapetyan, Eduard M. Kazaryan, Hayk A. Sarkisyan, Dmitry A. Firsov, and Maxim Y. Vinnichenko 20 Characterization of Nitride Silicon Layers Sin:x Enriched in Silicon at Different Stoichiometries by Photocurrent SpectroscopyMethod and Mass Spectrometry of Secondary Ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Linda Boudjemila 21 Strongly Prolate Conical Quantum Dot in an External Electric Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Khachik S. Khachatryan, David B. Hayrapetyan, Eduard M. Kazaryan, and Hayk A. Sarkisyan 22 Linear and Nonlinear Optical Properties of Strongly Oblate Ellipsoidal Quantum Dot in the Presence of Electric Field . . . . . . . 193 Gagik Ohanyan 23 Research on Transition Between Substrate Integrated Waveguide and Microstrip Line . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Chen’ Yu Part II

Photonics and Optical Information

24 Visible Light Communication System with Changing Lighting Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Daniil S. Shiryaev, Olga A. Kozyreva, Ivan S. Polukhin, Aleksey I. Borodkin, Maksim A. Odnoblyudov, and Vladislav E. Bougrov 25 Chromatic Dispersion in Subcarrier Wave Quantum Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Fedor Kiselev, Roman Goncharov, and Eduard Samsonov 26 Development of a Method for Assessing of the Oxygen Supply of Tissues Based on a Multi-channel Spectrum Analyzer . . . . . . . . 233 Maria S. Mazing, Anna Yu. Zaitceva, and Yuriy J. Kislyakov

xii

Contents

27 Possibilities of Using Optical Solitons in High-Speed Systems . . . . . 241 Elena I. Andreeva and Ivan A. Potapov 28 Fluorescence Quenching of Tetraphenylporphyrin-Fullerene Molecular Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Marina A. Elistratova, Margarita O. Koroleva, and Irina B. Zakharova 29 Gold Nanoparticle Array Formation by Low-Temperature Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Polina Bespalova, Yakov Enns, Tatyana Kunkel, Vasilii Balanov, Anastasiya Speshilova, Alexandr Vorobyev, Maxim Mishin, and Platon Karaseov 30 Computer Modeling of Fiber Optic Current Sensor . . . . . . . . . . . . 263 Valentina Temkina, Andrei Medvedev, and Alexey Mayzel 31 Photometry Setup for Dynamic Dye Concentration Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Ilya Kolokolnikov, Ilya Lavrenyuk, Ekaterina Savchenko, Maksim Baranov, and Elena Savchenko 32 Estimation of Nanoparticles Sizes by Laser Correlation Spectroscopy Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Zoja Zabalueva, Elina Nepomnyashchaya, Elena Velichko, Ge Dong, and Tatyana Kudryashova 33 Experimental Study of Frequency Modulation in Single-Frequency Lasers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Philipp V. Skliarov, Konstantin V. Muravyov, and Aleksei O. Kostromitin 34 Temperature Dependence of Acousto-Optic Polarization Mode Conversion Peak Frequency and Efficiency . . . . . . . . . . . . . . . . . . . 299 Andrey V. Varlamov, Petr M. Agrusov, Igor V. Il’ichev, Vladimir V. Lebedev, Aleksandr V. Shamrai, and Serguei I. Stepanov 35 Intermodal Fiber Interferometer with Scanning Laser and Correlation Signal Processing: An Experimental Study . . . . . . 307 Alexandr Petrov, Ivan Chapalo, and Oleg Kotov 36 Development of a Monitoring System the Flow of Charged Particles for Analysis of the NanosatelliteFlight Path . . . . . . . . . . . 317 Dennis Malygin and Jean R. Stepanov 37 The UV-Vis Transmission Spectra of Ferromagnetic Fluids . . . . . . 327 Arseniy Alekseev, Elina Nepomnyashchaya, Elena Velichko, and E. Shan

Contents

xiii

38 Calculation of Parameters of Positive Column in Laser Tubes of Variable Diameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Vadim Kozhevnikov, Vadim Privalov, Alexander Fotiadi, and Valery Shemanin 39 Radiation Power of He–Ne Laser with Different Geometry of the Tube Cross Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Vadim Kozhevnikov, Vadim Privalov, and Valery Shemanin 40 Laser System for the Average Volume-Surface Diameter of Aerosol Particles Measuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Vadim E. Privalov, Vladimir V. Dyachenko, Alina A. Kovalyova, and Valery G. Shemanin 41 The Compensation of Radiation-Induced Losses in the Fiber Optic Communication Line in Its Operation Mode . . . . . . . . . . . . . 359 Diana S. Dmitrieva, Valeria M. Pilipova, and Khuan Dominges Part III

Information Technologies and Signal Processing

42 Object Classification Based on Channel State Information Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Maksim A. Lopatin, Stanislav A. Fyodorov, Sergey V. Zavjalov, and Dong Ge 43 Implementation of a Broadband Horn Antenna with High Level of Cross-polarization Discrimination in Microwave Inspection Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Viktor V. Meshcheriakov, Semen N. Semenov, and Valentin I. Dudkin 44 Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges . . . . . . . . . . . . 383 Kseniya V. Trusova 45 Research on FBMC/OQAM Spectral and Energy Characteristics for Different Prototype Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Lavrenyuk Ilya, Maksimova Elizaveta, and Sadovaya Yekaterina 46 Multiple Object Tracking Using Convolutional Neural Network on Aerial Imagery Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Sergey B. Makarov, Vitalii A. Pavlov, Andrei K. Bezborodov, Aleksey I. Bobrovskiy, and Dong Ge 47 Application of a Convolutional Neural Network for Detection of Ignition Sources and Smoke . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Ilya R. Aliev, Vitalii A. Pavlov, Sergey V. Zavjalov, and Yekaterina Sadovaya

xiv

Contents

48 ROM-Based Encoder with Bubble Error Correction . . . . . . . . . . . 429 Mikhail A. Bellavin and Dmitry O. Budanov 49 Performance Analysis for Massive MIMO Systems Based on Quadriga Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Saeed Alsabbagh and Aleksandr Gelgor 50 CPU-Based FPGA Algorithm Model of Fiber Optic Current Sensor Demodulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Alexey Mayzel, Andrei Medvedev, and Valentina Temkina 51 Configuring the Interval Target in a Multilayer Feedforward Neural Network on the Example of the Problem of Medical Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Eugeniy Mirkin and Elena Savchenko 52 Investigation of the Effect of ADC Imperfections on the Amplitude Spectrum Measurement Error for a Quadrature Demodulator Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 Alexander R. Senchenko and Andrey N. Serov 53 Analysis of the Possibility of Correcting the Shape of the Average Cardiac Complex in the Presence of Synchronization Errors During Accumulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Irina A. Kondratyeva, Alexander S. Krasichkov, Eugene M. Nifontov, and Fabien Shikama 54 Glucose Variability in Gestational Diabetes Patients with Different Glycemic Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Evgenii Pustozerov, Nikol Sachkova, Aleksandra Tkachuk, Elena Vasukova, Aleksandra Dronova, Tatiana Pervunina, Elena Grineva, and Polina Popova 55 Application of Simulink for Research of a Frequency Measurement Method Based on Quadrature Demodulation Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Alexander R. Senchenko and Andrey N. Serov 56 A Model of the Cross-correlation Method for Processing Unknown-Form Signals in a Passive Multi-position Radar System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Ivan S. Serdiukov 57 Transmission Errors in Screen-Camera Link System . . . . . . . . . . . 527 Nikita Beliakov, Aleksei Borodkin, Ivan Polukhin, Olga Kozyreva, Daniil Shiryayev, Anna Kamarchuk, Maksim Odnoblyudov, and Vladislav Bougrov

Contents

xv

58 Visualization of Three-Dimensional Light Bullets Propagation in Nanotubes Taking into Account the Mechanical Tension and Magnetic Field Using Graphics Processor . . . . . . . . . . . . . . . . 537 Dmitry Skvortsov and Natalia Konobeeva 59 Receiver for m-ary Radio Communication System Between Motile Objects in the Microwave Range . . . . . . . . . . . . . . . . . . . . . 545 Igor A. Zharikov 60 Justification of the Choice of Signal Processing Method and Its Implementation in the Digital Part of the Receiver for Radar Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Angelina V. Moroz, Kirill Y. Malanin, and Ivan K. Savelev 61 Features of Signal Processing in the Study of Defects in Metallic Mediums Using an Electromagnetic Acoustic Wave . . . . . . . . . . . . 557 Anna A. Mozhayko and Sergey A. Manninen 62 Assessment of Baroreflex Mechanism by Joint Analysis of Arterial Blood Pressure and Heart Rate . . . . . . . . . . . . . . . . . . . 565 Aleksei A. Anisimov, Nikolai B. Suvorov, Natalia L. Frolova, Aleksandr V. Belov, Elizaveta A. Agapova, and Timofei V. Sergeev 63 The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Mojtaba Mousavi, Faridoddin Shariaty, Mahdi Orooji, and Elena Velichko 64 Method of Fetal Movement Registration for Remote Monitoring Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Yulia O. Bobrova, Olga N. Kapranova, and Kseniya V. Filipenko 65 Determination of the Structure Contour Parameters in Biological Films for the Development of the Cuneiform Dehydration Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Baranov Maksim and Malleville Tristan Part IV

Telecommunications and Navigation Systems

66 Simulation of a Half-Duplex Protocol in a Meteor Radio Communication System to Estimate MessageDelivery Time . . . . . . 605 Mashkova Ekaterina, Zavjalov Sergey, Tatyana Kudryashova, and Xue Wei 67 Experimental Studies of the Wideband VCO’s Tuning Characteristics Linearization Using an ActiveDiode Converter . . . . 615 Ekaterina I. Khabitueva, Victor M. Malyshev, and Alexander B. Nikitin

xvi

Contents

68 Ultra-Low-Noise Reference Oscillator Based on a Dielectric Resonator with Mechanical and Electrical Frequency Tuning . . . . 621 Egor V. Egorov, Anastasia V. Ivanova, Sergey B. Makarov, and Victor M. Malyshev 69 Interactive Applicationfor the Synthesis of Communication Lines with Antenna Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 Tokhir R. Raimzhanov, Sergey V. Kuzmin, and Konstantin O. Korovin 70 Model of Integrated Radio Access and Wireless Backhaul for 5th Generation Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Irina Stepanets, Grigoriy Fokin, and Sergei Odoevskii 71 Optical Signal Processing Method of the Rubidium-87 Quantum Frequency Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Anton P. Valov, Valentin I. Dudkin, Nikita A. Lukashev, and Shen’ Chzjinhan’ 72 Comparison of Channel Estimation Methods for Underwater Acoustic Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Artem Chilingarov, Evgenii Vylegzhanin, Bang Khuc, Danila Puzko, Yuriy Batov, and Aleksandr Gelgor 73 Comparison of PAPR Reduction Techniques for OFDM Transmission Over Underwater Acoustic Channel . . . . . . . . . . . . . 665 Evgenii Vylegzhanin, Artem Chilingarov, Bang Khuc, Danila Puzko, Yuriy Batov, and Aleksandr Gelgor 74 Design of Airborne Dual-Band Low-Profile Antenna Array . . . . . . 675 Alexey V. Andropov, Sergey V. Kuzmin, and Konstantin O. Korovin 75 Preamble Signals for Detection Timing and Doppler Synchronization in Underwater Acoustic Communications . . . . . . . 683 Bang Khuc, Evgenii Vylegzhanin, Artem Chilingarov, Danila Puzko, and Yuriy Batov 76 Analysis of the Capacity of an LTE Carrier Based on Simulation and Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 Damara A. Verdugo, Pablo A. Lupera, and Roman V. Davydov 77 Beamforming and Spatial Multiplexing Performance Evaluation in 5G Ultra-Dense Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709 Vitaly Lazarev and Grigoriy Fokin 78 Blind Synchronous Reception Algorithm for UWB Signals . . . . . . . 719 Aleksandr Volvenko

Contents

xvii

79 Direct TDOA Based Positioning in Satellite Geolocation . . . . . . . . . 729 Pavel Kistanov, Elizaveta Shcherbinina, Alexander Titov, Oleg Tsarik, and Igor Tsikin 80 Solution of the Dynamic System of Quantum Kinetic Equations for Hot Atomic Gas Interacting with Laser Radiation . . . . . . . . . . 739 Konstantin Barantsev, Alexey Kuraptsev, and Andrey Litvinov 81 Experimental Investigation of Radiation Characteristics of the Controlled Slot Antenna Array . . . . . . . . . . . . . . . . . . . . . . . 747 Vabishchevich Daniil, Kiseleva Ekaterina, Sochava Alexander, and Bogachev Sergei 82 Wireless Wi-Fi module Testing Procedure in Gigabyte Passive Optical Network to Optical Network Terminal of Equipment . . . . . 755 Ekaterina M. Gryaznova 83 The Orbits Shape Influence of the Navigation Satellite Systems on Positioning Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Ekaterina Borisevich, Alexandr Korolev, and Roman Lozov

Part I

Electronics and Nanotechnologies

Chapter 1

Integrated Ultra-Low Power RF-DC Converter for Wireless Passive Microdevices Alexander S. Sinyukin

and Boris G. Konoplev

Abstract Integrated RF-DC converter for wireless passive microdevices implemented using standard CMOS technology was proposed. The model of multi-stage converter considering voltage drops on nanoscale MOS transistors for weak and strong inversion regions was presented. The converter operation at different input power levels including ultra-low levels was studied.





Keywords Energy harvesting Voltage multiplier Subthreshold operation mode Internet of things

1.1


Nanoscale CMOS


Introduction

Nowadays wireless mobile systems become more and more widespread in logistics, industry, commercial activity and everyday life. Significant fraction of the wireless systems is passive devices, i.e. devices which don’t have internal power supply unit so they have to get energy for operation from related base stations or reader devices. The most principal application areas of wireless passive microdevices are wireless sensor networks (WSN) [1, 2], radio-frequency identification (RFID) [3, 4] and Internet of things (IoT) [5, 6]. In all of these fields the miniaturized passive devices like sensors with transceiver functions or RFID transponders are used. In some applications, e.g. logistic identification operations in stores and warehouses where considerable distances between energy source like reader and related passive devices diminish the transmitted energy, the power level received by passive devices can be too low to provide the power supply. Another way to supply the passive devices lies in gathering energy from ambient space. This process is called energy harvesting or energy scavenging [7, 8] and the energy of radio-frequency A. S. Sinyukin (&)
B. G. Konoplev Southern Federal University, 44, Nekrasovsky lane, Taganrog 347928, Russia e-mail: [email protected] B. G. Konoplev e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_1

3

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A. S. Sinyukin and B. G. Konoplev

radiation from radio and TV stations, cell stations, Wi-Fi and Bluetooth networks could be used as energy source. The radio-frequency energy can’t be straightforwardly utilized for supplying IC of passive device so RF-DC converter should be used. Voltage rectifiers or multipliers based on nanoscale CMOS transistors are often used as RF-DC converter in wireless passive devices [1–3, 6]. This component is supposed to convert the RF energy from receiving antenna to DC voltage and form the voltage level acceptable for supplying the rest circuitry. However the input power level could be very low so the design of RF-DC converters with high power conversion efficiency and voltage multiplication efficiency which are able to provide power supply even in the context of ultra-low input power levels is a very relevant objective. Integrated form of the passive system ensures small sizes, high reliability, high reproducibility and consequently series production of the designed device. Another important factor in the design of passive RF-DC converter is applied technology. With more advanced technologies, e.g. with dielectric isolated wells [9, 10] or low-Vth (zero-Vth) transistors [11, 12], one can design high performance device but the manufacturing cost will be high as well. So in many cases it is preferable to choose standard technology with significantly lower cost. The purpose of this paper is to propose new voltage multiplier modification and to demonstrate the possibility of wireless passive devices operation at ultra-low input power levels.

1.2

Methods

The circuits based on classical Dickson’s multiplier [13] are often used for rectification and multiplication of the input voltage in wireless passive microdevices. In the cases when these circuits are built using MOS transistors gate terminal of each transistor is connected to drain terminal of the same device forming diode structure and body terminal is traditionally connected to the ground [4, 14] as depicted in Fig. 1.1a. For increasing the output voltage and hence for improving the overall efficiency the standard multiplier circuit has been modified (Fig. 1.1b).

a

b

Fig. 1.1 Schematic circuit of a voltage multiplier based on diode-connected MOS transistors with standard a and proposed b connection of MOS terminals

1 Integrated Ultra-Low Power RF-DC Converter …

5

Nanoscale MOS transistors in RF-DC converters of passive devices often have to operate in subthreshold mode due to low input power levels. But majority of previous models [10–12] didn’t considered real voltage drop on diode-connected devices so such models could be inapplicable in ultra-low power applications due to significant measure of inaccuracy. For this reason new model based on EKV model of nanoscale MOS [15] has been proposed. This model characterizes both the operation of circuits with standard connection of MOS terminals and the operation of the proposed circuit. Moreover the developed model takes into consideration the voltage drop on diode-connected transistors both for weak inversion and strong inversion regions. For weak inversion and standard terminal connection: VD;k þ 1 ¼ uT
lnðI=I0 Þ þ VT0 =n þ ðn  1Þ
ðVk þ Vin
C=½C þ CS Þ=n; Vk þ 1 ¼ Vk þ Vin
C=ðC þ CS Þ  VD;k þ 1  1=ðC þ CS Þ=f ;

ð1Þ ð2Þ

where Vk, Vk+1 are the potentials in adjacent nodes; Vin is input voltage amplitude; n is the slope factor; uT is the thermal potential; C is the coupling capacitance; Cs is the parasitic capacitance; VD,k+1 is the voltage across diode-connected device; I is the load current; I0 is the transistor characteristic current; for zero node k = 0, Vk = 0. For the proposed circuit: VD ¼ uT
lnðI=I0 Þ þ VT0 =n:

ð3Þ

Equation for Vk+1 in the case of the proposed circuit is exactly the same as for the standard one except that for the proposed circuit VD,k+1 = VD,k = VD. For weak inversion region I < 1 µA and for strong inversion region I > 10 µA approximately. For the device research and development, CAD system Tanner EDA [16] has been utilized. In particular for electrical circuit analysis and transient processes investigation T-Spice module has been used. BSIM4v4.8.0 model [17] of nanoscale MOS transistor (90 nm CMOS technology [18]) has been chosen for T-Spice simulation. At the same time software package for design and simulation of CMOS ICs Microwind & DSCH [18] has been used for layout forming.

1.3

Results

The results of transient simulation of the 8-stage voltage multiplier based on NMOS transistors with standard terminal connection are showed in Fig. 1.2a. The results of transient simulation of the 8-stage multiplier based on PMOS transistor with proposed terminal connection are showed in Fig. 1.2b. The operating frequency was 2.45 GHz.

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a

b

Fig. 1.2 Simulation results of transient processes in voltage multiplier (stage numbers are designated by digits): the standard circuit a and the proposed modification b

a

b

Fig. 1.3 The effect of transistor sizes of the proposed circuit on the efficiency for different input voltage levels: Vin1 = 0.05 V, Vin2 = 0.1 V, Vin3 = 0.2 V, Vin4 = 0.3 V a; and the effect of number of multiplier stages on the output voltage b

The effect of the transistor sizes (channel width to length ratio, W/L) on the multiplication efficiency can be seen in Fig. 1.3a and the effect of the number of multiplier stages on the output voltage is shown in Fig. 1.3b. In Fig. 1.4a and 1.4b the effect of capacitors values and load resistances on the efficiency is depicted respectively. The multiplication efficiency of the standard NMOS 8-stage multiplier and the proposed PMOS circuit is shown in Fig. 1.5a. The layout of the proposed 8-stage PMOS voltage multiplier implemented using CMOS 90 nm standard technology can be seen in Fig. 1.5b. The overall area of the RF-DC converter is 205  212 µm2.

1 Integrated Ultra-Low Power RF-DC Converter …

a

7

b

Fig. 1.4 The effect of the capacitances a and the load resistance b of the proposed circuit on the multiplication efficiency: C1 = 50 fF, C2 = 100 fF, C3 = 500 fF, C4 = 5 pF; R1 = 1 MX, R2 = 10 MX, R3 = 100 MX; R4 = 1 GX

a

b

Fig. 1.5 Multiplication efficiency of the standard NMOS circuit and the proposed PMOS circuit a and the layout of the proposed device b

1.4

Discussion

In the standard multiplier circuit (Fig. 1.1a) body effect results in increasing voltage drops on transistors when adding multiplying stages (Fig. 1.2a). In the proposed schematic circuit (Fig. 1.1b) the negative influence of the body effect on the output voltage and, hence, efficiency is weakened due to the voltage on diode-connected MOS transistors doesn’t depend on the number of stages, because the voltage between bulk and source terminals for all transistors remains nearly constant. This has been taken into consideration in the proposed multiplier model and can be seen

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from structure of Eq. (1.3) in comparison with (1.1). The voltage increments on every stage are equal for the proposed circuit (Fig. 1.2b), so multiplication efficiency increases, what is confirmed by the results from Fig. 1.5a. The increasing of channel width to length ratio of MOS transistors results to the efficiency growth but to certain limits (Fig. 1.3a). Efficiency for W/L values higher than 35–50 settles at approximately constant level and even decreases for relatively high input voltages. This can be explained by preponderance of reverse currents and capacitance losses impact over the positive effect of the forward current increasing. As was mentioned before the proposed terminal connection allows use cascading to increase the overall output voltage level without losing efficiency. Consequently the multiplication efficiency remains nearly constant when number of stages increases. So in order to reach VDD = 1 V required for 90 nm technology, input power level −21.5 dBm for 16-stage circuit and −17.5 dBm for 8-stage circuit is sufficient (which corresponds to about 55 mV and 85 mV respectively in our case, see Fig. 1.3b). The growth of multiplication efficiency due to increasing the value of coupling and storage capacitors (Fig. 1.4a) can be explained by necessity of certain capacitance level to store charge during charging process. From some point (about 500 fF) the further capacitance enlarging doesn’t lead to any significant efficiency increment. The increasing of load current, i.e. lowering load resistance, results to dramatic drop of the efficiency (Fig. 1.4b). The value 10 nA is close to the lowest possible load current and for operation with such low currents special protocols of storage and usage of the harvested energy with phase separation could be applied [19]. In turn fast growth and following reaching nearly constant value of efficiency characteristics in Fig. 1.4 is presumably related to overcoming the region of PMOS transistor subthreshold voltage and operation in the saturation region.

1.5

Conclusion

The design, modelling and simulation of the integrated RF-DC converter operating at ultra-low input power level and implemented using standard CMOS technology were conducted in this work. The results of simulation showed that wireless passive microdevice like passive RFID tag can operate at ultra-low input power level (lower −20 dBm) if appropriate parameters are chosen. The proposed circuit not only provide high performance but also makes its implementation possible using standard technology. The obtained results could be useful for designers of passive microdevices and wireless power supply systems. Acknowledgements The reported study was funded by RFBR, project number 19-37-90018.

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References 1. T. Umeda, A 950-MHz rectifier circuit for sensor network tags with 10-m distance. IEEE J. Solid-State Circ. 41(1), 35–41 (2006) 2. J. Yi, Analysis and design strategy of UHF micro-power CMOS rectifiers for micro-sensor and RFID applications. IEEE Trans. Circuits Syst. I Regul. Pap. 54(1), 153–166 (2007) 3. S.-Y. Wong, Power efficient multi-stage CMOS rectifier design for UHF RFID tags. Integr. VLSI J. 44(3), 242–255 (2011) 4. G. De Vita, Design criteria for the RF section of UHF and microwave passive RFID transponders. IEEE Trans. Microw. Theory Tech. 53(9), 2978–2990 (2005) 5. F. Gutierrez, Fully-integrated converter for low-cost and low-size power supply in internet-of-things applications. Electronics 6(38), 1–20 (2017) 6. U. Guler, A reconfigurable passive RF-to-DC converter for wireless IoT applications. IEEE Trans. Circuits Syst. II Express Briefs 66(11), 1800–1804 (2019) 7. C.R. Valenta, Harvesting wireless power: survey of energy-harvester conversion efficiency in far-field, wireless power transfer systems. IEEE Microwave Mag. 15(4), 108–120 (2014) 8. L.-G. Tran, RF power harvesting: a review on designing methodologies and applications. Micro Nano Syst. Lett. 5(14), 1–16 (2017) 9. G. Gosset, Very high efficiency 13.56 MHz RFID input stage voltage multipliers based on ultra low power MOS diodes, in 2008 IEEE International Conference on RFID, Las Vegas, NV, USA (IEEE, 2008), pp. 134–140 10. J.-P. Curty, A model for µ-power rectifier analysis and design. IEEE Trans. Circuits Syst. I Regul. Pap. 52(12), 2771–2779 (2005) 11. Y. Yao, A fully integrated 900-MHz passive RFID transponder front end with novel zero-threshold RF–DC rectifier. IEEE Trans. Industr. Electron. 56(7), 2317–2325 (2009) 12. M.-L. Sheu, Implementation of a 2.45 GHz passive RFID transponder chip in 0.18 lm CMOS. J. Inf. Sci. Eng. 26(2), 597–610 (2010) 13. J.F. Dickson, On-chip high-voltage generation in MNOS integrated circuits using an improved multiplier technique. IEEE J. Solid-State Circ. SC-11(3), 374–378 (1976) 14. M.R. Shokrani, An RF energy harvester system using UHF micropower CMOS rectifier based on a diode connected CMOS transistor. Sci. World J. 963709, 1–11 (2014) 15. C.C. Enz, E.A. Vittoz, Charge-Based MOS Transistor Modeling (Wiley, Chichester, 2006) 16. Tanner AMS and MEMS Design Flows, https://www.mentor.com/tannereda/. Accessed 01 May 2020 17. C. Hu, A.M. Niknejad, N. Paydavosi, BSIM4v4.8.0 MOSFET Model – User’s Manual (UC Berkeley, Berkeley, 2013) 18. E. Sicard, Microwind & DSCH Version 3.5 - User’s Manual Lite Version (INSA Toulouse, Toulouse, 2010) 19. M. Stoopman, An RF-powered DLL-based 2.4-GHz transmitter for autonomous wireless sensor nodes. IEEE Trans. Microw. Theory Tech. 65(7), 2399–2408 (2017)

Chapter 2

Multistage Depressed Collector with Azimuthal Magnetic Field for the DEMO Prototype Gyrotron Oleg I. Louksha , Pavel A. Trofimov , Vladimir N. Manuilov, and Mikhail Yu. Glyavin Abstract In this paper, the numerical simulation of a multistage depressed collector system based on separation of electrons in the crossed axial electric and azimuthal magnetic fields for the DEMO prototype gyrotron is described. In the simulation, the idealized model with the azimuthal magnetic field created by an on-axis conductor was studied. The trajectory analysis was performed for the spent helical electron beam with electron energy distribution close to the distributions obtained experimentally in high-power gyrotrons. As a result of geometry and potential optimization of the collector sections, the total efficiency of more than 80% was achieved, which is close to the maximum value of the collector efficiency with ideal separation. The obtained results will be used at the next stage of the study, in which the azimuthal magnetic field will be created by a toroidal solenoid. Keywords Gyrotron

2.1


Electron beam
Energy recovery

Introduction

Presently, gyrotrons occupy leading positions as the most effective high-power vacuum devices in the millimeter and submillimeter wavelength ranges. Among their applications, the use of gyrotrons for electron-cyclotron resonance heating and current drive in fusion reactors like the international thermonuclear experimental reactor (ITER) [1] and the demonstration power station (DEMO) [2] should be highlighted. Next generation of fusion reactors requires gyrotrons with increased power, frequency and efficiency compared with the ones available at the present O. I. Louksha (&)
P. A. Trofimov Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia e-mail: [email protected] V. N. Manuilov Lobachevsky State University, Nizhny Novgorod 603950, Russia V. N. Manuilov
M. Yu.Glyavin IAP RAS, Nizhny Novgorod 603950, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_2

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time. For example, the DEMO will use gyrotrons with an output power of 1–2 MW at 240–260 GHz with a total efficiency exceeding 60% [3]. Such a high total efficiency can be achieved only with implementation of multistage depressed collectors [4]. In multistage depressed collectors, the helical electron beam is separated on fractions with different energy, which are deposited afterwards on collector electrodes under different potentials. New possibilities for spatial separation of electrons are opened by using an approach based on their drift in the crossed electric and magnetic fields [5, 6]. A four-stage depressed collector based on using the azimuthal magnetic and the axial electric fields for electron radial drift was developed for the 74.2 GHz, 100 kW gyrotron at SPbPU [7, 8]. This report presents the results of trajectory analysis of the collector system of prototype gyrotron for the DEMO project. This system is based on the method of electron separation developed for the SPbPU gyrotron. The simulations were made for the idealized azimuthal magnetic field created by a conductor located on the axis of the device. The 3D modeling software package CST Studio Suite [9] was used for the calculations.

2.2

Parameters of Electron Beam and Model of Multistage Depressed Collector

The DEMO prototype gyrotron is under development at the Institute of Applied Physics in Nizhny Novgorod. This gyrotron is designed for an operating frequency f of 250 GHz and an accelerating voltage U0 of 55 kV [3]. In the central plane of the resonator, where the magnetic field induction B0 is 9.57 T, the main electron beam parameters have the following calculated values: the average radius of the leading centers of electron trajectories R0 is 3.85 mm, the spread of the radii of the leading centers DR is 0.2 mm. It is important to know the energy spectrum of electrons in the spent beam to design a system of electron energy recovery. In our calculations, we used a typical energy spectrum determined on the basis of experimental data obtained in high-power gyrotrons [10]. This spectrum corresponds to the operation regime of the gyrotron with the electronic efficiency ηel = 36%. The start plane of particles in the trajectory analysis was the central plane of the resonator (z = 0). In this plane, 936 emission centers were located at radii of 3.75, 3.85, 3.95 mm and were uniformly distributed along azimuthal coordinate. In turn, each emission center was a source of 19  4 beamlets with different energy and current (19 values), corresponding to electron energy spectrum in the spent HEB, and different cyclotron rotation phase (4 values). Therefore, approximately * 71∙103 electron trajectories were calculated in total. Each of the energy fractions had an initial value of the pitch factor (i.e., the ratio of transverse to longitudinal velocity) in the range from 0.1 to 1.2. For realistic representation of the pitch factor distribution, the simulation data on interaction of electrons with high-frequency field in the SPbPU gyrotron were used [8].

2 Multistage Depressed Collector with Azimuthal Magnetic Field …

13

Preliminary trajectory analysis was carried out for the single-stage recuperation scheme. The deceleration voltage between the gyrotron body and the collector was chosen to be 23.6 kV taking into account a minimum electron energy of 24.1 keV in the spent beam. In this regime, the power Pdiss dissipated in the collector was 116 kW at the maximum power density * 250 W/cm2. The total efficiency of the gyrotron was * 63%. The key feature of the multistage depressed collector is the use of the azimuthal magnetic field [7]. In the framework of this study, we considered idealized distribution of the azimuthal magnetic field created by a conductor located on the axis of the model. The data obtained for this idealized system will be used as a starting point to perform the simulations for the real collector in which the azimuthal magnetic field is generated by a toroidal solenoid. The model of the collector region is shown in Fig. 2.1. The inner radius of the cylindrical part of the collector body is 160 mm. The correcting coils 2 in combination with the main gyrotron solenoid create a quasi-homogeneous distribution of the axial magnetic field Bz (z) with a value of 0.016 T in the region of electron deceleration (1100 < z < 1500 mm). The azimuthal magnetic field is created using a conductor defined as a Coil Segment Source in the CST Magnetostatic Solver. The chosen conductor geometry ensures that the distribution of the azimuthal magnetic field is adiabatic in the transition region between the resonator and the collector. The electric field is created using four conically-shaped electrode sections (Fig. 2.1). The pipe connected to the collector body is located on the axis of the device. This pipe will be used for placing the internal winding of the toroidal solenoid similar to that of the SPbPU gyrotron [8, 11]. The values of the section potentials were following: UI = −23.6 kV, UII = −29.1 kV, UIII = −32.4 kV,

Fig. 2.1 Schematic view of the depressed collector model with the distribution of potential u in the y–z plane. 1—collector body, 2—correcting coils, I–IV—collector sections

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UIV = −37.8 kV at grounded gyrotron and collector bodies. Geometry and potentials of the collector sections were chosen as a result of complex parametric optimization aimed to achieve the minimum value of the dissipated power Pdiss. The value of Pdiss was determined as a result of trajectory analysis described in the next section. The potential distribution in the electron deceleration region obtained after optimization is shown in Fig. 2.1. The process of geometry and potential optimization is also needed to minimize the number of electrons reflected from the deceleration region towards the resonator. In gyrotrons, reflected particles move along magnetic field lines and can reach the resonator and decrease the electronic efficiency, which limits the possibilities of total efficiency enhancement [12, 13]. The threshold level of electron reflections, which does not affect gyrotron operation, varies in different gyrotrons and can reach 5–10% [14]. It also should be noted that one of advantages of the designed depressed collector system is the possibility of intercepting reflected as well as secondary electrons by the collector sections and its body during their movement in the radial direction under the action of the crossed electric and magnetic fields [7].

2.3

Results of Trajectory Analysis of the Four-Stage Depressed Collector

Electron trajectories in the depressed collector were calculated using CST Tracking Solver that includes finite integration method to solve a Newton’s equation of particle motion with periodic update of particle momentum and position in correspondence with formulas ! d ! ! ðm v Þ ¼ e E þ ! v  B dt

ð1Þ

d! r ¼! v: dt

ð2Þ

Electron trajectories were calculated for two different values of the azimuthal magnetic field Bh in the area of HEB deceleration (z > 1100 mm) equal to approximately 0.08 T and 0.13 T. Moving in this region, electrons make several full-turns around the axis. The number of this turns obviously depends on the ratio Bh/Bz and on the time before deposition of electrons on an electrode surface. The positions of particles fixed at the moment of their intersection of the plane x = 0 at Bh = 0.08 T are shown in Fig. 2.2. As shown in this figure, when electrons move in the deceleration region, their energy decreases while the radial position increases under the action of crossed Ez  Bh fields. Shape of the trajectory changes depending on the initial energy of electrons—electrons with higher energy pass longer distance along the axis and are deposited on sections with a more negative potential.

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15

Fig. 2.2 Positions of electrons with different energy W in the y–z plane

It should be noted that the main part of electrons is deposited on the back surface of the sections after changing direction of their axial velocity. This results in a longer time of electron radial drift and leads to an increase of the area of electron deposition that diminishes the heat loading on the collector surface. The main data characterizing the operation of the depressed collector at Bh = 0.08 T and 0.13 T are summarized in Table 2.1. Here are the values of the power PI–PIV dissipated on each of the sections, the total power Pdiss dissipated on the collector, the current Iref of electrons reflected from the collector (Ib = 10 A is the current of the spent beam), and the total efficiency of the gyrotron ηt calculated using the formula: gt ¼

PRF ; PRF þ Pdiss

ð3Þ

where PRF = ηel∙Ib∙U0 = 198 kW is the microwave output power. The achieved total efficiency is high enough and close to the limit for the recuperation system with four stages. A certain decrease of the efficiency should be expected if the real beam’s distributions of energies and coordinates will be considered for the calculations [7]. In the calculated regimes, the electron beam fraction reflected from the collector is minor and do not significantly affect the electronic efficiency of the gyrotron. Small reflections are primarily associated with a reduced voltage of the first collector section. The increase of deceleration voltage of the first section can possibly provide an additional increase in the total efficiency even in the presence of a noticeable reflection of electrons from the collector, when the output microwave power and electronic efficiency are reduced [15].

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Table 2.1 Results of trajectory analysis of the four-stage depressed collector

Parameter

Bh = 0.08 T

Bh = 0.13 T

PI, kW PII, kW PIII, kW PIV, kW Pdiss, kW Iref ηt, %

6.87 5.52 11.02 13.58 36.99 1.2
10−4∙Ib 84.3

6.78 7.84 11.38 13.51 39.51 1.1
10−3∙Ib 83.3

In the framework of this study, the heat loading on the collector sections was also calculated. For this purpose, we implemented a method of smoothing in which the deposition area of each of the electron trajectories (current tubes) was considered to be approximately equal to the square of the distance between adjacent trajectories. It is obvious that the heat loading on each section depends on amount of dissipated power PI–PIV. The maximum power density was equal to * 200 W/cm2. An additional decrease in the power density for considered four-stage depressed collector design can be achieved by reducing the magnetic field inductions Bz and Bh, which will lead to an increase in the radii of the leading centers of electronic orbits in the recuperation area, as well as by changing the tilt angles of the conical sections.

2.4

Conclusion

The numerical simulations of the collector for the DEMO prototype gyrotron confirmed possibility of efficient separation of spent beam electrons based on their drift in the crossed azimuthal magnetic and axial electric fields are described. The developed design of the four-stage depressed collector with an idealized azimuthal magnetic field created by an on-axis conductor ensures the achievement of a total gyrotron efficiency exceeding 80%. The data obtained will be used in the design of a toroidal solenoid which can be applied to create the azimuthal magnetic field in a real device. Acknowledgements The study was performed by a grant of the Russian Science Foundation (project No. 16-12-10010). Part of the results were obtained using the computing resources of the Supercomputer Center of the Peter the Great St. Petersburg Polytechnic University [16]. Development of the DEMO gyrotron prototype is carried out as a part of the Russian Science Foundation project No. 19-79-30071, and all requirements for the electron-optical system are formulated in this project.

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References 1. V. Rozhansky et al., Modeling of ITER edge plasma in the presence of resonant magnetic perturbations. Contrib. Plasma Phys. 56(6–8), 587–591 (2016) 2. A.S. Kukushkin, V.Y. Sergeev, B.V. Kuteev, Preliminary results of divertor modelling for DEMO-FNS reactor. J. Phys: Conf. Series 907, 012012-1–012012-5 (2017) 3. G.G. Denisov et al., First experimental tests of powerful 250 GHz gyrotron for future fusion research and collective Thomson scattering diagnostics. Rev. Sci. Instrum. 89(8), 084702-1– 084702-4 (2018) 4. M. Glyavin, V. Manuilov, M. Morozkin, Two-stage energy recovery system for DEMO gyrotron, in Proceedings of 43rd International Conference on Infrared Millimeter and Terahertz Waves IRRMW-THz 2018, Nagoya, Japan (IEEE, 2018), pp. 8510139-1–8510139-2 5. I.Gr. Pagonakis et al., A new concept for the collection of an electron beam configured by an externally applied axial magnetic field. IEEE Trans. Plasma Sci. 36(2), 469–480 (2008) 6. V.N. Manuilov et al., Gyrotron collector systems: types and capabilities. Infrared Phys. Tech. 91, 46–54 (2018) 7. O.I. Louksha, P.A. Trofimov, A method of electron separation for multistep recuperation systems in gyrotrons. Tech. Phys. Lett. 41(9), 884–886 (2015) 8. O.I. Louksha, P.A. Trofimov, Highly efficient gyrotron with multi-stage recuperation of residual electron energy. Tech. Phys. 64(12), 1889–1897 (2019) 9. CST Studio Suite page on Dassault Systems, https://www.3ds.com/products-services/simulia/ products/cst-studio-suite. Accessed 04 May 2020 10. N.P. Venediktov et al., Measurements of the spread in the initial electron energy in a gyrotron. Tech. Phys. 45(4), 1571–1574 (2000) 11. O.I. Louksha, P.A. Trofimov, A multistage depressed collector system for gyrotrons, in Proceedings of 18th International Vacuum Electronics Conference on IVEC 2017, London, United Kingdom (IEEE, 2017), pp. 8289518-1–8289518-2 12. K. Sakamoto et al., Major improvement of gyrotron efficiency with beam energy recovery. Phys. Rev. Lett. 73(26), 3532–3535 (1994) 13. M.V. Morozkin et al., A high-efficiency second-harmonic gyrotron with a depressed collector. Int. J. Infrared Millimeter Waves 29(11), 1004–1010 (2008) 14. B. Piosczyk et al., Single-stage depressed collectors for gyrotrons. IEEE Trans. Plasma Sci. 24(3), 579–585 (1996) 15. N.A. Zavolsky et al., Numerical modeling and experimental study of high-power gyrotrons with energy recovery, in Proceedings of 28th International Crimean Conference Microwave and Telecommunication Technology CriMiCo 2018, Sevastopol, Russia (Sevastopol State University Press, 2018), pp. 1131–1137 16. Supercomputer Center of Peter the Great St. Petersburg Polytechnic University Homepage, http://www.scc.spbstu.ru. Accessed 05 May 2020

Chapter 3

Improvement of the Microwave Strip Devices Prototyping Technology Alexey S. Podstrigaev , Andrey V. Smolyakov , and Igor V. Maslov

Abstract Requirements for the perfection of technology of prototyping microwave strip devices are substantiated. Construction of appliance for microwave strip devices adjustment and testing based on patented technical decisions is described. Methods of work with the appliance and the results of inaccuracy analysis carried out using the appliance are described. Methods of estimation of the described appliance economic effect are proposed. The effect is estimated on the example of the reduction of time costs at the creation of a radio-electronic complex. Keywords Microwave strip devices Microwave strip devices testing

3.1


Microwave strip devices adjustment


Introduction

Microwave devices include many different assemblies and each of them requires individual adjustment [1–3]. One part of the complex microwave device designing process is prototyping. At this stage, technical decisions, fabrication, and adjustment methods are worked out and characteristics are optimized. The designing process of sophisticated microwave devices mostly includes several iterations and each of them ends with prototyping [4–6]. Therefore, reducing the time and economic costs of prototyping is crucial. Due to its fabrication simplicity, technological effectiveness, and high repeatability of characteristics of microstrip devices are widely used in modern microwave electronics. But even at the modern level of technology considering the deviation of permittivity from the nominal value, the technological tolerances, inaccuracies in A. S. Podstrigaev
A. V. Smolyakov (&) Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg 197376, Russia e-mail: [email protected] I. V. Maslov EvoCo, Inc., Tokyo 151-0073, Japan © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_3

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Fig. 3.1 The appliance for measuring the characteristics of a dual-ported microwave device [11]

calculating and modeling, device readjusting is relevant. Readjusting ability becomes especially important within the R&D process of microwave electronics. At the same time, the immediacy and the simplicity of readjusting are also important. It is obvious, that depending on its function, circuit design, and constructional building, microwave devices have the different overall and conjunctive dimensions, the different number of microwave energy inputs and outputs, feeding inputs, kind of material, and substrate thickness. Now the individual technological appliance is produced for every device or group of devices with similar design when prototyping or adjustment is needed. At this stage, the device is a board with elements placed on it. That is why the appliance for adjustment is a conductive base with deepenings for microwave device placing. Coaxial-to-microstrip transitions (CMT), which provides the possibility to measure characteristics of the installed microwave device [7–10], are fixed on the appliance. Often the transitions can move within the small ranges. The disadvantage of the described approach is the necessity to make different versions of the appliance, depending on the dimensions and thickness of the microwave device board, the number, and location of its outputs. To provide the possibility of testing of the dual-ported microwave devices having different linear dimensions, French specialists developed the appliance with the alterable central element (Fig. 3.1) [11]. Its major disadvantage is the impossibility to adapt to the device under test (DUT) thickness and to attach the connectors from all device’s sides. An alternative to using the appliance is the rigid mounting of the CMTs on the DUT’s board [12]. Furthermore, to provide the construction rigidity large surface must be sealed. At the same time, board overheating, track’s delamination, and, consequently, the board destruction may occur. Therefore, it is necessary to improve the microwave devices adjustment and testing technology, considering the following abilities: – to adjust microwave microstrip device, regardless of its dimensions, connectors location and its board thickness; – to provide the possibility of the quick and multiple CMT connectiondisconnection (for characteristics measuring), which eliminates the risk of the microwave device damage in adjusting (testing) process;

3 Improvement of the Microwave Strip Devices Prototyping Technology

21

– shielding of the adjusted device; – providing accurate and reliable measurements.

3.2

Description of Technology for Adjustment and Testing of Microwave Microstrip Devices

The technology is based on the use of the appliance, which is universal for connected devices, is proposed for the microwave strip devices adjustment and testing. The appliance provides an ability to install and fix microwave devices with microstrip outputs in a wide range of linear dimensions. Such ability is provided by elements, movable along three axes. The general idea of the appliance design is shown in Fig. 3.2. Previously the main technical decisions were published in paper [13] and protected by Russian Federation patent [14]. In this paper, the appliance appearance, technology practicalities, and technique of estimating the economic effect for new technology are given. CMTs, included in the appliance, can easily move in a horizontal plane, can be attached to the outputs on the microwave device board and fixed. The number of CMTs can vary greatly. This number is mainly limited by dimensions of the CMTs themselves. Adjusted device shielding can be performed by producing the foil sidewalls if necessary. The foil is attached to the special miniature columns that are equipped with fasteners, installed on the device board, or it is fixed by the appliance’s elements. Accuracy and reliability of measurements are provided by using calibrating boards and reliable fixation of the adjustable device on the appliance. Fig. 3.2 Appliance elements with the installed microwave device (1—CMT, 2—bracket, 3—directional axis, 4—base, 5—device under test, 6— push-type actuator, 7— CMTs’ central output, 8— spring-loaded abut)

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Fig. 3.3 Embodiments of the brackets with CMT fixed on them

Fig. 3.4 Embodiments of the fixation by loop (1— directional axis, 2—base, 3— loop, 4—screw nut)

As stated above, proposed technology for adjustment and testing of microwave devices is based on the appliance for measuring of characteristics of microwave devices with microstrip outputs. Therefore, it is appropriate to describe the appliance construction in detail. For the installation of the uncased devices with different dimensions the possibility of moving of the CMTs in the horizontal plane and along the metal base is supported. To perform this, every CMT 1 is fixed on the bracket 2, which moves along axis 3, which in turn is moved in the plane parallel to the base 4. The thickness of the microwave device 5 board is considered by the spring-loaded abut 8, which presses the microwave device 5 to the central outputs 7 of the CMTs 1 protruding from the bracket 2. As a result, the appliance does not depend on the microwave device 5 board thickness. Microwave device 5 fixation is performed by its pressuring to the central outputs 7 of the CMTs 1 by push-type actuators 6 and abut 8. The number of the push-type actuators 6 depends on the microwave device 5 dimensions. Push-type actuators 6 improve the grounding and reduce the load on the abuts 8. It is useful when microwave device 5 has large mass-dimensional characteristics. The electrical contact of the microwave device 5 with the appliance’s elements is provided by using the metallic materials or conductive coatings in these elements. Directions of the fixing force application are conventionally shown by the big arrows in Fig. 3.2. Directions of abut 8 moving are shown by small arrows.

3 Improvement of the Microwave Strip Devices Prototyping Technology

23

Since the adjusted device outputs can be located on four sides, two versions of brackets for CMTs (Fig. 3.3) are developed. Grounding terminals are located on the base and on the movable slabs. Electric contact is additionally improved by using the loops, options of which are shown in Fig. 3.4. Axes 1 with brackets, placed on them (conventionally not shown in Fig. 3.4), are attracted to the base 2 using the loops 3 and screw nuts 4. The appliance, shown in Fig. 3.5, was developed based on the described constructive solutions. Its workspace is divided into four identical sections. Each section is assembled individually for each specific device. In Fig. 3.5 two sections are disassembled for the easiness of presentation. The board with the CMTs drawn to it is installed on one of the sections. For power and control signal supply each section has a terminal plate installed on the bases butt end. The grounding terminals are mounted near the terminal plates. Several sections can be used at the same time when adjusting a large size device or several devices simultaneously. Generally, the number of devices is limited by their dimensions and the workspace area. The number of CMTs, connected to the device, may vary greatly, because axes with brackets, fixed on them, may be easily disconnected and moved to the proper section.

Fig. 3.5 The appliance general view

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A minimal interval between outputs on the one side of the presented appliance is determined by the brackets’ width and amounts to 8 mm. The adjusted device board thickness is determined by the interval between axes and central CMT conductors and amounts to 18 mm or less. Minimal microwave device dimensions are 3  5 mm, which is determined by brackets and CMT dimensions. Maximal dimensions are 200  280 mm.

3.3

Measuring Method and Microwave Assemblies Shielding

The measurements inaccuracies are inserted by nonuniformities occurring in places, where the microwave device outputs are connected to the appliance’s CMTs. For example, when connecting CMT to the strip using the bridge, spurious inductance is inserted. And when connecting by soldering, spurious capacitance is occurred, because of solder spreading [15]. The problem of high-quality, reliable, and stable connection of measuring devices to the testing device and calibration elements is typical not only for the measurements but for all microwave techniques [16, 17]. The measurements of the boards, which were the microstrip line segments (Fig. 3.6), were taken for the estimation of nonuniformities effect on the transmission coefficient in a wide frequency range. The board topology is equivalent to the strip with the 50 ohms characteristic impedance. The board characteristics are listed in Table 3.1 [18]. The boards are made of RO4003C and the lucalox VK 100-1 brand. To reduce the attenuation, the board dimensions were chosen minimal.

Fig. 3.6 Calibration board

3 Improvement of the Microwave Strip Devices Prototyping Technology Table 3.1 Calibration boards characteristics

25

Characteristic

Board 1

Board 2

Substrate material e0 (F/m) L (mm) H (mm) W (mm) h (mm)

RO4003C 3.38 ± 0.05 7.5 20 1.1 0.508

VK 100-1 9.6 ± 0.2 6 7.5 0.48 0.5

For each board, the measurements were made for three connection methods of the CMT central output with the board output: by pressuring (1), using the bridge (2), and by soldering right to the strip (3). The meter of transmission and reflection coefficient modulus, which is connected to the appliance CMT by coaxial transitions, was used during the experimental analysis of the measurement inaccuracy of the appliance. The experiment results are shown in Fig. 3.7. According to it, mainly in the up to 4 and more than 13 GHz bands there are local VSWR maximums and their corresponding transmission coefficient minimums, caused by the gap between the board and the bracket and the insufficient pressure of boards to the grounding push-type actuators. The similar results were received for all three methods of the lucalox board fixation (Fig. 3.7b). The maximum VSWR value for this board is approximately 7.8, and the minimum gain value is approximately minus 15 dB. But on the most frequencies of the considered band VSWR value does not exceed 2.5, and transmission loss value is less than 5 dB. Flatter characteristics were received for the board of RO4003C material (Fig. 3.7a) with the connection by pressure and soldering. In these cases, VSWR value rarely exceeds 2.0 and has no noticeable maximums. At the same time, the transmission loss does not exceed the value of minus 5.1 dB. That is because the board of this material was best pressured. Consequently, the gaps between the board and brackets are reduced. A mounting using the bridge did not permit to make the pressure of the same quality. And for the lucalox board, hard pressure causes the cracking. It is necessary to say, that the method of fixation by pressure is the easiest in practice and gives the ability of fast mounting of the microwave devices on the appliance. Because the frequency characteristics, received for this method, almost do not differ from the characteristics, received for other methods, fixation by pressure can be recommended as preferable. The disadvantage of this method is the inaccuracy in multiple measurements which is caused by weak fixation of central CMT output on the microwave device board. Besides, applying the excessive mechanical load to the CMT output causes its bending, which influences on the wave resistance of transition and, consequently, on the measuring accuracy, and the CMT itself abrades.

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10

3

2

1

1

0

VSWR

K (dB)

0 3

2

-10 -20 0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

f (MHz)

a 1

10

2

3

VSWR

K (dB)

0 1

0

2

-10 3 -20 0

1

2

3

4

5

6

7

8

9

10 11 f (MHz)

12

13

14

15

16

17

18

19

b

Fig. 3.7 Frequency dependences of transmission coefficient (top) and VSWR (bottom) for three methods of connection: by pressure (1), by using the bridge (2) and by soldering (3); a—for board 1; b—for board 2

To take the loss between CMT and microstrip outputs into account described boards are proposed to use as calibration ones. According to the experiment results the value of VSWR does not exceed the value of maximum equipment measurement and stays stable with repeated calibration boards connecting and disconnecting. It confirms the possibility to use these boards for calibration.

3.4

Economic Effect

It is appropriate to evaluate the economic effect of the described appliance on the example of sophisticated electronic warfare (EW) system creation. And the largest effect is achieved on the research and prototyping stages. This effect consists if a significant reduction of time expenses and is achieved through three components: – time-savings TE1 in individual technological tools producing; – time-savings TE2 in the stage of microwave devices adjusting (because of exclusion of repeated preparing of the individual workplace before each assembly adjustment);

3 Improvement of the Microwave Strip Devices Prototyping Technology

27

– incoming supervision of bought microwave modules (time expenses TE3 in assembly units troubleshooting are excluded). To provide a quantitative assessment of the effect, a calculation was done for the EW jamming system, which is built based on principles described in [19–21]. The system contains one channel jamming transmitter and seven identical receiving channels, each of which contains twenty frequency channels. According to the system structure, it contains approximately 420 microwave assemblies. The results of calculations are TE1 ¼ 87 days, TE2 ¼ 3:1 days, TE3 ¼ 0:7 days. Thus, the total economy is 90.8 days, which is 19.3%, considering the average time of system prototype production is approximately 471 days. The greatest contribution is also made by the economy of the individual technological tools on the production stage.

3.5

Conclusion

Described technology gives the ability to adjust microwave microstrip devices and test microwave devices with the microstrip outputs (the devices themselves may not be necessarily designed with the microstrip line). Simultaneous testing of several devices and the joint adjustment of them are also possible. In company-consumers of microwave electronics the appliance, which is the basis of the technology, can be used for receiving inspection of microwave devices. The described appliance is universal. It minimizes the need for the production of individual tools for adjusting of microstrip devices. It makes it possible to avoid the soldering of the device’s board surface, which is necessary when using the CMTs described in [12] for microstrip device testing. At the same time, unlike the tool described in [11], the presented technology allows to adjust and test devices with the boards of various thickness, as well as devices with a large number of ports. The use of the universal tool systems in the fabrication process and in R&D simplifies adjustment, reduces time expenses for workplaces re-equipment, and allows to avoid the installation of broken assemblies on the receiving inspection stage.

References 1. V. Kazmirenko, Y. Prokopenko, Y. Poplavko, Tuning range of microwave devices with micromechanical control, in 2017 XI International Conference on Antenna Theory and Techniques (ICATT), Kiev (IEEE, 2017) 2. M.A. Abdul Latip, M.K. Mohd Salleh, N. Ab Wahab, Tuning circuit based on varactor for tunable filter, in 2011 IEEE International RF & Microwave Conference, Seremban (IEEE, 2011)

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3. G. Simpson, Hybrid active tuning load pull, in 77th ARFTG Microwave Measurement Conference, Baltimore (IEEE, 2011) 4. S. Li, P.D. Laforge, A post-fabrication tuning method for a varactor-tuned microstrip filter using the implicit space mapping technique, in 2015 IEEE MTT-S International Microwave Symposium, Phoenix (IEEE, 2015) 5. A. Boldaji, M.A. Antoniades, Method of decoupling and independently tuning the second mode of a microstrip-fed slot antenna using series inductive loading. IEEE Antennas Wirel. Propag. Lett. 12, 1017–1020 (2013) 6. C. Cai, J. Wang, G. Zhang, Tunable microstrip lowpass filter with compact size and ultra-wide stopband. Electron. Lett. 51(19), 1514–1516 (2015) 7. V. Teppati, A. Ferrero, M. Sayed, Modern RF and Microwave Measurement Techniques (Cambridge University Press, New York, 2013) 8. Agilent Technologies, De-embedding and Embedding S-Parameter Networks Using a Vector Network Analyzer, Appl. Note 1364-1 (2004) 9. O. Moravek, K. Hoffmann, M. Polivka, L. Jelinek, Precise measurement using coaxial-to-microstrip transition through radiation suppression. IEEE Trans. Microw. Theory Tech. 61(8), 2956–2965 (2013) 10. J. Cheng, E.S. Li, W. Chou, K. Huang, Improving the high-frequency performance of coaxial-to-microstrip transitions. IEEE Trans. Microw. Theory Tech. 59(6), 1468–1477 (2011) 11. B. Sylviane, F. Grossier, M. Lecreff, G. Ralala, D. Geffroy, Adjustable device for measuring the characteristics of a microwave component, United States Patent US 4808919 A, 28 February 1989 12. G. Askari, H. Fadakar, H. Mirmohammad-Sadeghi, Analysis, design and implementation of a useful broadband coaxial-to-microstrip transition, in PIERS Proceedings, Guangzhou (The Electromagnetics Academy, 2014) 13. A.S. Podstrigaev, V.P. Likhachev, L.B. Ryazantsev, Technique for tuning microwave strip devices. Meas. Tech. 59(5), 547–550 (2016) 14. A.S. Podstrigaev, N.I. Fomchenkova, Device for characterization of microwave devices, Russian Federation Patent RU 2577805 C1, 20 March 2016 15. B. Oldfield, Backside connections. Microw. J. 3, 106–114 (1997) 16. B. Oldfield, Connector and termination construction above 50 GHz. Appl. Microw. Wirel. 13, 56–66 (2001) 17. J. Browne, Coax test fixture checks microstrip circuits to 60 GHz. Microw. RF 28, 136–137 (1989) 18. Rogers Corporation, High Frequency Circuit Materials: RO4000 Series datasheet (2017) 19. V.N. Vernigora, A.V. Volodin, A.P. Dyatlov, V.P. Polyanichenko, Device for generating radar station response noise, Russian Federation Patent RU 2237372 C2, 27 September 2004 20. A.V. Volodin, V.A. Tokarev, Spot jamming unit for radio detection and ranging stations, Russian Federation Patent RU 2329603 C2, 20 July 2008 21. R.A. Poisel, Modern Communications Jamming Principles and Techniques, 2nd edn. (Artech House, Norwood, 2011)

Chapter 4

Peculiar Properties of Nuclear Magnetic Resonance in Dispersed Magnetically Ordered Nanostructures and Requirements for Radiospectroscopic Equipment for Its Observation Artem Khudyakov, Ivan Pleshakov, Yurii Kuzmin, Anton Mazur, Efim Bibik, and Mikhail Shlyagin

Abstract The paper discusses problems of a special variant of nuclear magnetic resonance, namely, the resonance observed in compounds with a magnetic order. It is shown that this effect acquires additional differences from the conventional case if it is registered in nanostructured magnetic materials, for which a number of estimates are obtained. The analysis of changes in the enhancement coefficient of the exciting radio frequency field for objects with uniaxial magnetic anisotropy, to which particles of dispersed substances tend to pass with their sizes decrease, is carried out. Relaxation processes and possible mechanisms for changing of the relaxation times of nuclear spin system are discussed. The specificity of this type of A. Khudyakov (&)
I. Pleshakov
Y. Kuzmin Ioffe Institute, St. Petersburg 194021, Russia e-mail: [email protected] I. Pleshakov e-mail: [email protected] Y. Kuzmin e-mail: [email protected] A. Mazur Saint Petersburg State University, St. Petersburg 198504, Russia e-mail: [email protected] E. Bibik Saint Petersburg State Institute of Technology (Technical University), St. Petersburg 190013, Russia e-mail: eefi[email protected] M. Shlyagin Centro de Investigación Científica y Educación Superior de Ensenada, Ensenada, CP 22860, Mexico e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_4

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resonance is demonstrated by the example of a nanomaterial based on magnetite, used as a precursor for the preparing of ferrofluids. The difference in the parameters of nuclear magnetic resonance signals in bulk and nanostructured samples is valuated and the resulting special requirements for experimental equipment are determined. Keywords Magnetic nanostructures Radiospectrometer

4.1


Nuclear magnetic resonance


Introduction

Among nanosystems, a special place is occupied by dispersed magnetic nanostructures—ensembles of particles with sizes of hundreds of nanometers or less, having various types of magnetic order (most commonly ferro- and ferrimagnets are found, while others are also possible). Interest in them is dictated by the fact that with a decrease in the size of the magneto-ordered object, its properties change, i.e., there is an opportunity to obtain materials with new magnetic characteristics [1]. To date, quite a lot of such substances have been discovered and investigated. Present work will focus mainly on iron-containing oxides, which are the initial substances for producing ferrofluids—very important representatives of this class of materials. They are used in optoelectronics [2, 3], in many physical experiments [4–9], and also have the prospect of application in biomedicine [10]. The properties of these materials are studied using many methods of condensed matter physics, but nuclear magnetic resonance (NMR) is used relatively rarely, despite the fact that in principle it can provide significant information about magnetically ordered materials (exact values of hyperfine fields, data on magnetic anisotropy, etc.) [11]. This is due to the difficulties of registering of the response of nuclear spin system in samples with a small amount of working substance and an increased anisotropy field (the latter is very common in nanostructured materials). Additional difficulties arise in the case of the above-mentioned solid phases of ferrofluids, since they are usually iron-containing substances, and 57Fe nuclei have an extremely small magnetic moment and, consequently, give a weak signal. Therefore, despite the fact that there are quite a large number of works on, for instance, NMR in cobalt nanostructures, there are very few such studies for iron-containing systems. In this paper, a general consideration of the features of NMR observation in magnetically ordered nanomaterials will be performed, with an example of dispersed magnetite, which is a precursor for the production of ferrofluids. Based on this, the requirements for radio-spectroscopic equipment intended for such experiments are formulated.

4 Peculiar Properties of Nuclear Magnetic Resonance …

4.2

31

Methods and Samples

The analysis of parameters describing NMR in a dispersed magnetic medium was performed in present work using a model that assumes that particles of this substance tend to acquire uniaxial anisotropy when their sizes decreases to the order of several tens of nanometers, and also that the anisotropy field in them increases significantly compared to bulk material. This behavior is not universal, but it is still typical for magnetic nanostructures (see [12] and references therein). For this case, an expression for the enhancement coefficient of the radio frequency (RF) magnetic field corresponding to arbitrary spatial orientations of the particles is obtained and estimation of signal intensity was made. The results of the analysis were compared with the experimentally observed response of 57Fe nuclear spin system of nanostructured magnetite. Measurements were carried out on the Tecmag Redstone radiospectrometer by two-pulse technique of spin echo registration at frequency of about 70 MHz and durations of the first and second RF pulses of 2 and 4 ls correspondingly. In these measurements the NMR spectrum and relaxation time T2 were determined at temperature T = 77K. The sample was paste-like substance using as a standard precursor for fabrication of ferrofluids (the same that was studied in [13], where also described the procedure for its preparation). This substance is a system whose magnetic phase is composed of Fe3O4 (magnetite) nanoparticles with the average diameter of about 10 nm, separated by thin layers of oleic acid, usually used as a surfactant in the manufacturing of such compounds. For comparison, the signal from powdered magnetite crystals was also investigated.

4.3 4.3.1

Theoretical Analysis, Experiment and Discussion Model of Magnetic Nanoparticles with Uniaxial Anisotropy

It is known that when NMR is observed in magnetic materials, the excitation of the nuclear spin system and its response are registered by a two-stage mechanism: the RF magnetic field h creates forced oscillations of electron magnetization, and then they are transmitted to the nuclei by the hyperfine interaction [11]. The main parameter describing NMR in this case is the enhancement coefficient η, which shows how many times the internal RF field hhf is greater than that applied to the sample. As a rule, η is of 104–105, and in conventional materials it can be additionally increased due to domain boundaries. The first obvious feature of nanostructures is that they do not have the latter effect, since the characteristic sizes of their particles are significantly lower than the monodomain threshold. Taking into account the above reasoning, it is further assumed that the nanoparticle is spherical, and has a single axis of easy magnetization with a large

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(i.e., significantly higher than h) magnetic anisotropy field Ha. In fact, the anisotropy of nanoparticles is quite complex, and it includes both magnetocrystalline and surface anisotropy, as well as shape anisotropy [14]. We assume that these contributions are accounted for by a single effective constant K1. According to [15], for this system, from the condition of minimum of Zeeman and anisotropy energies, the expression can be obtained: sin 2h0 ¼ ðh=HA1 Þ sinðhh  h0 Þ:

ð4:1Þ

The symbols included in it are illustrated by Fig. 4.1, which shows a coordinate system associated with a nanoparticle in such a way that its z axis is aligned with the anisotropy axis. Here h0 is the angle of deviation of the magnetization of the particle Ms from its equilibrium direction (i.e. from z) under the action of the RF magnetic field h, hh is the angle between h and z, HA1 = K1/Ms. Note that h is an alternating value (h = h0sinxt), and the figure shows its momentary position. Taking into account that, because of Ha >> h0, the angle h0 is small, and it is easy to determine its maximum values from (4.1) homax ¼ 

a sin hh ; 1 þ a cos hh

ð4:2Þ

where the notation a = h0/Ha is entered (and also used the well-known relation Ha = 2HA1). Figure 4.2 demonstrates how the field hhf appears on the nuclei. Since the hyperfine field Hn is related to the magnetization by the hyperfine interaction constant A by the expression Hn = − AMs [11], the amplitude of its alternating component can be represented as hhf 0 ¼ AMs jsin h0max j ffi AMs h0max :

Fig. 4.1 The coordinate system associated with the individual particle p (a—the axis of anisotropy)

ð4:3Þ

4 Peculiar Properties of Nuclear Magnetic Resonance …

33

Fig. 4.2 The diagram explaining the mechanism of appearance of RF hyperfine field on nuclei

Using the definition of the enhancement coefficient, η = hhf0/h0, from (4.2) and (4.3) we obtain g¼

Hn sin hh : Ha 1 þ a cos hh

ð4:4Þ

Formula (4.4) transforms to a well-known simplified expression for η (η = η0 = Hn/Ha) in the absence of an external constant magnetic field [11] at hh = 90°. In general case, it describes a non-homogeneously distributed enhancement coefficient, since the sample consists of a set of randomly oriented nanoparticles. If we assume that the excitation of a nuclear spin system is determined by the averaged η (which is not quite true, but can be taken as an approximation), using (4.4) it is easy to estimate the ratio of η in nanostructure to η0 in a uniaxial monodomain bulk material (η0 = Hn/Ha0, where Ha0 is the anisotropy field in it): Z 0

p

Z g0 dhh = 0

p

  p Ha 1 : gðhh Þdhh ffi 2 Ha0 1  a

ð4:5Þ

Expression (4.5) shows that the difference between the responses of nanostructure and bulk sample is mainly determined by the ratio of anisotropy fields. In the absence of domain boundaries in bulk material and at approximately equal anisotropy fields, the ratio (4.5) will be about 1.5 (note that the typical value of h0 for NMR in a magnetic material is of the order of 10 Oe, and Ha can be estimated as 500 Oe [12]). In reality, the change in anisotropy can be quite significant, i.e., the RF field amplitude necessary for NMR in nanostructure will be at least by the order of value higher, than for bulk material. On the other hand, the registration of the response is also proportional to η, and will require at best an order of magnitude greater sensitivity of the receiving equipment, or a substantially larger number of signal accumulations. For a specific variant of NMR on 57Fe nuclei, these requirements are further increased.

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NMR Measurements and Requirements for Radiospectroscopic Equipment

An experiment performed according to the method described above confirmed the correctness of the estimates made. For optimal excitation of the echo signal in the magnetite paste, pulses of several times greater amplitude were used than for the powdered single crystal. The registration of the response, which in this nanomaterial turned out to be extremely weak, was performed by accumulation of signal with number of 40960. Such measurements take considerable time and, therefore, in addition to the high sensitivity of the receiver, they require high stability of the equipment. The study of the spin-spin relaxation time T2 was complicated by the fact that it was significantly less in paste than in bulk material. In the latter case, it was tens of microseconds, while the estimate for the nanomaterial gave T2  2–4 ls, i.e. the change was more than order of value. Undoubtedly, such an effect is based on new mechanisms that appear with a restriction of the characteristic size of system elements, the analysis of which is a separate problem. As an assumption, one can point to a change in the microstructure of nanoparticles and, possibly, to a transformation of the spectrum of spin waves involved in the scattering of nuclear magnetic moments. From the point of view of requirements for radiospectroscopic equipment, it means the necessity to provide rapid recovery of the receiver after the action of exciting pulses on the sample and, inevitably, on the input of the receiver. This can be achieved by additional circuits for blanking of this part of the device.

4.4

Conclusion

To summarize, we argue that the observation of NMR in magnetic nanomaterials in comparison with the conventional case is associated with significant difficulties: a very small signal and very short time during which an unsteady response of the nuclear spin system can be observed after its excitation. Their physical causes are the increase in anisotropy inherent in magnetic nanoparticles and the reduction of the spin-spin relaxation time. Therefore, the higher standards should be imposed on radiospectroscopic equipment: the need of high sensitivity of the receiver, the ability to perform a large number of signal accumulations, and also the ability to register the NMR signal after a short (about microseconds) time after exciting pulses.

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References 1. N. Domracheva, M. Caporali, E. Rentschler (eds.), Novel Magnetic Nanostructures (Elsevier, Amsterdam, 2018) 2. J. Philip, J.M. Laskar, Optical properties and applications of ferrofluids. J. Nanofluids 1(1), 3– 20 (2012) 3. E.N. Velichko, G.L. Klimchitskaya, E.N. Nepomnyashchaya, Casimir effect in optoelectronic devices using ferrofluids. J. Electron. Sci. Technol. 18(1), 100024 (2020) 4. E.N. Velichko, G.L. Klimchitskaya, E.K. Nepomnyashchaya, Casimir repulsion though a water-based ferrofluid. Mod. Phys. Lett. A 35, 2040016 (2020) 5. E.N. Velichko, G.L. Klimchitskaya, V.M. Mostepanenko, Dispersion forces between metal and dielectric plates separated by a magnetic fluid. Tech. Phys. 64(9), 1260–1266 (2019) 6. K.G. Gareev, E.K. Nepomnyashchaya, Obtaining and characterizing a water-dased magnetic fluid. Bull. Russian Acad. Sci.: Phys. 83(7), 904–905 (2019) 7. G.L. Klimchitskaya, V.M. Mostepanenko, E.K. Nepomnyashchaya, E.N. Velichko, Impact of magnetic nanoparticles on the Casimir pressure in three-layer systems. Phys. Rev. B 99(4), 045433 (2019) 8. G.L. Klimchitskaya, V.M. Mostepanenko, E.N. Velichko, Effect of agglomeration of magnetic nanoparticles on the Casimir pressure through a ferrofluid. Phys. Rev. B 100(3), 035422 (2019) 9. G.L. Klimchitskaya, V.M. Mostepanenko, E.K. Nepomnyashchaya, E.N. Velichko, Impact of magnetic particles on dispersion forces in ferrofluid-based microdevices, in 2018 IEEE International Conference on Electrical Engineering and Photonics (IEEE, 2018), pp. 156– 159 10. E.A. Perigo, G. Hemery, O. Sandre, D. Ortega, E. Garaio, F. Plazaola, F.J. Teran, Fundamentals and advances in magnetic hyperthermia. Appl. Phys. Rev. 2, 041302 (2015) 11. E.A. Turov, M.P. Petrov, Nuclear мagnetic resonance in ferro- and antiferromagnets (Halstead Press-Wiley, New York, 1972) 12. J.M. Vargas, E. Lima, R.D. Zysler, J.G.S. Duque, E. De Biasi, M. Knobel, Effective anisotropy field variation of magnetite nanoparticles with size reduction. Eur. Phys. J. B 64 (2), 211–218 (2008) 13. A.S. Mazur, I.V. Pleshakov, A.V. Khudyakov, E.E. Bibik, Y.I. Kuzmin, NMR investigation of iron-containing magnetically ordered nanomaterial used for preparing of magnetic fluid. J. Phys.: Conf. Ser. 1326(1), 012009 (2019) 14. S.V. Stolyar, S.V. Komogortsev, L.A. Chekanova, R.N. Yaroslavtsev, O.A. Bayukov, D.A. Velikanov, M.N. Volochaev, E.V. Cheremiskina, MSh Bairmani, P.E. Eroshenko, R.S. Iskhakov, Magnetite nanocrystals with a high magnetic anisotropy constant due to the particle shape. Tech. Phys. Lett. 45(9), 878–881 (2019) 15. A.G. Gurevich, G.A. Melkov, Magnetic Vibrations and Waves (Nauka, Moscow, 1994). (in Russian)

Chapter 5

The Statistical Description of de Haas— van Alphen Oscillations in Silicon Nanosandwich Vladimir Romanov , Vadim Kozhevnikov , Vladimir Grigorev , and Mariia Filianina Abstract Here, we present room temperature de Haas—van Alphen oscillations measured in silicon nanosandwich in a weak magnetic field. Our results demonstrate a decrease of the oscillation magnitude with increasing magnetic field strength. This behavior is drastically different from the results reported earlier and it is attributed to the low-dimensionality of the studied structure, which enables room temperature observation of the de Haas—van Alphen effect in moderate magnetic fields up to 1000 Oe. We employ the classic Lifshitz-Kosevich formalism based on the dependence of the carrier effective mass on the applied magnetic field, to statistically describe this effect. We note that the statistical approach allows a more accurate interpretation of the experimentally observed results as compared to the previously used approach on the basis of classical thermodynamics. In particular, it allows us to highlight the non-oscillating contribution of the magnetization and its impact on the shape of the observed curve. Furthermore, we analyze the relation of the obtained carrier effective mass with the specific features of the studied silicon nanosandwich, which are determined by the formation of negative-U delta barriers within this structure.




Keywords Magnetization Silicon nanosandwich quantization De Haas–van Alphen effect





Effective mass
Size

V. Romanov (&)
V. Kozhevnikov Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia e-mail: [email protected] V. Kozhevnikov e-mail: [email protected] V. Grigorev
M. Filianina Johannes Gutenberg University, Mainz 55128, Germany e-mail: [email protected] M. Filianina e-mail: mfi[email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_5

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Introduction

Statistical description of the magnetization in an external magnetic field, known as the de Haas-van Alphen (dH-vA) effect, was first predicted by L. Landau in 1930 and then was carried out by Lifshitz and Kosevich [1, 2]. Lifshitz and Kosevich considered the oscillating contribution of the magnetic moment of an electron gas within thin metallic layers with an arbitrary dispersion law and obtained the relations for the general case. Their results played a key role in the analysis of experimental results for metallic and half-metallic systems, e.g. to study the Fermi surface shape, determine the carrier effective mass etc. A number of extensive reviews give a detailed overview on this topic, among which we highly recommend the works by S.V. Vonsovskij “Magnetism” and D. Shoenberg “Magnetic oscillations in metals” [3], and works [6–11]. In the light of growing interest towards low-dimensional structures where the dH-vA effect was observed, the statistical description by Lifshitz and Kosevich [1, 2] retained its relevance. However, after accurate analysis of the magnetization behaviour in such structures, prof. Shoenberg pointed out that besides the critical fields, amplitudes and shape of the oscillations which can be extracted using the Lifshitz-Kosevich formalism, the additive diamagnetic and paramagnetic contributions also play an important role in the magnetization state and need to be taken into account. It is worth mentioning that even though Kosevich and Lifshitz themselves indicated this problem, their calculations did not include the non-oscillating magnetization contribution [2]. This is further elaborated in Refs. [3, 5], which reveal that the variation of the gamma-parameter, the phase correction c, can be non-trivial in the case of an arbitrary dispersion relation in an external magnetic field. Specifically, this parameter determines the level of the magnetization around which the oscillation takes place. Alternatively, the non-oscillating magnetization contribution in the dH-vA effect can be found experimentally. The aim of this contribution is to discuss the influence of the external parameters on the carrier effective mass, which is one of the key quantities as it determines the conductivity of the studied structure. As discussed by Datta in Ref. [4], the features of the dH-vA oscillations as well as the carrier effective mass should depend on the external magnetic field, while the Lifshitz-Kosevich formalism [1] assumes that the effective mass is defined by the intrinsic properties of the structure and is independent of the external parameters, e.g. the external magnetic field.

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Experiment

In this work we investigated a silicon nano-sandwich made of an ultra-narrow 2-nm-wide p-type quantum wire (Si-QW) surrounded by strongly B-doped delta barriers forming a negative-U dipole centers. The nano-sandwich was fabricated on an n-type Si(100) surface and exhibited low carrier effective mass and highly suppressed electron interaction [12–14]. The measurements of the field dependence of the magnetization were carried out at room temperature using the magnetic field strength up to 800 Oe. For the experiments we used a Faraday Balance setup based on the MGD 312 FG spectrometer and the measured dependencies we analysed using the Faraday method. During the field scan with a given step size ΔH, each measurement was acquired in a thermodynamic equilibrium, which was automatically controlled by a custom-designed software. The magnetic field step size, ΔH, was set to 1 Oe, which allowed us to resolve the dH-vA oscillations up to the values of the filling factor m = 12. We point out that before the measurements the studies sample was kept in a closed container in air and at room temperature and was not affected by any external influences, which could disturb the thermodynamic state of the system. This is principally different from the experiments on GaAs/AlGaAs heterostructures, where the dH-vA oscillations are observed in the samples optically irradiated beforehand. Moreover, the size of the observed effect in this case in the order of 10−5 of the measured signal [10]. The results of the magnetization behaviour in the silicon nano-sandwich exhibiting the dH-vA oscillations at room temperature and in weak magnetic field reveal the decrease of the oscillation magnitude with increasing magnetic field (Fig. 5.1). The overall behavior of the non-oscillating contribution, in this case, is identical to that observed in Ref. [10]. In this work, we apply the Lifshitz-Kosevich formalism to the description of the experimentally observed field dependence of the magnetization in the silicon nano-sandwich and further adapt it to include the carrier effective mass dependence on the magnetic field.

5.3

Lifshitz-Kosevich Formula with a Variable Carrier Effective Mass

As mentioned above, we adapt the classic Lifshitz-Kosevich expression [1, 2] describing the dH-vA effect assuming the carrier effective mass variation with the external magnetic field. We set m0 to be the effective mass in the absence of the external magnetic field. Then, following the analytical expression derived in Ref. [15], we write the expression for the field-dependent effective mass, m*, as

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Fig. 5.1 Magnetic field dependences of the magnetization in a silicon nano-sandwich demonstrating the dH-vA effect at room temperature and in weak magnetic field. 1—experiment, 2— calculations with m0 ¼ 6
105 me and a ¼ 3
106


 m ¼ 1 þ aH 2 m0 where a is the parameter describing the rate of the effective mass increase with the magnetic field strength. Note that we use the model of the electron gas with an arbitrary dispersion relation, for which the free energy is expressed as follows    N X l  Ei F ¼ Nl  kT ln 1 þ exp kT i¼1 where l is the chemical potential, and the summation is over all electronic states with the energy Ei. It can be seen that in the case of the variable carrier effective mass the calculation of the free energy is identical to the case of the constant effective mass. Therefore, we skip directly to the expression of the oscillating part of the free energy of the system:

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ð1Þ where xc ¼ meH c is the cyclotron frequency. Now, we define Aðkz Þ as the area of the Fermi surface cross section in k-space by 00 a layer kz . Then, the parameters A0 and A0 are the expansion coefficients of the 2 00 function Aðkz Þ in the vicinity of kz = k0, where @A @z ¼ 0, i.e. A ¼ A0 þ k A0 =2, k = kz − k0, with A ¼ A0 being the area of the extreme cross section. @F Since the magnetic moment M is derived from the free energy F as M ¼  @H , we can write the oscillating part of the magnetic moment as:

ð2Þ Assuming

@xc @H

2

¼ ec m1 ð11aH , we obtain þ aH 2 Þ2 0

Thus, the oscillating part of the magnetic moment is defined as

ð3Þ Equation 5.3 allows us to directly estimate the effective mass. For the observation of the d-H-vA effect, the argument of the hyperbolic function is required to be around 1. Thus, using the field strength in the range of H  102  103 Oe and for the room temperature (300 K) we obtain the estimate for the effective mass which should be in the order of 105  104 of the electron mass m  105  104 me , with me being the electron mass. The value A0 can be found using the oscillation period as

where c is the phase correction. Our quantitative analysis reveals that the best agreement with the experimental results in Fig. 5.1 is achieved using the following values of the parameters: m0 ¼ 6
105 me and a ¼ 3
106 .

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As mentioned above, the nonperiodic contribution to the magnetization is defined by the dispersion relation and is taken into account by the term c, which in the case of an arbitrary dispersion relation can be determined from the experiment.

5.4

Discussion and Conclusions

In conclusion, in this work using the arguments of the statistical physics, we generalized the Lifshitz-Kosevich formalism of the description of the dH-vA effect to the case of an arbitrary dispersion relation which occurs in structures with the magnetic field-dependent carrier effective mass. The calculated amplitude of the oscillations is found to decrease with the increasing external magnetic field strength, which is in good agreement with our experimental results. Moreover, the behaviour of the oscillation amplitude as a function of the field strength obtained with the help of the generalized Lifshitz-Kosevich formula is consistent with the research results from [15–17], in which a relatively simple thermodynamic model is used. Despite the prevailing opinion [1, 2] that the non-periodic contribution determines the shape of the field dependence of the magnetization observed in the experiment (Fig. 5.1), our study shows that the value of the parameter c exhibits a non-trivial dependence on the external field strength and, thus, should be determined from the experiment. In Fig. 5.1 for clarity, we approximate the non-oscillating component of the magnetization variation with the external field using a parabolic dependence. This, indeed, is a crude approximation and leads to a shift of the oscillations level at some integer values of the filling factor. Thus, we obtain an accurate interpretation of the field dependence of the magnetization of the silicon nanosandwich at room temperature and in weak magnetic fields using the generalized Lifshitz-Kosevich formula, which includes the variation of the carrier effective mass with the external magnetic field.

References 1. I.M. Lifshitz, A.M. Kosevich, Theory of magnetic susceptibility in metals at low temperatures. Soviet Phys. JETP 2(4), 636–645 (1956) 2. A.M. Kosevich, I.M. Lifshitz, The de Haas-van Alphen effect in thin metal layers. Soviet Phys. JETP 2(4), 646–649 (1956) 3. D. Shoenberg, Magnetic Oscillations in Metals (Cambridge University Press, Cambridge, 1984) 4. S. Datta, Electronic Transport in Mesoscopic Systems (Cambridge University Press, Cambridge, 1995) 5. L. Roth, Semiclassical theory of magnetic energy levels and magnetic susceptibility of bloch electrons. Phys. Rev. 145(2), 434–448 (1966)

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6. I.D. Vagner, T. Maniv, E. Ehrenfreund, Ideally conducting phases in quasi two-dimensional conductors. Phys. Rev. Lett. 51(18), 1700–1703 (1983) 7. J.G.E. Harris, R. Knobel, K.D. Maranowski, A.C. Gossard, N. Samarth, D.D. Awschalom, Magnetization measurements of magnetic two-dimensional electron gases. Phys. Rev. Lett. 86(20), 4644–4647 (2001) 8. E. Gornik, R. Lassnig, G. Strasser, H.L. Stormer, A.C. Gossard, W. Wiegmann, Specific heat of two-dimensional electrons in GaAs-GaAlAs multilayers. Phys. Rev. Lett. 54, 1820–1827 (1985) 9. J.P. Eisenstein, H.L. Stormer, V. Narayanamurti, A.Y. Cho, A.C. Gossard, C.W. Tu, Density of states and de Haas-van Alphen effect in two-dimensional electron systems. Phys. Rev. Lett. 55(8), 875–878 (1985) 10. M.P. Schwarz, M.A. Wilde, S. Groth, D. Grundler, Ch. Heyn, D. Heitmann, Sawtoothlike de Haas-van Alphen oscillations of a two-dimensional electron system. Phys. Rev. B 65, 245315 (2002) 11. S.A.J. Wiegers, M. Specht, L.P. Lévy, M.Y. Simmons, D.A. Ritchie, A. Cavanna, B. Etienne, G. Martinez, P. Wyder, Magnetization and energy gaps of a high-mobility 2D electron gas in the quantum limit. Phys. Rev. Lett. 79(17), 3238–3241 (1997) 12. N.T. Bagraev, R.V. Kuzmin, A.S. Gurin, L.E. Klyachkin, A.M. Malyarenko, V.A. Mashkov, Optically detected cyclotron resonance in heavily boron-doped silicon nanostructures on n-Si (100). Semiconductors 48(12), 1605–1612 (2014) 13. N.T. Bagraev, D.S. Gets, EYu. Danilovsky, L.E. Klyachkin, A.M. Malyarenko, On the electrically detected cyclotron resonance of holes in silicon nanostructures. Semiconductors 47(4), 525–531 (2013) 14. V.V. Romanov, V.A. Kozhevnikov, N.T. Bagraev, Thermodynamic description of oscillations of the magnetization of a silicon nanostructure in weak fields at room temperature. Density of States. Semiconductors 53(12), 1633–1636 (2019) 15. V.V. Romanov, V.A. Kozhevnikov, N.T. Bagraev, C.T. Tracey, De Haas-van Alphen oscillations of the silicon nanostructure in weak magnetic fields at room temperature. Density of States. Semiconductors 53(12), 1629–1632 (2019) 16. V.V. Romanov, N.T. Bagraev, V.A. Kozhevnikov, G.K. Sizykh, C.T. Tracey, De Haas-van Alphen effect in a silicon nanosandwich: determination of the effective carrier mass. J. Phys.: Conf. Ser. 1236, 012013 (2019) 17. V.V. Romanov, V.A. Kozhevnikov, C.T. Tracey, B.S. Ermakov, The de Haas-van Alphen effect in a silicon nanosandwich at the room temperature. Numerical simulation of the oscillations with integer filling factors, in Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), ed. by E. Velichko, Saint Petersburg (IEEE, 2019), pp. 205–206

Chapter 6

Infralow Frequency Dielectric Spectroscopy of PMN Relaxor Aleksandr Vakulenko , Sergei Vakhrushev , and Ekaterina Koroleva

Abstract The results of ultrabroadband dielectric spectroscopy study of the PMN single crystal in the frequency range of 10−4 Hz < x6.0 g/cm3) which correlates with theoretical parameters. Also substrates are mechanically stable and have a surface roughness from 250 to 600 nm. Parameters achieved shows that the raw material for substrates was chosen correctly. The ink evaluation shows that Pt-particle ink has a good printing compatibility as well as high sedimentation stability (less than 0.2 mm per month). Only way to obtain the narrow line (40 µm) in microheater layouts was aerosol-jet printing, the second approach we used, inkjet printing, was not that efficient because of large ink droplets (the narrowest lines we were able to achieve were wider than 100 µm). The evaluation of cured Pt-heater showed an appropriate resistance in a range of 10–30 O. We tested the heaters with micro melting technique [11, 12] at temperatures up to 450–600 °C. To conduct these tests we used polyamide micro powder [13] to understand the dependence of power consumption on working temperature at the surface of microhotplate presented in Fig. 11.2.

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a

b

Fig. 11.2 a 40 µm wide platinum microheater printed by aerosol-jet system; b microhotplate membrane after 3YSZ substrate laser cutting. µm wide Pt microheater during evaluating on heating tests up to 450–600 °C by micro melting technique with using micro powder of polyamide

11.4

Discussion

To fabricate all parts of the gas sensor we used digital technological flow only. To be able to use this approach we developed a 3D-model of the future device using special software, as a result we got a file in.STL format. To reduce the price we used an Al2O3 monolithic ceramics to manufacture the package for the sensor. We used a widely-spread SOT-23 package (size is 3.0  1.4  1.0 mm) as a form factor for sensor because it makes it possible to dissipate a heating power up to 350 mW at a room temperature [10]. Special 20 W fiber laser with tunable pulse duration in range of 50–200 ns and a wavelength of 1.064 lm, controlled by specially produced software [14], was used to fabricate different parts of obtained sensor (as presented in Fig. 11.3). This approach allows us to combine the process of micronilling with on-line comparison of fabricated device with its geometrical parameters at 3D model. Fabricated from monolithic Al2O3 of MOX sensors package by laser micromilling part additional had checking on geometric parameters and roughness of surface by “S neoxSensofar” 3D Optical Profiler [15] which results are presented in Fig. 11.3. The roughness of ceramics formed after laser treatment is crucial for the application of thick film coatings, and the correct geometric dimensions are critical for joining various parts of the sensor package. To constantly reproduce the results on such small sizes (SMD SOT-23 form-factor package), it is necessary to automatically control the geometrical and surface parameters.

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a

b

c

d

e Fig. 11.3 a 3D model of gas sensor parts, b assembling parts of sensor to single package c parts of gas sensor after laser micromilling d already assembled gas sensor, d fabricated by laser micromilling bottom part of the package and e checking geometric parameters by “S neoxSensofar” 3D Optical Profilerone

11.5

Conclusion

The main objective of the research was to show the reproduction and stability of the results, and the border where these results are located for chosen approach. As a solution, the ability to use the SOT-23 package form factor was demonstrated. Fabricated sensors are the product of successful compromise between using in current work technologies perfect mechanical stability of 3YSZ membrane fabricated by tape casting technology, high resolution of platinum metallization printed

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by aerosol-jet system and geometrical parameters and surface roughness of Al2O3 obtained by using laser micromilling. For the developed construction MOX of sensors mass production should be cheap and not require a clean room. In our work we tested a way of fabrication of all parts of gas sensor including a package using only materials with high resistance to aggressive environment (platinum and ceramics), in spite of this, used technologies allow us to reduce the cost of the final product and simplify the whole production process. That approach in fabrication of MOX of sensors order to make the technology available for many different agricultural applications, e.g. livestock facilities, food-storage, forestry, fish farming and horticulture seems quite promising for developing smart agriculture, where the devices must work under harsh environmental conditions such as high humidity level and temperature. Acknowledgements This research was funded by the Israel Innovation Authority, BMBF (Federal Ministry of Education and Research) in Germany with funding No. 02P15B520, and the Ministry of Science and Higher Education of the Russian Federation funding with unique identifier RFMEFI58718X0054 in frame of MANUNET project MNET17/ADMA-1147.

References 1. M. Dachyar, Knowledge growth and development: internet of things (IoT) research, 2006– 2018. Heliyon 5, 1–14 (2019) 2. A.D. Boursianis, M.S. Papadopoulou, P. Diamantoulakis, A. Liopa-Tsakalidi, P. Barouchas, G. Salahas, G. Karagiannidis, S. Wan, S.K. Goudos, Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet Things Article 100187 (2020, in press) 3. M. Mahbub, A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network. Internet Things 9, Article 100161 (2020) 4. N. Yamazoe, K. Shimanoe, New perspectives of gas sensor technology. Sens. Actuators B: Chem. 138(1), 100–107 (2009) 5. D. Spirjakin, A.M. Baranov, A. Somov, V. Sleptsov, Investigation of heating profiles and optimization of power consumption of gas sensors for wireless sensor networks. Sens. Actuators A 247, 247–253 (2016) 6. D. Spirjakin, A. Baranov, A. Karelin, A. Somov, Wireless multi-sensor gas platform for environmental monitoring, in IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings (2015), pp. 232–237 7. A.V. Vasiliev, A.V. Pisliakov, A.V. Sokolov, N.N. Samotaev, S.A. Soloviev, K. Oblov, V. Guarnieri, L. Lorenzelli, J. Brunelli, A. Maglione, A.S. Lipilin, A. Mozalev, A.V. Legin, Non-silicon MEMS platforms for gas sensors. Sens. Actuators B: Chem. 224, 700–713 (2016) 8. I. Simon, N. Bârsan, M. Bauer, U. Weimar, Micromachined metal oxide gas sensors: opportunities to improve sensor performance. Sens. Actuators B: Chem. 73(1), 1–26 (2001) 9. D. Briand, J. Courbat, Micromachined semiconductor gas sensors, in Semiconductor Gas Sensors (2013), pp. 220–260 10. N. Samotaev, K. Oblov, M. Etrekova, D. Veselov, A. Gorshkova, Parameter studies of ceramic MEMS microhotplates fabricated by laser micromilling technology, in Proceedings of 2nd International Conference on Metal Material Processes and Manufacturing (2020)

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11. F. Biró, C. Dücso, Z. Hajnal, F. Riesz, A.E. Pap, I. Bársony, Thermo-mechanical design and characterization of low dissipation micro-hotplates operated above 500 °C. Microelectron. J. 45(12), 1822–1828 (2015) 12. F. Bíró, Z. Hajnal, C. Dücső, I. Bársony, The critical impact of temperature gradients on Pt filament failure. Microelectron. Reliab. 78, 118–125 (2017) 13. Material data sheet, https://www.shapeways.com/rrstatic/material_docs/mds-strongflex.pdf. Accessed 30 May 2020 14. N. Samotaev, K. Oblov, A. Ivanova, A. Gorshkova, B. Podlepetsky, Rapid prototyping of MOX gas sensors in form-factor of SMD packages, in Proceedings of IEEE 31st International Conference on Microelectronics (MIEL) (2019) 15. https://www.sensofar.com/metrology/products/sneox/specifications/. Accessed 30 May 2020

Chapter 12

SOI Based Micro-Bead Catalytic Gas Sensor Nikolay Samotaev , Alexander Pisliakov , Dmitry Filipchuk , Maya Etrekova , Ferenc Biro , Csaba Ducso , and István Bársony Abstract One of the most dangerous threats to everyday life and industrial activity is the possible explosion of earth gas, which can occur in various conditions, but usually occurs due to an excess of explosive gases in an enclosed environment. In order to protect people at their home and workplace, several types of gas sensors have been developed. Nevertheless, one of the most useful devices for explosive and combustive gases detection is the catalytic gas sensor. This type of sensors has shown a good performance in detecting of flammable gases with concentration close to the lower explosion limit (LEL). In order to meet the growing need for portable devices further evolution of these gas sensors is required to make them smaller and reduce the power consumption. To achieve this goal it is essential to reduce the 120 to 150 mW power dissipation of the Pt-coil based sensors (pellistors). Low Power Thermocatalytic Sensors manufactured with SOI (silicon on insulator) technology can be functional at temperatures below 600 °C with the power consumption in a range of 20–50 mW. The current aim of researches is the elaboration of novel sensor processing and development of nanostructured catalyst layer which is stable and effective at high temperatures and compatible with microelectronic silicon MEMS technology. Keywords Catalytic gas sensor


Flammable gases
Lower explosion limit

N. Samotaev
A. Pisliakov
D. Filipchuk
M. Etrekova (&) National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe Highway 31, 115409 Moscow, Russian Federation e-mail: [email protected] F. Biro
C. Ducso
I. Bársony Centre for Energy Research, Institute for Technical Physics and Materials Science - MFA, Konkoly-Thege Miklós út 29-33, 1121 Budapest, Hungary © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_12

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Introduction

We suggest using catalytic gas sensors for flammable gas detection with concentrations close to LEL since these devices perform stable and have appropriate sensitivity at low gas concentrations. To make sensors compatible with movable and handling devices we have to reduce the minimum power dissipation of the sensors based on Pt-coil (pellistors), which now is in a range from 120 to 150 mW as it is shown in [1, 2]. At the same time the sensors must show appropriate stable performance and high sensitivity. Micro-hotplate structures described in [3] are able to show a stable work at temperatures up to 600 °C with power consumption equal to 20–50 mW but they are compatible with thin film techniques and thereby the deposition of separately processed nano-catalyst suspensions involves high challenge in view of the processing technology as this is a combination of thin and thick film processes. Moreover, the volume of the catalyst must be high enough to provide appropriate surface and thermal signal during operation [4]. The only serial design of this type sensors are silicon membrane catalytic gas sensor [5], but needs power of 110–130 mW [6] close to the level of standard pellistors. A compromise is the fabrication of a microscale filament heater from stable single crystal silicon and formation a micro-bead similar to the conventional devices. This work demonstrates the current status of device development. Having in hand the basic chip, currently we are interested in obtaining the stable nanostructured catalyst layer which will be compatible with MEMS thick film technology and will show a good performance at higher temperatures as well.

12.2

Materials and Methods

Uniform and reproducible crystalline Si filaments were formed from SOI (silicon on insulator) wafers, because the buried oxide provides uniform thickness of the device layer and guarantees identical geometry. Cantilevers are suspended on stress compensated SiO2-Si3N4 membrane to increase their mechanical stability and eliminate their bending out of the original plane (Fig. 12.1). Thereby the reduced stress provides longer lifetime. The higher resistivity of the device layer ensures higher filament resistance at the same temperature compared to its thin film metal reference, therefore the cross section of the current routes should be increased to achieve the sufficient resistance. A plausible advantage of the single crystalline filament material and the design is the minimized degradation effect of electro migration, thereby the lifetime of the heater is expected to achieve 6000–8000 h. Moreover, the heated area of the filament can be completely covered with catalyst or passive material, similarly to the coil-type filament devices. The opened side chip design facilitates catalyst deposition.

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a

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b

c Fig. 12.1 a Sketch of silicon cantilever type micro-heater pair. Both micro-heaters are opened at the right side in these pictures in order to facilitate suspension (catalyst) deposition and forming pearl-like catalyst material bead. b Layouts of the silicon cantilever type microheater. c The silicon cantilever type micro-heater layer structure

Different approaches of fabrication gas sensitive material for coil and silicon membrane sensors were used. For the coil formation catalyst should be bulky and form bead or cylinder with diameter in a range of 400–500 µm. The MEMS structures covered by the thinnest layer of catalyst were deposited on a micro-hotplate with a characteristic diameter of 100 µm, thus the catalyst layer forms a 2D surface. In the present work we target a micro-bead structure characterized with 40–50 µm diameter. To conduct the experiments described in the present work we used nano-dispersed Al2O3 and ZrO2 ceramic carriers. They were made by the following method. Each material was divided into two equal parts in order to fabricate an active catalytic layer from one part and a comparative element from the second part. Both substances were applied carefully to exhibit equal surface areas. Salts of platinum acid (H2PtCl6) and palladium chloride (PdCl2) were chosen as agents for the impregnation of the catalyst support. High temperature annealing allowed clusters in the catalyst support to form. To be able to deposit both materials with a drop-coating method onto MEMS silicone micro-heater we added an organic binder into active gas sensitive and passive reference materials.

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Results and Discussion

The synthesized active gas sensitive materials Al2O3 and ZrO2 were tested by scanning electronic microscope ZEISS EVO 50 XVP. SEM images of Al2O3 and ZrO2 with Pt-Pd catalysis are present on Fig. 12.2, respectively. The SEM study of the first attempt to fabricate non-agglomerated Pt and Pd nanoparticles intended to be supported on catalysts based on Al2O3 and ZrO2. In the Al2O3 part showed the presence of local and point inclusions of Pt and Pd against the background of the carrier (white spots in Fig. 12.2a). This can be explained by the difference in the surface properties of these materials and the tendency of the pure carriers to agglomerate at different speeds. In any case, it is necessary to optimize the calculation used to load the catalyst platinum acid (H2PtCl6) and palladium chloride (PdCl2) for each type of powder, even depending on the size of its particles. SEM views of the fabricated c-Si filaments are presented in Fig. 12.3. The continuously widening suspension arms are for proper heating and also for improved mechanical stability. The small upward bending is due to the residual stress but will lean at operational temperature. The chip is opened at sides in order to facilitate suspension (catalyst) deposition and forming pearl-like catalyst. In the presented pictures we demonstrate the feasibility of chip processing and also the suspension deposition technique. The diameter of micro-bead is 50 lm (Fig. 12.4).

a

b

Fig. 12.2 a SEM photo Pt-Pd catalysts on ɤ-Al2O3 ceramic carrier deposited and fired on micro-heater. b SEM photo Pt-Pd catalysts on Ce-ZrO2 ceramic carrier deposited and fired on micro-heater

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Fig. 12.3 SEM views of the c-Si cantilever heater. The side opening facilitates the deposition of the catalyst or passive coating in the reference element. The left picture is artificially colored

Fig. 12.4 SEM views of the micro-pellistor chip (artificially colored). One filament is coated with catalyst, the opposite with passive coatings. The manual deposition can be semi-automated in volume manufacturing

Coating the c-Si micro-heaters with chemically passive suspension we tested the filament stability. In order to access the life-time of the filament we operated at elevated temperature, i.e. at 800 ± 30 °C. The temperature was measured by micro-melting method as described elsewhere [7]. The filament is coated with Si3N4 on the top side, whereas with 1 µm thick SiO2 (buried oxide of the SOI wafer). Contrary to the Pt filaments the electro migration effect doesn’t govern in the degradation of the c-Si heater [8], but its oxidation must be considered. Knowing the barrier properties of the stoichiometric silicon-nitride we have to calculate only with the back-side oxide layer for the further oxidation of the filament. The lowest temperature in the available models for Si oxidation is 700 °C. At this temperature the Si consumption by the oxide growing is ca. 240 nm pro year. That means maximum 12% increase of filament resistivity. Nevertheless, the targeted operation temperature is 450–550 °C only, thereby we expect less than 3% change in resistivity. Considering similar changes in the active and the passive elements of the Wheatstone-bridge configuration we think that the filament characteristics will meet the required sensor parameters.

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The transfer from the volumetric to micro-bead design for the thermo-catalytic sensor involves many problems. First of all, this is the synthesis of a new gas-sensitive layer, the catalytic activity of which must be higher compared with traditional used materials. The solution of the problem starts with optimizing the classical materials have been used for many years in spiral-type pellistor sensors: primarily catalysts of platinum group metals on nanostructured Al2O3 or ZrO2 ceramics. The stability and behavior of those materials at high working temperatures have already been tested over tens of years in real working conditions (mains, gas line pipes, leakage alarm systems and etc.). The decrease in the quantity of catalytic material deposited on the c-Si micro-heater leads to insufficient catalytic activity of the sensor as a whole. An increase in the operating temperature can correct for the situation, but it is limited by the long-term stability of the silicon micro-heater, as well as by the transformation temperature of the crystallographic phase of the ceramic catalyst carrier. The critical temperature considering both effects in this work is 550 °C [9]. The use of ɤ-Al2O3 ceramics with a Pt-Pd catalyst enables the fabrication of the micro-bead sensor, provided a semi-automatic technology is available to transfer controlled volume of active and passive suspensions [10, 11]. Actually we could just demonstrate the feasibility of the process by manual manipulation. Most likely the solution is seen in the application of another carrier material what exhibits reversible crystal modification below 800 °C temperature, thereby preventing the change the surface area [12]. The ZrO2 is a good candidate but needs to be stabilized with additives of rare earth metals [13–15].

12.4

Conclusion

Nanostructured catalyst carriers were synthesized using aluminum oxide and zirconium oxide materials. Catalytic carriers were impregnated with Pt-Pd containing inks to form active and salts and reference suspension during deposition on filament. The feasibility of processing a one-side open cantilever-type pellistor from c-Si filaments was demonstrated. The filament can be covered with catalytic and reference suspensions from the side of chip using a capillary technique to form micro-bead type pellistor with a characteristic size of 50–60 µm. The device compatible life time of the heater is expected to excess 1 year at the operation temperature below 550 °C. Acknowledgements This research was funded by the National Research, Development and Innovation Office Foundation, Hungary, funding No. 2017-2.3.4-TeT-RU-2017-00006, and the Ministry of Science and Higher Education of the Russian Federation funding with a unique identifier RFMEFI58718X0053.

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References 1. E. Karpova, S. Mironov, A. Suchkov, A. Karelin, E.E. Karpov, E.F. Karpov, Increase of catalytic sensors stability. Sens. Actuators B: Chem. 197, 358–363 (2014) 2. http://www.alphasense.com/WEB1213/wp-content/uploads/2013/07/CHD3.pdf. Accessed 30 May 2020 3. F. Bíró, C. Dücső, G.Z. Radnóczi, Z. Baji, M. Takács, I. Bársony, ALD nano-catalyst for micro-calorimetric detection of hydrocarbons. Sens. Actuators B: Chem. 247, 617–625 (2017) 4. D. Spirjakin, A.M. Baranov, A. Somov, V. Sleptsov, Investigation of heating profiles and optimization of power consumption of gas sensors for wireless sensor networks. Sens. Actuators A 247, 247–253 (2016) 5. https://www.sgxsensortech.com/products-services/industrial-safety/mems-pellistor/. Accessed 30 May 2020 6. https://www.sgxsensortech.com/content/uploads/2014/07/DS-0140-VQ548MP-DatasheetV6.pdf. Accessed 30 May 2020 7. N. Samotaev, A. Pisliakov, A. Gorshkova, P. Dzhumaev, I. Barsony, C. Ducso, F. Biro, Al2O3 nanostructured gas sensitive material for silicon based low power thermocatalytic sensor, in Proceedings of MS-CAMC (2019) 8. E.E. Karpov, E.F. Karpov, A. Suchkov, S. Mironov, A. Baranov, V. Sleptsov, L. Calliari, Energy efficient planar catalytic sensor for methane measurement. Sens. Actuators A 194, 176–180 (2013) 9. F. Biro, C. Ducso, Z. Hajnal, A.E. Pap, I. Barsony, Optimization of low dissipation micro-hotplates - thermo-mechanical design and characterization, in Proceedings of THERMINIC - 19th International Workshop on Thermal Investigations of ICs and Systems (2013), pp. 116–121 10. H. Wang, L. Hou, W. Zhang, A drop-on-demand droplet generator for coating catalytic materials on microhotplates of micropellistor. Sens. Actuators B: Chem. 183, 342–349 (2013) 11. L. Wu, T. Zhang, H. Wang et al., A novel fabricating process of catalytic gas sensor based on droplet generating technology. Micromachines 10(71), 77–87 (2019) 12. E.I. Kauppi, K. Honkala, A.O.I. Krause, J.M. Kanervo, L. Lefferts, ZrO2 acting as a redox catalyst. Top. Catal. 59, 823–832 (2016) 13. M. Kogler, E.-M. Kock, B. Klotzer, L. Perfler, S. Penner, Surface reactivity of YSZ, Y2O3, and ZrO2 toward CO, CO2, and CH4: a comparative discussion. J. Phys. Chem. C 120(7), 3882–3898 (2016) 14. R.D. Monte, J. Kašpar, H. Bradshaw, C. Norman, A rationale for the development of thermally stable nanostructured CeO2-ZrO2-containing mixed oxides. J. Rare Earths 26(2), 136–140 (2008) 15. A. Muto, T. Bhaskar, Y. Kaneshiro, Y. Kusano, Y. Sakata, K. Murakami, Preparation and characterization of nanocrystalline CeO2–ZrO2 catalysts by dry method: effect of oxidizing conditions. Appl. Catal. A 275(1), 173–181 (2004)

Chapter 13

Precision Spectrometric Search Dosimeter-Radiometer Based on a Matrix SiPM, Designed to Restore the Geometry of Ionizing Radiation Sources Vitalii Florentsev, Gennady Baryshev, Aleksandr Berestov, Anastasia Kondrateva, and Aleksandr Biryukov

Abstract The improvement of spectrometric devices and methods makes it possible to create a fundamentally new device that can distinguish not only the energy characteristics and activity of ionizing radiation sources. Today, devices are also expected to be able to quickly determine the geometry of the source, and not only from a set of standard ones. The method discussed in the article describes how the use of SiPM matrices allows detecting angular characteristics of the direction of gamma radiation incidence on the detector, allowing you to determine the position and types of sources. The detector created by this method has high sensitivity and resolution. These properties make it possible to use it in nuclear medicine to study the distribution of isotopes in the human body, in crystallography, archaeology, radiation safety systems, etc. Keywords Dosimeter-radiometer search


Ionizing radiation sources
Spectrometric

V. Florentsev
G. Baryshev (&)
A. Berestov
A. Biryukov National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409 Moscow, Russian Federation e-mail: [email protected] V. Florentsev SPA CADETECH JSC, 115230 Moscow, Russian Federation A. Kondrateva Peter the Great St. Petersburg Polytechnic University (SPbPU), Polytechnicheskaya, 29, 195251 St. Petersburg, Russia A. Kondrateva Alferov St. Petersburg National Research Academic University, 194021 St.-Petersburg, Russian Federation © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_13

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Introduction

The most important thing today is to control the radiation situation at various facilities and institutions. The use of dosimetric equipment gives only a general idea of the effect of ionizing radiation on the human body. However, the sources of exposure may be of different nature, such as inert gases [1] and suspended particles of radioactive dust. Different concentrations of these isotopes can be dangerous, depending on the energy of the photons they emit and the half-life of the elements themselves. Such sources are both of man-made and natural origin. In this regard, it is not enough to have data only on the average ionizing radiation dose rate reduced to the Co60 or Cs137 isotope for rapid decision-making on dosimetric control at the facility. Nowadays there are plenty of spectrometric devices in operation for various purposes [2–10]. Monitoring the selectivity of the contribution of ionizing radiation from various isotopes is assigned to spectrometric installations. In the vast majority of cases, these devices that work on scintillation crystals such as LaBr or NaI, as well as on more verified and accurate HPGe, are massive and expensive devices for stationary use in laboratory conditions [11]. In this paper, we consider the principle of constructing a multichannel spectrometer of x-ray and gamma-ray energy range from 50 keV to 2 MeV, based on the use of a directed hybrid semiconductor detector with a scintillating substance deposited on its working surface. This type of detector can be used to build a compact directional dosimeter-a spectrometer of a wide range of energies. The main problem of building a spectrometer on SiPM is its low resolution, which complicates the possibilities of mathematical post-processing of the spectrum associated with line interference. The spectra of samples containing uranium and thorium (samples of soil, building materials) or other nuclides with a large number of gamma lines, measured on a spectrometer with a scintillation gamma detector, are one continuous multiplet. It is not possible to correctly divide such a spectrum into the peak spectrum and the Compton scattering spectrum. The spectrum of continuous Compton scattering, which is not separated from the peak spectrum, makes all the components of such a spectrum overlap and interfere [12, 13]. In the spectra of samples measured on spectrometers with cooled semiconductor detectors, due to more than an order of magnitude better resolution, it is possible to correctly divide the spectrum into a peak spectrum and a continuous Compton scattering spectrum. According to the selected peaks, one can judge the nuclide composition of the spectrum and, by matching the peaks of the nuclide line, evaluate the activity of nuclides [14]. The detection process on a semiconductor detector is similar to the process using classical approaches in spectrometry. It consists of receiving an electric voltage pulse from the detector and measuring its characteristics.

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The energy resolution of the spectrometer is also calculated using Eq. 1. E1  E2  100%; 2 ðE1 þ E2 Þ

e¼1

ð1Þ

The main reason for the deterioration of the energy spectrum is the fluctuation of ionization of atoms during the passage of an infected particle. The value of this fluctuation is probabilistic and is estimated by Eq. 2. I d ¼ qffiffiffi ; E EI

ð2Þ

where I is the ionization potential, E is the energy of a single photon. The second important factor that makes SiPM superior to classical devices with the exception of HPGe is the time resolution required for the formation of an electrical signal when a photon of ionizing radiation hits. In addition, these detectors have a relatively small value of the “dead time” parameter. The gamma-neutron direct-pointing wearable dosimeter is designed for individual dosimetric monitoring, with real-time data display, in addition, the dosimeter is equipped with an alarm when the thresholds of instantaneous dose rates are exceeded. The device is equipped with a short-range radio channel for transmitting accumulated daily information about the received doses (Table 13.1).

Table 13.1 Characteristics of the developed device No

Characteristics

Parameter

1

The type of the detected radiation

2

Type of detector

3

Limits of the allowed basic relative error of gamma radiation measurements, no more than Limits of the allowed basic relative error of neutron radiation measurements, no more than Mode of selectivity of gamma radiation energies

Photon radiation in the energy range of 0.06 keV on the 241Am line up to 3 MeV Neutrons with energies of 0.025 eV to 14 MeV Beta radiation (optional) DGb-0,25 Silicon ion-implanted detector-gamma radiation; GS20-lithium glass and SiPM for neutron radiation detection 10%

4

5

15%

50 keV 2,8 Mev

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Table 13.2 Comparison of the main parameters of this device with the corresponding parameters of the currently existing dosimeters-radiometers of ionizing radiation No

Characteristics

Parameter

1

Photon radiation in the energy range of 0.05 keV on the 241Am line up to 6 MeV Neutrons with energies of 0.025 eV to 20 MeV Beta radiation (No) Geiger-Muller gamma detector. GS20-lithium glass and SiPM for neutron radiation detection

Photon radiation in the energy range of 0.06 keV on the 241Am line up to 3 MeV Neutrons with energies of 0.025 eV to 14 MeV Beta radiation (optional) DGb-0,25 Silicon ion-implanted detector-gamma radiation; GS20-lithium glass and SiPM for neutron radiation detection Limits of the allowed basic relative error of gamma radiation measurements, no more than 10% Limits of the allowed basic relative error of neutron radiation measurements, no more than 15% Mode of selectivity of gamma radiation energies 50 keV 2,8 Mev

2

3

4

5

Limits of the allowed basic relative error of gamma radiation measurements, no more than 20% Limits of the allowed basic relative error of neutron radiation measurements, no more than—25% Mode of selectivity of gamma radiation energies—absent

Comparison of the main parameters of this device with the corresponding parameters of the currently existing dosimeters-radiometers of ionizing radiation is given in Table 13.2. Competitive advantages of other manufacturers are most often expressed in doubtful numerical indicators of the width of the measured value ranges. The developed solution focuses on increasing the accuracy and reliability of readings in the most exploited energy range. In the present devices, ion implanted detectors are used at later stages, while sipm and NaI(Tl) assemblies were used at earlier stages.

13.2

Approach and Methodology

The work was into three main stages. First of all, the properties of the detector itself, located in the fields of x-ray and gamma radiation of various degrees of intensity, were studied. The detector’s directional angle was estimated experimentally, and the results of the experiment made it clear that for the DGB-0.25 M device [15] the maximum function lies in the range of = 6 angular degrees along the horizontal axis relative to the plane, and = 8 angular degrees along the vertical axis. When turning at a higher angle, the signal from the detector (sensitivity) weakens by 3 dBm per angular degree. This data indicates that the detector is sufficiently focused when used for search purposes.

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Fig. 13.1 Waveforms of DB-0.25 M signals detection of Am 241 emission in visible light dimming mode (left) and in natural light (right)

Fig. 13.2 Scheme with step-up voltage converter

In addition, a study was conducted on the dependence of visible light on the surface of the device. Figure 13.1 on the left shows a measurement of the flow of photons with energies at a maximum of 60 keV, the source was the isotope Am 241, which is in the dark. In Fig. 13.1 on the right, too, but with natural light falling at a sliding angle on the detector surface. As you can see from the waveforms, the effect of visible light on the detector surface has little effect on the noise characteristics of the ionizing radiation detector. The principle of operation of an ion-implanted silicon detector is similar to several devices at once. If the detector has a cellular matrix structure, then its operation in terms of receiving an electrical signal is similar to the operation of silicon matrix (SiPM). Power, is supplied from a pulsed STEP-UP voltage converter [16], the scheme of which is shown in Fig. 13.2.

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Fig. 13.3 Scheme with the charge amplifier

Fig. 13.4 Functional diagram of the device

Then the voltage supply passes through the smoothing BIH filters. The signal is measured using a charge amplifier [16]. The gain is calculated using Eq. 3. The electrical capacitance of the detector is measured experimentally. P k ¼ 20  log

Cs

Cfb

 ;

ð3Þ

The circuit for switching on the detector and the charge amplifier is shown in Fig. 13.3.

13.3

Results of Application in a Portable Device

The parameters of the DGB-0.25 m detector are suitable for use in a portable device. A cortex-M4 microcontroller will be used to expand the capabilities and improve the quality of signal post-processing, display the spectrum and basic information on the LCD. This processor is sufficient to provide everything you need. From controlling a high-speed 16-bit ADC and high-resolution LCD, to digital signal processing on a mathematical coprocessor built into the core. The functional diagram of the device is shown in Fig. 13.4.

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As you can see from Fig. 13.4, the device is quite compact and multifunctional. The device is powered by a LiPo battery. Mathematical processing and data conversion is performed on the core of the microcontroller ARM Cortex-M4. The graphical user interface is also built into the management firmware.

13.4

Conclusion

As a result of the research, a number of key points can be noted. At the time of writing this publication, all the necessary experimental studies have been conducted to study the possibility of using portable semiconductor ion-implanted sensors in portable search dosimeters and spectrometers. Sensors of the DGb-0.25 M series have the necessary orientation for the search mode of operation. In addition, their amplitude and time resolution are not inferior to classical NaI scintillator-based spectrometers. By time resolution, they belong to the group of semiconductor sensors, with indicators significantly higher than scintillation ones. Electrical circuits that have been tested during the study and are necessary to ensure normal and long-term operation of the detector can be miniaturized for a portable device. Signal processing can be performed by the ARM core of the Cortex M4 microcontroller, which makes the device profitable in terms of power consumption, design complexity, and price. We have described how the use of SiPM matrices allows detecting the angular characteristics of the direction of incidence of gamma radiation on the detector, allowing you to determine the position and types of sources. The detector created by this method has a high sensitivity and resolution. These properties make it possible to use it in nuclear medicine, to study the distribution of isotopes in the human body, crystallography and archaeology, radiation safety systems, etc.

References 1. I.N. Bekmann, A.A. Schvirayev, Inert gases. Radiochimyia 29, 384 (1984). (in Russian) 2. O. Ryutaro, Spectrometric measurement device and program. Patent US 9,164,028 B2 (2015) 3. K. Chughtai, R. Heeren, Mass spectrometric imaging for biomedical tissue analysis. Chem. Rev. 110, 3237–3277 (2010) 4. S. Vozka, Device for UV-spectrometric analysis of gaseous compounds. Patent US 8,841,626 B2 (2014) 5. J. Petersen, On-chip electro membrane extraction with online ultraviolet and mass spectrometric detection. Anal. Chem. 83(1), 44–51 (2011) 6. F. Muehlberger, Method and device for the mass spectrometric detection of compounds. Patent US 7,910,883 B2 (2011) 7. R. Nelson, Ch. Borgers, Mass spectrometric immunoassay revisited. J. Am. Soc. Mass Spectrom. 22(6), 960–968 (2011)

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8. J. Bollinger et al., Improved sensitivity mass spectrometric detection of eicosanoids by charge reversal derivatization. Anal. Chem. 82(16), 6790–6796 (2010) 9. Y. Hwan et al., Surface acoustic wave nebulization facilitating lipid mass spectrometric analysis. Anal. Chem. 84(15), 6530–6537 (2012) 10. J. Lee et al., Laser desorption/ionization mass spectrometric assay for phospholipase activity based on graphene oxide/carbon nanotube double-layer films. J. Am. Soc. Mass Spectrom. 132(42), 14714–14717 (2010) 11. Multi-channel gamma-ray spectrometers for measuring x-ray and gamma radiation CANBERRA. Certificate in the State register of measuring instruments of the Russian Federation No. 18509-04 (in Russian) 12. ATOMTEX Gamma-beta-spectrometer MKC-AT1315, http://www.atomtex.com/ru/products/ spektrometry-stacionarnye/gamma-beta-spektrometr-mks-at1315. Accessed 18 June 2013. (in Russian) 13. A.E. Bahur, V.I. Malyshev, L.I. Manuilova, D.M. Zuev, Radioecology and alpha-spectrometry. Apparatura i novosti radiatsionnykh izmerenii [Hardware and radiation measurements news] 2, 32–39 (1995). (in Russian) 14. ORTEC Semiconductor Photon Detectors, http://www.ortec-online.com/Solutions/ RadiationDetectors/detector-stocklist.aspx. Accessed 12 Apr 2017 15. Silicon ion-implanted detector DGB-0.25 M, https://sniipplus.ru/products/дгб-025м/. Accessed 20 Nov 2019 16. R. Erickson, D. Maksimovich, Fundamentals of Power Electronics, 2nd edn. (Kluwer Academic Publishers, University of Colorado, Boulder, 2000)

Chapter 14

Flexible Piezoelectric Nanogenerator: PVDF-CsPbBr3 Nanocomposite B. A. Darshan , Kumar E. Dushyantha , H. S. Jithendra , A. M. Raghavendra , Kumar M. S. Praveen , and B. S. Madhukar Abstract In today’s world, human-machine interface technology is developing new ways of interaction between humans and machines. This paper attempts to design a flexible sensor that can be integrated with portable gloves. These gloves use electronic sensors to recognize different hand gestures. The glove is equipped with piezoelectric CsPbBr3/polyvinylidene fluoride (PVDF) nanocomposite strips which act as sensors attached at the joints of fingers. The proposed piezoelectric sensor strips exhibit lightweight, low cost, easily scale-up production, and high stability. The glove is integrated with Arduino UNO microcontroller or NodeMCU which analyses the sensor readings to identify the movement. This movement can be analyzed and applied for various applications such as home automation, smart city, sensor glove for hearing aid people, robot or robotic arm control, etc.




Keywords PVDF perovskite nanocomposite Sensor gloves automation Flexible electronics Superposition




14.1





Smart city/home

Introduction

Communication has a prime importance in present digital world. To do shopping, asking for directions and event planning etc., depend on reliable communication. Many have to face this reality and all the challenges associated with the inability to communicate. B. A. Darshan
K. E. Dushyantha
H. S. Jithendra
A. M. Raghavendra (&)
K. M. S. Praveen Department of Electronics and Communication, JSS Science and Technology University, Mysuru 570006, India e-mail: [email protected] K. M. S. Praveen e-mail: [email protected] B. S. Madhukar Department of Chemistry, JSS Science and Technology University, Mysuru 570006, India © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_14

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We can see developments in field of wearable electronics represents a paradigm shift in consumer electronics assemblages with human motion monitoring and integration with surgical tools as well as body sensor networks [1]. However, devices work with sustainable energy resource, technologically and economically feasible human-machine interfacing and implantable biomedical devices are already demonstrated [2, 3]. Further integration with biomechanical motions as the smart garment can quantitatively monitoring and recognizing human activities in situ [4, 5–7]. The glove-based gesture recognition systems are applicable in home automation, design and 3d modelling, manufacturing, health-care, robotics, device control, sign language recognition etc. Gloves developed here are able to recognize different gestures that are computer programmed to produce a specific letter or word [8]. In past various attempts were made to carry out measure analog haptic data using gloves and then use it to control machines, the first prototypes were made in the 1980s, such as the MITLED glove which was used to track the position of the hand and then show it as a computer graphics animation [9]. The digital data entry glove used touch, proximity, tilt and inertial sensors to measure finger joint flexion and wrist tilting and was able to convert these motions into 96 ASCII letters and numbers [10]. The gloves developed in 1982 was basically made of plastic tubes and light sensors that were able to measure and store joint angles. The Cyber Glove consists of 22 piezoresistive sensors for measuring finger flexion, including software for virtual modelling [11]. Other glove developed includes 20 Hall-effect sensors, using that joint bending angles for motion analysis and medical applications are measured. Most of the above mentioned gloves use electronic sensors need external power supplies, mounted on the cloth supports. A common drawback in this approach is a less efficiency, low accuracy and high power consumption by the sensors. Moreover, self-powered motion sensor system for HMI showed a great potential toward the many practical applications [12]. Recently piezoelectric nanogenerators have come to light as a promising green technique for the harvesting of electrical energy from such mechanical energy resources through piezoelectric effect [14]. The demonstration of power generating devices based on nanomaterials, piezoelectric zincoxide (ZnO) nanowires arrays were successfully implemented [13]. In this work a conductive strip made of cesium lead bromide perovskite nanoparticle with piezoelectric properties is integrated into a glove. Here strip is integrated in such a way that it is stretched and compressed with the movement of finger joints. The quantity of charge deposition on this strip surface is proportional to pressure applied. All-inorganic CsPbBr3 perovskite nanocrystals (NCs) have attracted widespread attention as a new type of photovoltaic material. CsPbBr3 NCs have many exceptional properties, such as excellent photoluminescence quantum yield (PLQY), narrow spectral bandwidth, and wide spectral tunability, which lay the foundation for the scientific and practical applications of CsPbBr3 NCs. It is very important to mentioning that CsPbBr3 NCs have already found broad applicability to optoelectronic devices, such as light-emitting diodes (LED), solar cells, photodetectors, and lasers. However, they are easily decomposed by heat, oxygen, ultraviolet (UV) radiation, and moisture on account of the strong ionic character and low formation energy of CsPbBr3 NCs. As a consequence, CsPbBr3 perovskite

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nanocrystals have poor stability, greatly limiting their practical applications. Poly-vinylidene difluoride is a hydrophobic polymer with superior piezoelectric, mechanical and film-forming properties, which is used as the polymer matrix for coating perovskite NCs in this paper. This paper aims to develop a prototype device to help in building a bridge for communication gap between hearing and non-hearing people. A conductive strip with piezoelectric properties is integrated into a glove, the strip is integrated in such a way that it is stretched and compressed with the movement of finger joints. The quantity of charge deposition on this strip surface changes as it is stretched or compressed, this change is notable and can be used to monitor hand gestures. Scanning electron microscopy (SEM) is used to study morphology and understand the working of piezoelectric strip. A portable system is designed to monitor analog data for each gesture using Arduino microcontroller and Node MCU.

14.1.1 Fabrication The CsPbBr3 is prepared by simple facial method under ambient condition 1wt/wt % of CsPbBr3/PVDF nanocomposite is prepared by simple solvent casting method. To prepare the 1wt/wt% CsPbBr3/PVDF nanocomposites following steps are employed. A known amount of PVDF is dissolved in dimethyl formamide (DMF), and to this solution of PVDF calculated amount of CsPbBr3 is added and stirred on a magnetic stirrer for about 45 min at 45 °C. After complete dissolution of polymer and the CsPbBr3 the solution is poured on cleaned glass plate and kept for drying for 4 h at 55 °C. Then a clean film of CsPbBr3/PVDF is obtained and it is used for the fabrication of piezoelectric materials. Figure 14.1 give brief picturization of the process of fabrication. Materials were fabricated for different dimensions such as 4  2 cm, 3.5  2.15 cm, 4  1.5 cm, with silver paste coated all over it for electric connection. These electric connections are then used to test the material under various conditions for its analysis, so that it can be used for different purposes, applications.

Fig. 14.1 Shows about steps involved in fabrication of piezoelectric material a–c CsPbBr3 nanocomposite material. d Flexible nanogenerator coated with silver paste. e XRD pattern grown on CsPbBr3 nanoparticles

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Results and Discussions

Piezoelectric is the property the material to convert mechanical energy into electric energy. Figure 14.2(a) tells about this property of the material. When no mechanical force is applied on the material, no voltage is produced, hence no deflection is observed in the voltmeter. But, when mechanical forces are applied on the material, as shown in the second part of Fig. 14.2(a), the force produces momentary deflection in the voltmeter. Once electric dipoles are aligned nanogenerator, the piezoelectric potential in a single direction when vertical compression is applied to the induced electrons flow through the electrode. As the compressive force is removed, the piezoelectric potential inside the device instantly disappears, the accumulated electrons move back to the top electrode, and an electric signal is observed in opposite direction. Figure 14.2(b) shows the response of the CsPbBr3 nanomaterial on applying random vibrations on it. The material approximately reaches around 60 mV along the time period. This random vibrations or oscillations of the material may be due to wind energy (caused outside or under fan), random gentle vibrations produced on the material, may cause this deflection to produce the voltage in the material. The voltage produced increases as the material thinness in size, and as the number of layer increases with superposition of material layers or the series connection. While the series connection increases the induced voltage from the nanogenerators, the parallel connection of the materials increases the current produced by the material around few Nano amperes. Also, the current density increases with the increase in concentration of the Nano particles.

(a)

(b)

Fig. 14.2 Shows the behavior of piezoelectric materials. a Working principle of the voltage and current scaling-up in nanogenerators. b Voltage read due continuous vibration of the piezoelectric material

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14.2.1 Experimental Proof of Concept Test The tapping on the material impulse mechanical energy, which impel the nanogenerators to produce voltage. Figure 14.3(a) shows the result graph obtained on tapping the material, which produced voltage around 30–35 mV as shown in the graph. A normal tap on the material would cause a voltage in the range of 20 mV, whereas the force applied each time on tap (as the theory specifies), increases voltage with increase in the magnitude of force applied. It can also be seen that implementation of superposition of materials i.e., connecting the 3 layers of material (above mentioned) in series, helped the produced voltage to reach around 65 mV. Hence, the output voltage can be increased with increase in layers of materials, and also by increasing in the concentration of the material. As the nature of the project is to develop a glove that can be used for various purpose, the material was also tested wearing to index finger and middle finger (Fig. 14.3(b)–(c)) to imply the structure of a glove, the results obtained are as shown in the graph. Wearing the glove to individual fingers produced an output voltage of 40–50 mV as the bending angle increased. The superposition theorem concept was used, and hence sensor connected to both fingers produced sudden deflection in the voltage produced, as the deflection angle increased towards 180° (Fig. 14.4).

Fig. 14.3 a Shows the results and demonstration of gentle tap on the material. The effect of bending angle and output voltage on b index finger, c middle finger d both middle and index fingers

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Fig. 14.4 Different bending angles considered

Fig. 14.5 a An equivalent circuit diagram of LED embedded with experimental photograph. b Arduino serial plotter with observed deflections

Tables shows the result obtained for testing the material at different bending angles. From the results obtained one can infer that, as the bending angles increases, the applied compressive force also increases proportionally and hence producing an increased voltage spike when the bending angle increases. The voltage obtained by bending index finger is greater than middle finger as the force due to bending movement of middle finger is less because of physical limitations of middle finger (Tables 14.1 and 14.2). LED is the simplest and best way to test the functionality of piezoelectric material. Here the idea is to make a low cost smart glove that can produce responsive voltage against finger actuation. To demonstrate this a single material is interface with ESP8266 (version 1.0)/Node MCU to read the analog input on vibrating the material or on bending the material on finger actuation. In Fig. 14.5(a), the outer picture shows the equivalent circuit diagram of the interfaced material, and inner picture shows the equivalent experimental circuit diagram. Figure 14.5(b), shows the serial plot in Arduino software on a resolution of 1023 scale, on vibration/

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Table 14.1 Index finger

Table 14.2 Middle finger

Table 14.3 Both index finger and middle finger

Bending angle

Voltage (V)

0° 45° 90° 135° 180°

0.03 0.023 0.049 0.051 0.057

Bending angle

Voltage (V)

0° 45° 90° 135° 180°

0.003 0.013 0.034 0.044 0.042

Bending angle

Voltage (V)

0° 45° 90° 135° 180°

0.02 0.03 0.041 0.046 0.065

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actuation. The receiving signal was set to a threshold of 10, which is used to trigger the red LED (wavelength 650 nm). This successful demonstration of the experiment can be extended to various applications such as controlling PWM of a fan motor, can also be used to control the direction of motion of robot. Increase in the output voltage produced is helpful to implement such applications (Table 14.3).

14.2.2 Applications The CsPbBr3/PVDF nanocomposite piezoelectric material-based hand glove can be applicable in various fields. Based on hand gesture or finger actuation operations to be performed are specified. This can give a new turn in home automation, to control robotic arm, device control, sign language recognition etc. In medicine and health care, electronic gloves are used for the rehabilitation and assessment of patients

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with contagious disease with use of robotic hand motion by acquiring hand glove gesture data. Also, it has application where there is need to monitor small movements, such as in case of coma patients. So that, an alarm can be alerted, if the patient is awake. Gloves developed here are able to recognize different gestures that are computer programmed to produce a specific letter or word. With higher wt/wt% nanocomposite the piezoelectric material can be used for energy harvesting applications. The material can be fabricated along with the keys of keyboard where the pressure applied is used to charge keyboard battery.

14.3

Conclusion

In this work, a piezoelectric material of 1wt/wt% CsPbBr3/PVDF nanocomposite was applied to fabricate flexible sensor strips which can sense vibrations, tensile force and compression force. Sensor is incorporated in hand gloves which sense finger-actuation and can be used for various remote operable applications. As our future work, with further investigation the material can be utilized in energy harvesting with more effective use of motion and enhanced manufacturing process.

References 1. J. Zhong, Y. Zhang, Q. Zhong, Q. Hu, B. Hu, Z.L. Wang, J. Zhou, Fiber-based generator for wearable electronics and mobile medication. ACS Nano 8(6), 6273–6280 (2014) 2. X. Pu, L. Li, M. Liu, C. Jiang, C. Du, Z. Zhao, W. Hu, Z.L. Wang, Wearable self-charging power textile based on flexible yarn supercapacitors and fabric nanogenerators. Adv. Mater. 28, 98–105 (2016) 3. Y.C. Lai, J. Deng, S.L. Zhang, S. Niu, Z.L. Wang, Single-thread-based wearable and highly stretchable triboelectric nanogenerators and their applications in cloth-based self-powered human-interactive and biomedical sensing. Adv. Fun. Mater. 27, 11604462 (2016) 4. Y.K. Fuh, J.C. Ye, P.C. Chen, H.C. Ho, Z.M. Huang, Hybrid energy harvester consisting of piezoelectric fibers with largely enhanced 20 V for wearable and muscle-driven applications. ACS Appl. Mater. Interfaces 7(31), 16923–16931 (2015) 5. T. Yamada, Y. Hayamizu, Y. Yamamoto, Y. Yomogida, A. Izadi-Najafabadi, D.N. Futaba, K. Hata, A stretchable carbon nanotube strain sensor for human-motion detection. Nat. Nanotechnol. 6, 296–301 (2011) 6. Y.K. Fuh, P.C. Chen, H.C. Ho, Z.M. Huanga, S.C. Lia, All-direction energy harvester based on nano/micro fibers as flexible and stretchable sensors for human motion detection. RSC Adv. 5, 67787–67794 (2015) 7. B.U. Hwang, J.H. Lee, T.Q. Trung, E. Roh, D.I. Kim, S.W. Kim, N.E. Lee, Transparent stretchable self-powered patchable sensor platform with ultrasensitive recognition of human activities. ACS Nano 9(9), 8801–8810 (2015) 8. S. Kumar, M. Junaid Sultan, A. Ullah, S. Zameer, S. Siddiqui, S. Kamran Sami, Human machine interface glove using piezoresistive textile based sensors. IOP Conf. Series: Mater. Sci. Eng. 414 (2018)

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9. S. Bryson, C. Levit, The virtual wind tunnel. IEEE Comput. Graph. Appl. 12(4), 25–34 (1992) 10. D.J. Sturman, D. Zeltzer, A survey of glove-based input. IEEE Comput. Graph. Appl. 14(1), 30–39 (1994) 11. G. Grimes, Digital data entry glove interface device. U.S. Patent 4 414 537, AT&T Bell Lab, Murray Hill, NJ, USA (1983) 12. W.Q. Yang, J. Chen, X.N. Wen, Q.S. Jing, J. Yang, Y.J. Su, G. Zhu, W.Z. Wu, Z.L. Wang, Triboelectrification based motion sensor for human-machine interfacing. ACS Appl. Mater. Interfaces. 6(10), 7479–7484 (2014) 13. Z.L. Wang, J. Song, Piezoelectric nanogenerators based on zinc oxide nanowire arrays. Science 312, 242–246 (2006) 14. B. Dutta, E. Kar, N. Bose, S. Mukherjee, NiO@SiO2/PVDF: a flexible polymer nanocomposite for high performance human body motion based energy harvester and tactile e-skin mechanosensor. ACS Sustain. Chem. Eng. 6, 10505–10516 (2018)

Chapter 15

Formation of Functional Conductive Carbon Coating on Si by C60 Ion Beam Vladimir Pukha, Julia Popova, Mahdi Khadem, Dae-Eun Kim, Igor Khodos, Alexander Shakhmin , Maxim Mishin , Kirill Krainov, Andrei Titov , and Platon Karaseov Abstract We report new carbon-based coating that can be utilysed as a hard chemically inert highly condictive transparent blanket for electrodes and moving parts in a wide range of electronic and MEMS applications. Diamond-like carbon film was grown on (100) n-Si substrate by accelerated C60 ion beam irradiation. Ion energy was kept at 5 and 8 keV, substrate temperature was varied in the range 100–400 °C. Irradiation with 5 keV beam results in growth of carbon films at all temperatures used. Films are amorphous with high amount of sp3-bonded carbon atoms. Graphite nanocrystals of *1.5 nm in size were found in the coatings grown at 400 °C. 8 keV beam sputters the Si target at 100 and 200 °C, while bombardment at higher temperatures (300 and 400 °C) results in carbon film growth. Graphite nano-crystal formation is much less pronounced at this energy. In all cases, silicon carbide interlayer is formed at the substrate-coating interface due to the ion-beam mixing effect, which results in a good adhesion of the coating. Electric resistivity of coating obtained with 8 keV ions at Ts = 400 °C is 1.5  10−4 X  m, whereas 5 keV ions at same Ts = 400 °C gives 9  10−4 X  m.










Keywords Functional coating Diamond-like carbon Fulleren C60 beam Ion beam deposition Sputtering Friction Conductivity














Ion

V. Pukha (&) Institute of Problems of Chemical Physics (IPCP RAS), Chernogolovka, Russia e-mail: [email protected] J. Popova Lomonosov Moscow State University, Moscow, Russia M. Khadem
D.-E. Kim Department of Mechanical Engineering, Yonsei University, Seoul, South Korea I. Khodos Institute of Microelectronic Technology and High Purity Materials, Chernogolovka, Russia A. Shakhmin
M. Mishin
K. Krainov
A. Titov
P. Karaseov Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_15

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Introduction

It is now obvious to improve functionality of various electric and mechanical properties of material using different coatings. Electric conductivity, corrosion resistance, friction, hardness etc. can be controlled this way. Carbon coatings are of extreme interest thanks to the wonderful ability of carbon to form wide variety of C-C bonding that gives unique possibility to change properties of a coating. Diamond-like carbon films (DLC) were first synthesized about 60 years ago. Nowadays the range of their applications is very wide: electronics and optoelectronics, sensing, aircraft and automotive industry, medicine, [1–8]. Basically, such films are used in the form of coatings to extend a device life time, since they have such properties as: hardness, wear resistance, smoothness and chemical inertness [2]. The structure of a film and (sp3)/(sp2) bond ratio, which determine its properties, depend on the process parameters, like (i) the substrate temperature during film growth, (ii) energy, kind and composition of particles coming onto the surface during the deposition [3]. The maximum value of (sp3)/(sp2) ratio, and at the same time the maximum hardness (up to 80 GPa) were achieved for films obtained from a beam of carbon ions with an energy *100 eV at room temperature [1]. Properties of these coatings are close to diamond. Therefore they are called Diamond-Like Carbon (DLC). However plasma–grown DLC possess a number of drawbacks, the main of which is a high level of mechanical stresses (over 10 GPa) and low hardness to elasticity modulus ratio (usually *0.1). These disadvantages of diamond-like carbon films frequently results in a brittle fracture of coatings, especially in tribological applications. To overcome these drawbacks, doping with Si and various metals is used [1, 2]. Nanocomposites with a lower level of mechanical stresses and a higher ratio of hardness to the Young’s modulus can be formed this way [3]. However, formation of low friction highly conductive well adhered chemically inert coating is still a great challenge. Recently, we developed a technique to deposit a pure carbon super hard nanocomposite film using a mass-separated C60 ion beam [8, 9]. Depending on the process parameters, graphite nanocrystals surrounded by a diamond-like matrix can be formed in the coating. Nanocomposite films of this type were formed at a substrate temperature exceeding 300 °C. Interestingly, fullerene-beam-deposited coatings possess low level of compressive stress, high ratio of hardness to Young modulus and demonstrate excellent tribological properties in atmospheric ambient conditions [9, 10]. It was found that the nanocomposite coated Co/Cr alloy exhibited high wear resistance accompanied by high biocompatibility when tested on rabbits [11]. Such coatings are of interest to protect the surfaces of artificial joints, MEMS devices etc. Fullerenes are interesting as they can form conductive coatings [12, 13]. However, adhesion of C60 film on a substrate is challenging. It is well established that the structure of the coating deposited by a wide plasma beam of monatomic carbon and CHn radical ions depends on the substrate temperature and ion energy [14–16]. Strong competition between sputtering and deposition processes, which in turn may depend on the temperature of the substrate,

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plays an important role in the deposition process. Same effect can occur in the case of carbon coatings deposition by fullerene C60 ions. Note, that the sputtering with fullerene ions can be much more pronounced in compare to monatomic C ion bombardment [2, 3, 17, 18]. Note, processes that take place on the surface of a target irradiated with cluster ions strongly differs from processes occur under monatomic ion bombardment [17–22]. Thus, on the one hand, substrate temperature and ion energy will affect properties of coatings grown by fullerene beam. On the other hand, the effect in not straightforward and requires additional experimental investigation. In this work we study carbon coating deposition by accelerated 5 and 8 keV C60 ion irradiation of (001) Si target at different substrate temperatures.

15.2

Experimental Procedure

Fullerene C60 powder with 99.5% purity (NeoTechProduct, St. Petersburg, Russia) was used in ion source. The fullerene powder was purified by vacuum distillation before use. The ion beam was generated using a source with a saddle electrical field. From a source chamber the ion beam passed through a magnetic mass-separator and then impinged the surface of a substrate. Beam was defocused onto a spot of *1 cm in diameter. The working pressure in the chamber during deposition was *10−4 Pa. In detail, an installation and film deposition technique are described in [6, 9]. Ion energy was 5 and 8 keV, and the fluence was kept constant 3.8  1016 ion/cm2 in all cases. P-doped single-crystal (001) Si wafers with a native oxide layer were utilized as a target. The substrate temperature (Ts) was kept at 100, 200, 300, or 400 °C during film deposition. Samples were cooled down to room temperature in the growth chamber before moving to characterization. In order to distinguish the samples we have developed the notation coding all important parameters as follows. First two symbols represent substrate material (Si), one digit after dash indicates fullerene ion energy, and last three digits after second dash shows substrate temperature during the deposition. For example, Si-5-300 denotes the film deposited by 5 keV beam at 300 °C. NanoFab-25 (NT-MDT) was used for X-ray photoelectron spectroscopy (XPS) investigation of the samples. Sub-nm thick Au film was thermally deposited onto the carbon film in order to check the possible charging-related-shift of C1s peak position. Electrical measurements were made by a four-probe technique.

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Results and Discussion

15.3.1 Investigation of Carbon Bonding Irradiated spot was well distinguished on all samples irradiated by 5 keV ions. Same can be observed on the samples irradiated by 8 keV ions at 300 and 400 °C, whereas at lower temperatures it was hardly seen. This finding indicates that no film appears on the surface in the latter case, which was further supported by Rutherford backscattering spectroscopy [21]. The surface composition of the samples and the structure of the chemical bonds of the carbon film were studied by XPS. Survey spectra for two samples are shown in Fig. 15.1. It reveals the presence of O1s and C1s peaks in both cases. Furthermore, Si2p peak is clearly seen on the spectra of the sample grown at 200 °C, but it is absent in the spectrum of the sample irradiated at higher Ts. = 400 °C. Thus, no film is formed at these low substrate temperatures at 8 keV ion irradiation. Film absence can be explained by strong sputtering that dominates at these substrate temperatures. The chemical bond composition of the carbon film was analyzed by the C1s peak deconvolution into Gaussian-like components [1, 2]. Sample of component decomposition of the C1s peak for the Si-5-300 sample is presented in the Fig. 15.2. It was assumed that the main C1s peak consists of four components: sp2 and sp3, which characterize the hybridization of carbon, as well as peaks corresponding to single (C-O) and double (C = O) carbon-oxygen bonds. For the Si-8-100 and Si-8-200 samples C1s peak contains one more maximum, which characterizes bonding between carbon and Si substrate atoms. This fact confirms the existence of a carbide layer on the surface. Formation of this interlayer was earlier assumed from RBS data analysis [21]. It can be formed at the initial stage of the irradiation by ion-beam mixing.

250

XPS yield, arb. units

Fig. 15.1 XPS survey spectra of Si substrate after irradiation with C60 ions. The Si 2p peak is seen for the sample grown at low Ts and absent for samples obtained at high Ts. (Color online)

C 1s

O 1s

200

Si-8-400

150

Si 2p

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Si- 8 - 200

50 0

0

100

200

300

400

Binding energy, eV

500

600

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Formation of Functional Conductive Carbon Coating …

Fig. 15.2 C1s peak for the Si-5-300 sample (red line) and its decomposition to four Gaussian components. Peak centers are indicated with black vertical lines. Designations are also indicated (Color online)

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Si- 5 -300

XPS yield, arb. units

sp3

sp2

C -O C=O

282

284

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288

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(a)

sp 3 sp 2 O2

5 keV

60 40 20 0

100

200

300

400 0

Substrate temperature, C

C1s peak components, %

C1s peak components, %

Binding Energy, eV

80

(b)

8 keV

SP 3 SP 2 O2

60 40 20 0

100

200

300

400 0

Substrate temperature, C

Fig. 15.3 Fraction of sp2, sp3 and O- bonded carbon atoms in the films obtained by irradiation of Si substrate by 5 keV a and 8 keV b fullerenes (Color online)

Figure 15.2 shows an example of the C1s peak and its de-convolution into four Gaussian curves. Good agreement is seen in the Fig. 15.2. The relative sp3 content in each specimen was then calculated by dividing the sp3 peak area by the total C1s area. The results shows that the sp3 amount in the coatings deposited by 5 keV C60 ions were approximately 60% and decreases to 50% with temperature increase to 400 °C (see Fig. 15.3a). Estimation of the oxygen absorption/desorption ratio made according to procedure given in [23] taking into account the deposition conditions used shows that amount of oxygen atoms incorporated into the films during deposition is quite negligible. Moreover, impact of energetic C60 ion led to the formation of acoustic waves resulting in desorption of oxygen from the surface [24]. Therefore, the presence of oxygen compound peaks clearly seen in the Figs. 15.1 and 15.2 should be attributed to the surface absorption of atmospheric oxygen during sample transfer to the XPS machine [25].

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Similar analysis of films obtained by 8 keV fullerene ions shows that the entire components of C1s peak for all samples are almost independent on the substrate temperature (see. Fig. 15.3b). Note, this result was obtained for both continuous and non-continuous films. Thus, the temperature controlled shift from substrate sputtering to film deposition found to be in the interval Ts = 200–300 °C, is not associated with the carbon bond fraction.

15.3.2 Structure of the Coating TEM study was done to investigate structure of the films. Silicon substrate was etched out and carbon film was placed onto TEM grid. Figure 15.4 presents typical TEM images of films obtained by 5 keV C60 ions at 100 °C (left panel) and 400 °C (right panel). Figure 15.4 shows that coatings deposited at 100 °C have an amorphous structure. From XPS data it follows that it id DLS with a large amount of sp3-bonded carbon (see Fig. 15.3). Temperature increase causes decrease in the concentration of sp3 bonding and increase in the concentration of sp2-bonded carbon atoms. At the same time, formation of bright rings on the SAED pattern is seen, which corresponds to disordered graphite structure. This finding can be explained by the formation of graphite nanoclusters. Indeed, small graphite nanosheets are visible in the micro diffraction pattern presented in the Fig. 15.5. The coatings obtained by 8 keV fullerene ions have a lower concentration of diamond bonds (see Fig. 15.3) At ion energy 8 keV, the growth of the carbon coating on the substrate begins at temperatures exceeding 200 °C. A sharp change in the interaction of C60 ions with the surface is observed with an increase in Ts in the range from 200 to 300 °C, where the temperature boundary of competition between sputtering and deposition is located, below which there is no continuous film. An effect characterizing the behavior of the carbon coating when the substrate

100°C

400°C

Fig. 15.4 Selected area electron micro-diffraction pattern of film deposited by 5 keV C60 ions at Ts= 100 °C (left) and 8 keV C60 ions at Ts= 400 °C (right)

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Fig. 15.5 The (002) dark-field TEM image of the film deposited by 8 keV C60 ions at 400 °C. Formation of graphite nanocrystals (white flakes) is seen. Bar length is 20 nm

400°

is irradiated with 8 keV C60 ions is found, which consists in the formation of carbon islands due to the diffusion of carbon atoms on the substrate surface. The components of the C1s peak (even for non-continuous films obtained at Ts = 100 °C and 200 °C) practically do not change with the temperature of the substrate. Thus, graphite nanocrystal formation is not associated with the concentration of sp3 bonds in the film. Thus, sputtering and deposition competition is strongly affected by substrate temperature. Winner changes in the temperature interval Ts * 200–300 °C. Formation of graphite nanocrystals is less pronounced at 8 keV than at 5 keV ion bombardment.

15.3.3 Sheet Resistance Besides the structural properties, the surface resistance of the obtained nanocomposite films has been studied. Two parallel silver strips 1  5 mm2 in size were applied at a 4 mm distance onto the sample surfaces. Resistance of Si substrate was found as 214 X. Similar measurement performed with 0.12 lm thick carbon film deposited by 8 keV C60 ions at the substrate temperature Ts = 400 °C gives the square resistance equal to 182 X. Thus, the electric resistivity of the film is 1.5  10−4 X  m. The electric resistivity of coating obtained with 5 keV ions at same Ts = 400 °C was measured as 9  10−4 X  m.

15.4

Conclusions

In conclusion, we report formation and properties of new carbon–based nanocomposite tha can be used as transparent conductive coating for micro- and nano-mechanical system part, sensors and actuators for biological and medical applications. Amount of sp3 bonded carbon atoms in the film deposited using 5 keV

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ions at deposition temperatures Ts = 100–300 °C is as high as 75%. It slightly decreases with temperature raise to 400 °C. Nanocomposite film consisting of graphite nanocrystals (*1.5 nm in size) embedded in diamond-like matrix forms at this Ts. Nanocomposite is formed due to the spatial separation of sp3 and sp2 components, and not due to the conversion of sp3 to sp2. Bombardment with 8 keV C60 ions sputters Si substrate at the temperatures up to 200 °C. Growth of carbon film starts at Ts= 300 °C. Amount of sp3 bonds in the films is slightly lower in this case and formation of graphite nanocrystals is less pronounced. Despite a significant amount of graphite present in the coating, they exhibit good mechanical properties, i.e. high hardness, low wear, and low friction coefficient. Formation of silicon carbide interlayer at the interface between the composite and the substrate provides excellent adhesion. Low sheet resistance provides possibility to use the composite films as a highly conductive protective electrode in various applications, like MEMS, actuators, detectors etc. Acknowledgements Work was supported by Russian Foundation for Basic Research (RFBR), grant # 19-58-51016 and the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (2019K2A9A1A06097636).

References 1. J. Vetter, 60 years of DLC coatings: historical highlights and technical review of cathodic arc processes to synthesize various DLC types, and their evolution for industrial applications. Surf. Coat. Technol. 257, 213–240 (2014) 2. C. Casiraghi, J. Robertson, A.C. Ferrari, Diamond-like carbon for data and beer storage. Mater. Today 10, 44–53 (2007) 3. C. Donnet, A. Erdemir (eds.), Tribology of Diamond-Like Carbon Films: Fundamentals and Applications (Springer, Heidelberg, 2007) 4. C.A. Love et al., Diamond like carbon coatings for potential application in biological implants —a review. Tribol. Int. 63, 141–150 (2013) 5. R.K. Roy, Biomedical applications of diamond-like carbon coatings a review. J. Biomed. Mater. Part B: Appl. Biomater. 83, 72–84 (2007) 6. Z. Ren, H. Qin, Y. Dong, G.L. Doll, C. Ye, A boron-doped diamond like carbon coating with high hardness and low friction coefficient. Wear 436–437, 203031 (2019) 7. M. Fenker, J. Julin, K. Petrikowski, A. Richter, Physical and electrical properties of nitrogen-doped hydrogenated amorphous carbon films. Vacuum 162, 8–14 (2019) 8. O.V. Penkov, V.E. Pukha, E.N. Zubarev, S.-S. Yoo, D.E. Kim, Tribological properties of nanostructured DLC coatings deposited by C60 ion beam. Tribol. Int. 60, 127–135 (2013) 9. V.E. Pukha, E.N. Zubarev, A.N. Drozdov, A.T. Pugachov, S.H. Jeong, S.C. Nam, Growth of nanocomposite films from accelerated C60 ions. J. Phys. D: Appl. Phys. 45, 335302 (9 pp) (2012) 10. M. Khadem, O.V. Penkov, V.E. Pukha, M.V. Maleyev, D.E. Kim, Ultra-thin carbon-based nanocomposite coatings for superior wear resistance under lubrication with nano-diamond additives. RSC Adv. 6, 56918–56929 (2016) 11. O.V. Penkov, V.E. Pukha, S.L. Starikova, M. Khadem, V.V. Starikov, M.V. Maleev, D.E. Kim, Highly wear-resistant and biocompatible carbon nanocomposite coatings for dental implants. Biomaterials 102, 130–136 (2016)

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12. I.B. Zakharova, D.I. Dolzhenko, V.F. Borodzyulya, N.T. Sudar’, The electroforming effect in polycrystalline fullerene C60 films. Tech. Phys. Lett. 45(2), 142–144 (2019) 13. D.I. Dolzhenko, I.B. Zakharova, V.F. Borodzulya, N.T. Sudar, Electroforming and resistive reversible switching effect in polycrystalline fullerene C60 films. J. Phys.: Conf. Ser. 1199(1), 012023 (2019) 14. O.A. Podsvirov, P.A. Karaseov, A.Ya. Vinogradov, A.Yu. Azarov, N.N. Karasev, A.S. Smirnov, A.I. Titov, K.V. Karabeshkin, Residual stress in diamond-like carbon films: role of growth conditions and ion irradiation. J. Surface Invest. 4, 241–244 (2010) 15. P.A. Karaseov, O.A. Podsvirov, K.V. Karabeshkin, A.Ya. Vinogradov, A.Yu. Azarov, N.N. Karasev, A.I. Titov, A.S. Smirnov, Influence of ion irradiation on internal residual stress in DLC films. Nucl. Instrum. Meth. Phys. Res. B 268, 3107–3110 (2010) 16. P.A. Karaseov et al., Effect of ion bombardment on the phase composition and mechanical properties of diamond-like carbon films. J. Surface Invest. 8, 45–49 (2014) 17. V.N. Popok, Energetic cluster ion beams: modification of surfaces and shallow layers. Mater. Sci. Eng. R 72, 137 (2011) 18. V. Lavrentiev et al., Controllable fabrication of amorphous Si layer by energetic cluster ion bombardment. Vacuum 98, 49–55 (2013) 19. P.A. Karaseov, K.V. Karabeshkin, A.I. Titov, M.W. Ullah, A. Kuronen, F. Djurabekova, K. Nordlund, G.M. Ermolaeva, V.B. Shilov, Single and molecular ion irradiation-induced effects in GaN: experiment and cumulative MD simulations. J. Phys. D: Appl. Phys. 50(50), 505110 (1–12) (2017) 20. K.V. Karabeshkin, P.A. Karaseov, A.I. Titov, Effect of an increase in the density of collision cascades on the efficiency of the generation of primary displacements during the ion bombardment of Si. Semiconductors 50(8), 989–995 (2016) 21. V.E. Pukha, N.N. Dremova, M.V. Maleyev, M.V. Mishin, A.L. Shakhmin, A.V. Arkhipov, K.V. Krainov, A.I. Struchkov, A.I. Titov, P.A. Karaseov, Target sputtering and carbon film deposition by C60 ion beam of keV energy, in Proceedings of XXIII International Conference on Ion-Surface Interactions (ISI-2017), ed. by V.E. Yurasova, A.I. Titov, E.Yu. Zykova, P.A. Karaseov, vol. 3 (MEPhI, Moscow, 2017), pp. 124–127 22. V.V. Kozlovski, A.E. Vasil’ev, P.A. Karaseov, A.A. Lebedev, Formation of radiation defects by proton braking in lightly doped n- and p-SiC layers. Semiconductors 52(3), 310–315 (2018) 23. L.I. Maissel, R. Glang, Handbook of Thin Film Technology (McGraw-Hill, New York, 1970) 24. R. Webb, Energetic cluster induced desorption from a graphite surface. Appl. Surf. Sci. 231– 232, 59–63 (2004) 25. S. Nakao, K. Yukimura, S. Nakano, H. Ogiso, DLC coating by HiPIMS: the influence of substrate bias voltage. IEEE Trans. Plasma Sci. 41, 1819–1829 (2013)

Chapter 16

Degradation of GaN Conductivity Under Irradiation with Swift Ions Platon A. Karaseov , Ashish Kumar, Andrei I. Struchkov , Andrei I. Titov , Kandasami Asokan, Dinakar Kanjilal, and Ambuj Tripathi Abstract We present the results of investigation of radiation hardness of GaN epitaxial layers grown on sapphire substrate. Samples were irradiated with swift Ni, Ag and Au ions at room temperature. Electric and structure response to irradiation has been carried out by employing in situ measurements of electrical resistivity of irradiated samples. Drastic raise of sheet resistance with the ion fluence increase was found. Nuclear energy loss plays a significant role in ion-beam induced resistivity change since they determine the formation of point defects by a decelerating ion. Electronic energy loss despite its much higher value is believed to be much less important in conductivity reduction. Device characteristics degradation is mainly caused by point defect formed by cosmic radiation.
















Keywords GaN Swift ions SHI Ion irradiation Sheet resistance Radiation damage Point defects Cosmic radiation Device degradation




16.1







Introduction

GaN is now in the market as the semiconductor material used in a number of important applications, like short wavelength electronics, optoelectronics, and high-temperature/high-power electronics [1, 2]. Application of GaN-based detectors and chips in radiation-harsh environment is promising [3]. Various GaN-based devices are expected to be used in avionic and satellite systems where radiation tolerance is critical. Indeed, the performance of electronic devices is strongly affected by the radiation-induced damage [4, 5]. Upper parts of the Earth’s atmosphere, as well as magnetosphere are bombarded by a nearly isotropic flux of energetic charged particles: protons, a-particles, and heavier ions. Particle energies P. A. Karaseov (&)
A. I. Struchkov
A. I. Titov Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia e-mail: [email protected] A. Kumar
K. Asokan
D. Kanjilal
A. Tripathi Inter-University Accelerator Center, New Delhi, India © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_16

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typically range up to several hundred MeV. Thus, study of radiation—induced damage formation is of great importance to improve the device lifetime and reliability. Ion irradiation is a method to simulate the effects of radiation rich harsh environments on materials. On the other hand, irradiation with accelerated ions is widely utilized in modern semiconductor technology to selectively introduce doping species or to form isolative areas. The last is especially effective in the case of wide band-gap material based devices [2]. Accelerated ion penetrating in matter transfers its energy via collisions with target atom’s nucleus and electrons [6]. Ion-nuclei interactions produce recoil atoms thus initiating formation of the so-called collision cascade consisting of point defects (interstitials and vacancies). Electron subsystem excitation energy mainly goes to the target heat via electron-phonon coupling. Various aspects of defect formation in GaN during ion implantation, like chemical effect of implanted species [7–9], and variation in collision cascade density [10–12] were studied. In particular, formation of electrical isolation layers in gallium nitride using light ion irradiation with energies about few MeV was investigated [13, 14]. Drastic decrease in the GaN layer conductivity with ion fluence increase [13] was ascribed to formation of complexes consisting of a point defect generated by a fast ion and a doping impurity atom [14]. Conductivity of a sample is decreased due to following reasons [14–16]: (1) Carrier mobility decreases due to enhanced scattering on lattice defects; (2) Free carrier concentration decreases due to carrier capture to deep defect levels, which compensate shallow donor or acceptor levels; (3) Shallow doping levels can disappear due to formation of complexes consist of that doping center and one or more point defects generated by stopping ion. Ion energy transfer dramatically changes with the energy increase. Probability of ion interaction with target nuclei decreases and nuclear energy loss becomes very small. At the same time, excitation of electronic system of a target significantly rises up, and could reach tens of keV/nm [6]. So large energy deposition results in a strong local heating and even melting of a specific volume around a straight ion trajectory. Disordered zone (track) may form along ion path. These tracks may have either amorphous or nanocrystalline structure in GaN [17–19] and were found to be continuous or consisting of a number of separated disordered zones along ion trajectory. These disordered volumes will affect electrical properties of a GaN target. Penetration depth of swift ions can be sufficient to enter device structures used in avionics and space applications and degrade their performance. Thus, it is important to investigate the influence of swift ion irradiation on sheet resistivity of GaN. Earlier experiments [20, 21] on swift ion induced degradation of GaN conductivity have shown very weak influence of 100 MeV O ions. On the other hand, sheet resistance raised several orders of magnitude under 200 MeV Ag ion irradiation at 77 K [22]. The degradation of electric conductivity of GaN layers under ion bombardment can go significantly different ways in swift and lower energy regimes. However, the lack of experimental data in the swift energy regime for GaN makes it difficult to develop a proper model considering electric conductivity evolution mechanisms.

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In this work we investigate the influence of irradiation with swift on the electric conductivity of GaN epitaxial layers to clarify role of different defect formation mechanisms on the degradation of sheet resistance.

16.2

Experimental Procedure

GaN epitaxial films used in this study were grown on sapphire substrates at Ioffe Institute (St.-Petersburg, Russia) by MOVPE technique. Silicon doped * 2 um thick GaN layer, grown on nominally undoped * 1 um thick highly resistive GaN buffer, has carrier concentration of 5  1019/cm3, surface roughness (RMS) less than 1 nm over 10  10 um2 scan area and dislocation density about 2  108 cm−2. Wafer was cut into 8  8 mm2 pieces and cleaned in acetone and isopropyl alcohol in ultrasonic bath for 10 min each. Indium ohmic contact pads were applied onto the corners of rectangular samples. The sample resistivity of pristine GaN epitaxial layers was Rs = 100 X cm. Irradiation was performed using 15 MeV Pelletron accelerator facilities available at Inter University Accelerator Center (IUAC), New Delhi, India. Set of ions used in this study is listed in the Table 16.1. A constant beam flux of 6.25  109 ions/ sec/cm2 was maintained during all irradiations. Sample surface normal was kept tilted about 7° relative to beam direction to avoid channeling effects. Sheet resistance of GaN layers was measured in situ using Van der Pauw technique. Electrical connections were taken out of irradiation chamber to an Agilent Semiconductor Analyzer (B1500A). A standard protocol of measurement was followed where fluence if each ion was fractured and in situ I-V characteristics were recorded after 10 min of ion beam fracture exposure. All experiments were performed at room temperature 300 K.

Table 16.1 List of ions analyzed in this study, ion energy E, corresponding electronic (Se) and nuclear (Sn) energy loss, their ratio (Se/Sn), and g—averaged number of point defects generated in GaN target by an individual ion, as simulated by SRIM [21]

Ion

E, MeV

Se, keV/nm

197 79 Au 108 47 Ag 57 28 Ni

115 120 50

Sn, eV/nm

Se/Sn

24.2

514

47

22.7

120

189

12

54

241

6.1

13

g, 106 cm−1 46

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Results and Discussion

16.3.1 In situ Resistivity Measurements As it was mentioned in the Introduction section, mechanisms of defect formation under swift ion irradiation are driven by ion energy transfer in collisions with the target electrons and nuclei. To study the role of these two energy transfer channels in the degradation of the GaN conductivity, we choose the kind and energy of ions to keep one of them constant, changing the second and vice versa. I.e., 120 MeV Ag, and 115 MeV Au ions have almost same electronic energy loss, whereas nuclear energy loss increases by almost an order almost of magnitude. Both electron and nuclear loss of 50 MeV Ni ion are about twice less than that of 120 MeV Ag ions (see Table 16.1). Figure 16.1 illustrates measured resistivity of GaN layers with the fluence increase at low fluences (Fig. 16.1(a)) and in the whole fluence range (Fig. 16.1 (b)). It is seen from these figures that in all cases sheet resistance increases with ion fluence, i.e. irradiation causes the degradation of GaN conductivity. Figure 16.1(a) reveals the difference in the resistivity growth rate. It scales in the following sequence: 50 MeV Ni < 120 MeV Ag < 115 MeV Au. Figure 16.1(b) reveals that each of the heavy ion curves has at least two distinct fluence regions. The first region comprises the lowest doses, where sample resistance increases only slightly with increasing ion fluence. The second region is characterized by a very fast increase in the value of Rs. In the case of 50 MeV Ni and 120 MeV Ag ions in can raise by 9–10 orders of magnitude in a relatively narrow fluence interval. Interestingly, shape of the experimental curves in the Fig. 16.1(b) is very similar to one obtained in the case of much lower (few MeV) energy ion irradiation, when nuclear energy loss is much more pronounced in energy transfer [13–15], and no track formation is possible.

Fig. 16.1 Relative sheet resistance of GaN epitaxial films irradiated with different ions as indicated in the legend at low fluences a and in the whole fluence range b

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Threshold energy for track formation in GaN was found to be * 15 keV/nm [18]. According to SRIM simulations, inelastic energy loss in GaN is about 24 keV/ nm and 22 keV/nm for 115 MeV Au and 120 MeV Ag ions respectively. Thus, at least discontinuous tracks could be formed during irradiation in these cases.

16.3.2 Discussion Two main mechanisms are responsible for energy transfers from an accelerated ion to a target are electronic and nuclear energy loss. It is obvious to suggest major role of electron excitation processes due to very high loss of the ion energy via this mechanism. Indeed, as can be seen from the Table 16.1, it is about 24 keV/nm in the case if 115 MeV Au ion irradiation, whereas nuclear loss is almost three orders of magnitude lower (514 eV/nm). On the other hand, even twenty times lower nuclear energy loss drives the process of conductivity degradation in the case of MeV light ion bombardment [13, 15, 16, 24]. Moreover, as it was mentioned above, the shape of the experimental curves in the Fig. 16.1(b) is very similar to one obtained in the case of much lower (few MeV) energy ion irradiation, when nuclear energy loss is much more pronounced in energy transfer [13–15], and no track formation is possible. These observations and facts show the possibility of the manifestation of both energy transfer mechanisms in determining the growth of resistance. The interest in elucidating their relative role has determined the choice of ions and energies used in this experimental study (see Table 16.1). Indeed, Ag and Au ions transfer enough energy to the electronic system of GaN target to produce damaged tracks. Such amorphous pockets are to be considered as switched off from the conductive state. Let us estimate relative volume of amorphous zones formed within tracks, assuming them discontinuous with one third of its volume being amorphized. Average cross section area of track created in GaN by 104 MeV Pb ion (having almost same 24 keV/nm energy loss) will be less than * 50 nm2 as follows from high resolution plain view TEM images given in [18]. Thus, total amorphized area will be less than * 0.03% after target irradiation to highest experimental fluence of 120 MeV Ag ions, which is equal to 3  1012 cm−2. It will affect sample resistance, but such a low value cannot lead to an increase in sheet resistance Rs by 10 orders of magnitude. Indeed, space charge region formed around damaged cluster with “intrinsic conductivity” will somehow enlarge this volume. But it still will not be enough to explain experimentally observed resistance change. On the other hand, nuclear energy loss of 120 MeV Ag ions in GaN is about 120 eV/nm [23]. Since the threshold energy of point defect formation in gallium nitride is several tens of eV [25], each ion produces at least four point defects per nm of its path in the target. This fact is further illustrated by Fig. 16.2, where passage of 50 ions through GaN target is drawn. Red lines represent trajectories of Ag ions, and Ga and N recoil trajectories are drawn by green and blue lines. It is seen from Fig. 16.2 that each Ag ion creates a certain number of recoil atoms in

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Fig. 16.2 TRIM code simulation of 120 MeV Ag ion penetration in 3 um thick GaN layer (50 events). Typical trajectories of Ag ions (red lines) and recoil atoms of the GaN target (blue-green lines). Each recoil appears as a source of point defect formation events

collision cascade that are point defects. These point defects in GaN are strongly mobile at room temperature [4, 13–16, 21]. As it was suggested in [15, 16], these point defects migrating in the GaN are able to form complexes with doping atoms thus removing donor levels from the band-gap. This leads to a drastic decrease in the free charge carrier concentration, which in turn determines GaN conductivity degradation in the case of MeV light ion bombardment. Swift ions, as follows form Fig. 16.2 and Table 16.1, will generate enough simple point defects to make this mechanism of conductivity degradation important. In a few MeV ion irradiation case, the mostly probable mechanism of resistivity increase is formation of complexes consisting of a doping atom and a simple point defect generated by stopping ion. Thus, the dopant becomes electrically inactive and resistivity of the epitaxial layer increases. Sheet resistance can be expressed as: 1

1

RS ¼ ½ e l NS  ¼ ½ e l h ðnc þ nth Þ ;

ð1Þ

where e is the electron charge, l is charge carrier mobility, Ns is sheet concentration and nc is volume concentration of free carriers, h is epitaxial layer thickness, [e lhnth] is sample resistance after irradiation to a high fluence. In the extreme conditions of low and high irradiation fluences [15, 16] carrier concentration is derived as follows: nd =ni \\1 ð1  nd =ni Þ\\1

nc ¼ ni exp½as g U=ð1 þ a s ni Þ;

nc ¼ ni ð1  nd =ni Þ ¼ ni exp½ a sni  exp½as gU:

ð2Þ ð3Þ

where g is the average density of point defect generation per unit depth; ni is initial dopant concentration; a is constant of dopant-point defect quasi-chemical reaction; s is simple point defect lifetime before its recombination on unsaturated sinks, U is ion fluence. Either at low or at high fluences the dependence ln Rs= f (Ф) would be linear and its slopes will be described by Eqs. (2) and (3). Straight lines reflecting

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Fig. 16.3 Experimental sheet resistance of GaN sample normalized to resistance of virgin sample as a function of 120 MeV Ag and 50 MeV Ni ion fluence (symbols) and result of modeling (lines)

both these irradiation regimes are clearly seen in Fig. 16.1(b) for the 50 MeV Ni and 120 MeV Ag ion irradiation. This let us derive model parameters as and gmodel using experimental data. Figure 16.3 presents the result of calculation of Rs = f (Ф) on the base of our model. It is seen that model and experimental data coincide quite well. Thus, we conclude that point defect formation and related mechanism of conductivity degradation are important in the case of swift ion irradiation. Deviations seen in the Fig. 16.3 can be due to the limitation of the model. Indeed, not only charge carrier concentration but their mobility might depend on ion fluence, which was neglected. However, mobility change will not strongly affect simulated Rs = f(Ф). Sample resistance grows up by 10 orders of magnitude whereas mobility will not drop more than 10 times [15, 20]. Formation of strongly disordered zones within track, neglected in our model, will also affect sheet resistance raising deviations between experimental data and simulated results. But if we consider amorphous zone as intrinsic semiconductor surrounded with n-GaN, electrons will need to overcome barrier to enter this zone. Thus, tracks will not act as a carrier sink. Indeed, damaged clusters appeared in the swift ion track will also have an effect, but at much higher doses. As it was shown by previous experimental studies of electrical isolation in gallium nitride, complexes formed from point defects and impurity atoms are very stable. Samples remain highly resistive after rapid thermal annealing at temperatures up to 900 °C [14]. Stability of a highly resistive state obtained by swift ion irradiation requires additional studies.

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Conclusions

In conclusion, swift ion irradiation induced change of sheet resistance of GaN epilayer has been studied as a function of ion fluence, nuclear and electronic energy loss. Drastic raise of sheet resistance with the fluence increase can be explained by decrease in the concentration of free carriers due to the formation of complexes of ion-beam-generated point defects with shallow donor or acceptor dopants. The model describes well the dependence of sheet resistance on ion dose and the number of ion-beam-generated atomic displacements. The result points out a significant role of nuclear energy loss in ion-beam induced conductivity change since they determine the formation of point defects by a decelerating ion. Electronic energy loss plays a much smaller role in the conductivity degradation despite its much higher value. Thus, degradation of a device performance in radiation harsh environment is mainly due to the formation of point defects due to nuclear collisions happening in the active part of a device. Acknowledgements Authors are grateful to Dr. Wsevolod Lundin (Ioffe Institute, St.-Petersburg, Russia) for providing the GaN samples used in this study.

References 1. T.J. Flack, B.N. Pushpakaran, S.B. Bayne, GaN technology for power electronic applications: a review. J. Electron. Mater. 45, 2673–2682 (2016) 2. Y. Sun, X. Kang, Y. Zheng, J. Lu, X. Tian, K. Wei, H. Wu, W. Wang, X. Liu, G. Zhang, Review of the recent progress on GaN-based vertical power Schottky barrier diodes (SBDs). Electronics 8, 575 (1–15) (2019) 3. J. Wang, P. Mulligan, L. Brillson, L.R. Cao, Review of using gallium nitride for ionizing radiation detection. Appl. Phys. Rev. 2, 031102 (2015) 4. S.J. Pearton, R. Deist, F. Ren, L. Liu, A.Y. Polyakov, J. Kim, Review of radiation damage in GaN-based materials and devices. J. Vac. Sci. Technol. A Vac. Surf. Film. 31, 050801 (1–16) (2013) 5. K. Weide-Zaage, M. Chrzanowska-Jeske, Semiconductor Devices in Harsh Conditions (CRC Press, Boca Raton, 2016) 6. P. Sigmund, Particle Penetration and Radiation Effects (Springer, Heidelberg, 2006) 7. S.O. Kucheyev, J.E. Bradby, C.P. Li, S. Ruffell, T. van Buuren, T.E. Felter, Effects of carbon on ion-implantation induced disorder in GaN. Appl. Phys. Lett. 91, 261905 (2007) 8. A.I. Titov, K.V. Karabeshkin, P.A. Karaseov, A.I. Struchkov, Do chemical effects affect the accumulation of structural damage during the implantation of fluorine ions into GaN? Semiconductors 53(11), 1415–1418 (2019) 9. A.I. Titov, P.A. Karaseov, K.V. Karabeshkin, A.I. Struchkov, The formation of radiation damage in GaN during successive bombardment by light ions of various energies. Vacuum 173, 109149 (2020) 10. A.I. Titov, P.A. Karaseov, K.V. Karabeshkin, G.M. Ermolaeva, V.B. Shilov, Effect of monatomic and molecular ion irradiation on time resolved photoluminescence decay in GaN. Nucl. Instr. Meth. Phys. Res. B 458, 164–168 (2019)

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11. P.A. Karaseov, K.V. Karabeshkin, A.I. Titov, V.B. Shilov, G.M. Ermolaeva, V.G. Maslov, A. O. Orlova, Nonlinear optical effect upon the irradiation of GaN with cluster ions. Semiconductors 48(4), 446–450 (2014) 12. P.A. Karaseov, K.V. Karabeshkin, A.I. Titov et al., Single and molecular ion irradiation-induced effects in GaN: experiment and cumulative MD simulations. J. Phys. D Appl. Phys. 50(50), 505110 (2017) 13. H. Boudinov, S.O. Kucheyev, J.S. Williams, C. Jagdish, G. Li, Electrical isolation of GaN by MeV ion irradiation. Appl. Phys. Lett. 78, 943 (2001) 14. S.O. Kucheyev, H. Boudinov, J.S. Williams, C. Jagdish, G. Li, Effect of irradiation temperature and ion flux on electrical isolation of GaN. J. Appl. Phys. 91, 4117 (2002) 15. A.I. Titov, S.O. Kucheyev, Model for electrical isolation of GaN by light ion bombardment. J. Appl. Phys. 92, 5740 (2002) 16. A.I. Titov, P.A. Karaseov, S.O. Kucheyev, A model of electrical isolation in GaN and ZnO bombarded with light ions. Semiconductors 38, 1179–1186 (2004) 17. D.K. Avasthi, G.K. Mehta, Swift Heavy Ions for Materials Engineering and Nanostructuring (Springer, Heidelberg, 2011) 18. M. Sall, I. Monnet, F. Moisy, C. Grygiel, S. Jublot-Leclerc, S. Della-Negra, M. Toulemonde, E. Balanzat, Track formation in III-N semiconductors irradiated by swift heavy ions and fullerene and re-evaluation of the inelastic thermal spike model. J. Mater. Sci. 50, 5214 (2015) 19. M. Karlušić, R. Kozubek, H. Lebius, B. Ban-d’Etat, R.A. Wilhelm, M. Buljan, Z. Siketić, F. Scholz, T. Meisch, M. Jakšić, S. Bernstorff, M. Schleberger, B. Šantić, Response of GaN to energetic ion irradiation: conditions for ion track formation. J. Phys. D: Appl. Phys. 48, 325304 (1–12) (2015) 20. A. Kumar, D. Kanjilal, V. Kumar, R. Singh, Defect formation in GaN epitaxial layers due to swift heavy ion irradiation. Radiat. Eff. Defects Solids 166, 739–742 (2011) 21. A. Kumar, T. Kumar, A. Hahnel, D. Kanjilal, R. Singh, Dynamics of modification of Ni/ n-GaN Schottky barrier diodes irradiated at low temperature by 200 MeV Ag14 + ions. Appl. Phys. Lett. 104, 033507 (2014) 22. A. Kumar, R. Singh, P. Kumar, U.B. Singh, K. Asokan, A.I. Titov, P.A. Karaseov, D. Kanjilal, In-situ transport and microstructural evolution in GaN Schottky diodes and epilayers exposed to swift heavy ion irradiation. J. Appl. Phys. 123, 161539 (2018) 23. J.F. Ziegler, M.D. Ziegler, J.P. Biersack, SRIM—the stopping and range of ions in matter (2010). Nucl. Instrum. Methods Phys. Res. B 268, 1818–1823 (2010) 24. A.I. Titov, P.A. Karaseov, S.O. Kucheyev, Furthering the understanding of ion-irradiation-induced electrical isolation in wide band-gap semiconductors. Nucl. Instrum. Methods Phys. Res. B 243, 79–82 (2006) 25. J. Nord, K. Nordlund, J. Keinonen, K. Albe, Molecular dynamics study of defect formation in GaN cascades. Nucl. Instrum. Methods Phys. Res. B 202, 93–99 (2003)

Chapter 17

Impact of Chemical Effects on Topography and Thickness of Modified GaN Surface Layers Bombarded by F and Ne Ions Andrei I. Struchkov , Konstantin V. Karabeshkin, Alexander V. Arkhipov , Viktor A. Filatov, Platon A. Karaseov , Alexander Yu. Azarov , and Andrei I. Titov

Abstract The surface topography and thickness of GaN surface layers modified by bombardment by 1.3 keV/amu F and Ne ions at room temperature have been studied in a wide fluence range. The results show that, for low fluences, practically no difference in surface topography was observed for both ion species (F or Ne). However, for higher fluences, a type of the implanted species has a strong effect on both the surface topography and a thickness of the modified layer. In particular, bombardment by F ions leads to an increase of swelling compared to that for Ne ones. The concentration of F atoms in the target after implantation to high fluences is enough to cause partial change in the chemical composition of the material. Thus, the effect observed can be attributed to ion-defect reactions occurring during F ion implantation, which are not present during Ne ion implantation. Keywords GaN Chemical effect


Ion implantation
Damage formation
Surface topography


A. I. Struchkov (&)
A. V. Arkhipov
V. A. Filatov
P. A. Karaseov
A. I. Titov Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia e-mail: [email protected] K. V. Karabeshkin Joint-Stock Company “ELAR”, St.-Petersburg, Russia A. Yu. Azarov Centre for Materials Science and Nanotechnology, University of Oslo, Oslo, Norway © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_17

151

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Introduction

Gallium nitride based devices have reached the market in a number of optoelectronic, high-power electronic and other important applications. Ion beam irradiation is a powerful tool in semiconductor technology. Defect production inevitably accompanies irradiation of a target with ions. Thus, it is of great importance to understand basic processes underlying ion-bean-related damage formation. Ion implantation is a very important tool for the fabrication of number of GaN-based high-power and optoelectronic devices [1, 2]. However, for a successful application of ion implantation, it is necessary to understand and mitigate the processes of radiation damage induced by ion beams. These processes affect not only the crystalline structure of GaN, but its surface topography as well. Ion-implantation-induced amorphization occurs alongside with material decomposition and the formation of N2 bubbles [1, 3–7]. This causes such changes in surface topography as swelling and an increase in the surface roughness [8–13]. It has been shown previously [14] that during the irradiation by F ions up to at least doses of *20 DPA chemical effects did not have a noticeable impact on the accumulation efficiency of the structural damage both in bulk and on the surface of GaN. This dose corresponds to a concentration of *1022 cm−3 of implanted 25 keV fluorine ions. In this work, we compare the effects of fluorine and neon irradiation of GaN epitaxial layers to determine the role of chemical properties of implanted ions in the irradiation-induced changes of surface topography, roughness, nanoparticle formation and increase of thickness of bombarded layers.

17.2

Experimental

Wurtzite (0001) epitaxial GaN layers 2 lm thick, grown at Ioffe Institute by MOVPE technology on c-plane sapphire substrate have been irradiated by fluorine and neon ion beams. The implantation was carried out at room temperature using 500 kV HVEE implanter at 7° off the [0001] direction in order to minimize channeling effects. Ion energy was kept at 1.3 keV/amu (25 and 26 keV for F and Ne, respectively), the flux density was 3.6  10−3 DPA s−1, that is, 3.67  1012 and 3.31  1012 cm−2 s−1 for F and Ne ions, respectively. Doses were normalized to the average number of displacements per atom (DPA). Values of DPA were calculated using TRIM code (version SRIM 2013) [15] with effective threshold energies for atomic displacements of 25 eV for both Ga and N sub-lattices. DPA values are the concentrations of ion-beam-generated lattice vacancies at the

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maximum of the nuclear energy loss profile nv normalized to the atomic concentration of GaN: DPA = nv  Ф/nat (nat = 8.85  1022 atoms  cm−3). The doses of implanted Ne and F ions ranged from 10 to 40 DPA. Surface topography was studied by atomic force microscopy (AFM) in the tapping mode using a Nano-DST instrument manufactured by Pacific Nanotechnology. Step heights between implanted and un-implanted regions of the surface were also measured. These regions were made by masking the GaN surface by a cleaved piece of a silicon wafer that was in mechanical contact with the part of the sample surface during implantation. Surface roughness was measured over 4  4 lm2or 1.5  1.5 lm2 areas located far from the border of the implanted regions to avoid potential effects of the silicon mask sputtering [16].

17.3

Results and Discussion

Figure 17.1 shows AFM images of unimplanted GaN surface and GaN surfaces bombarded with F and Ne ions to doses in range between 10 and 40 DPA (the latter corresponding to ion fluence 4.08  1016 and 3.68  1016 cm−2 for F and Ne ions respectively). It is seen that the virgin sample, as well as samples irradiated by dose of 10 DPA are relatively smooth with distinct atomic height ledges and pinholes of threading dislocations. These dislocations are inevitably formed during the MOVPE growth process of GaN and their concentration isn’t high enough to substantially affect changes in surface topography during ion bombardment. Starting with 20 DPA, we can observe formation of nanoparticles on the surface of GaN. Notably for doses of 30 DPA (not shown) and 40 DPA (see Fig. 17.1) a dramatic difference in surface topography after irradiation by F and Ne ions is revealed. In particular, bombardment by F ions leads to creation of particles of much larger size in comparison to Ne ions. Figure 17.2 shows 3D AFM images of the border between implanted and un-implanted regions for the case of 40 DPA irradiations. There is an increase of thickness of GaN layer (swelling) after irradiation with both F and Ne ions. Slightly more pronounced swelling is seen after F ion implantation. Figure 17.3 demonstrates ion fluence dependencies of surface roughness and step height between masked and irradiated GaN regions for cases of bombardment by both F and Ne ions. For fluences up to 20 DPA both irradiations produce similar results within the experimental error. On the other hand, both surface roughness and step height (which corresponds to thickness increase) are higher in case of fluorine implantation when doses exceed 20 DPA. The heating of any of the samples (beyond a few K) is prevented by low flux densities and does not account for achieved results.

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Virgin

F 20 DPA

F 40 DPA

F 10 DPA

Ne 20 DPA

Ne 40 DPA

Fig. 17.1 AFM images showing topography of unimplanted GaN surface and the GaN surfaces after room-temperature irradiation with 1.3 keV/amu F and Ne ions to doses corresponding to 10, 20 and 40 DPA

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(a)

155

(b)

Fig. 17.2 AFM images showing the border between implanted and masked regions of GaN surface. Irradiation was done at room temperature with an energy of 1.3 keV/amu with a F ions and b Ne ions to fluence corresponding to 40 DPA

Fig. 17.3 Ion dose dependences of a the RMS surface roughness of GaN surfaces irradiated at room temperature and b the step height between implanted and masked regions of the sample surface irradiated with 1.3 keV/amu F and Ne ions

It should be noted that the lack of significant difference found in the dose range of 20 DPA and lower is consistent with the result reported in [14] and indicates that chemical properties of implanted species do not noticeably affect the formation of damage on GaN surface. However, after irradiation to the dose of 40 DPA the maximum concentration of implanted F atoms is * 9.8  1021 cm−3, which is more than 10% of atomic concentration of GaN (8.85  1022 cm−3). Such a high concentration of chemically active fluorine atoms is quite enough to cause a significant impact on the material’s structure and defect formation behavior. The exact material of the nanoparticles formed during the irradiation by F and Ne ions is not known and would require further study. However, it is expected that their structure does have Ga-Ga bonds alongside Ga-N bonds due to phase segregation [17]. As it was mentioned in the Ref. [15], implantation of chemically active impurity ions can enhance damage buildup because of (i) these implanted atoms can trap ion-beam generated mobile point defects, thus forming additional defect complexes

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and clusters and thereby increasing damage. (ii) Such a high concentration of fluorine atoms may cause the formation of a second phase, distortion of lattice around nano-inclusions of this new phase, and disordering of the surrounding GaN. (iii) It is also probable that presence of implanted fluorine atoms with a high concentration may enhance stability of irradiation-induced defects or/and amorphous phase inclusions in GaN due to a change of defect migration and interactions energy barriers. An additional research is required to understand the physical reasons of the above mentioned effects in detail.

17.4

Conclusion

The results presented show that the type of implanted species has a dramatic effect on surface topography of irradiated GaN layers if the fluences are high enough (*30 DPA or higher). For low irradiation fluences no significant difference was found after bombardment by Ne or F ions. The observed effects of increased swelling and surface roughness formation under F ion implantation can be attributed to ion-defect quasi-chemical reactions. Indeed, these reactions are not possible during the implantation by the chemically inert Ne ions. Acknowledgements Work was supported by Russian Foundation for Basic Research grant #18-08-01213.

References 1. S.O. Kucheyev, J.S. Williams, S.J. Pearton, Ion implantation into GaN. Mater. Sci. Eng. R 33, 51–107 (2001) 2. S.J. Pearton, GaN and ZnO-based Materials and Devices (Springer, Heidelberg, 2012) 3. X.F. Li, Z.Q. Chen, C. Liu, H.J. Zhang, A. Kawasuso, Enhanced damage buildup in C+implanted GaN film studied by a monoenergetic positron beam. J. Appl. Phys. 117(8), 085706 (2015) 4. S.O. Kucheyev, J.S. Williams, J. Zou, C. Jagadish, G. Li, Ion-beam-induced dissociation and bubble formation in GaN. Appl. Phys. Lett. 77, 3577 (2000) 5. S.O. Kucheyev, J.S. Williams, C. Jagadish, J. Zou, G. Li, A.I. Titov, Effect of ion species on the accumulation of ion-beam damage in GaN. Phys. Rev. B 64, 035202 (2001) 6. M. Ishimaru, Y. Zhang, X. Wang, W.-K. Chu, W.J. Weber, Experimental evidence of homonuclear bonds in amorphous GaN. J. Appl. Phys. 109, 043512 (2011) 7. M. Katsikini, F. Boscherini, E.C. Paloura, Dose-dependent bonding environment of oxygen implanted in GaN. Nucl. Instrum. Methods Phys. Res. B 268, 241 (2010) 8. K. Pagowska, R. Ratajczak, A. Stonert, A. Turos, L. Nowicki, N. Sathish et al., RBS/ channeling and TEM study of damage buildup in ion bombarded GaN. Acta Phys. Polonica A 120, 153 (2011) 9. S.O. Kucheyev, J.S. Williams, C. Jagadish, V.S.J. Craig, G. Li, Ion-beam-induced porosity of GaN. Appl. Phys. Lett. 77, 1455 (2000)

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10. B. Molnar, S.B. Qadri, S. Schiestel, R.M. Stroud, C.A. Carosella: Surface elevation and strain in ion implanted GaN. Mater. Res. Soc. Symp. Proc. 639, G11.53 (2000) 11. A.I. Titov, P.A. Karaseov, K.V. Karabeshkin, V.S. Belyakov, A.V. Arkhipov, S.O. Kucheyev, Effect of collision cascade density on swelling and surface topography of GaN. Nucl. Instrum. Methods Phys. Res. B 315, 257 (2013) 12. A.I. Titov, P.A. Karaseov, V.S. Belyakov, K.V. Karabeshkin, A.V. Arkhipov, S.O. Kucheyev, A.Yu. Azarov, Molecular effect on surface topography of GaN bombarded with PF4 ions. Vacuum 86(10), 1638 (2012) 13. Y. Gao, C. Lan, J. Xue, S. Yan, Y. Wang, F. Xu et al., Swelling or erosion on the surface of patterned GaN damaged by heavy ion implantation. Nucl. Instrum. Methods Phys. Res. B 268, 3207 (2010) 14. A.I. Titov, K.V. Karabeshkin, P.A. Karaseov, A.I. Struchkov, Do chemical effects affect the accumulation of structural damage during the implantation of fluorine ions into GaN? Semiconductors 53(11), 1415–1418 (2019) 15. J.F. Ziegler, SRIM-2013 software package, http://www.srim.org 16. S. Macko, F. Frost, B. Ziberi, D.F. Forster, T. Michely, Is keV ion-induced pattern formation on Si(001) caused by metal impurities? Nanotechnology 21, 085301 (2010) 17. M. Ishimaru, Y. Zhang, W.J. Weber, Ion-beam-induced chemical disorder in GaN. J. Appl. Phys. 106, 053513 (2009)

Chapter 18

A Symmetrical Design of a Microstrip Tunable Bandpass Filter Victoria Karpova

and Nikita Ivanov

Abstract In this paper an electrically tunable parallel coupled bandpass filter is presented. This type of filter consists of half-wavelength line resonators. The design equations, that are used to find the dimensions of resonators, are given in this paper. Capacitive load is deployed for continuous tuning of center frequency. This paper presents the comparison of two types of electrically tunable systems. They were implemented by connecting capacities to one open end of each resonator or to both open ends. The filters are simulated using Advanced Design System (ADS). Measured S-parameters of filters show, that insertion loss better than 3 dB and reflection coefficient less than −10 dB are obtained. These configurations help to change the center frequency by varying the values of capacitances. The designed filter can change the central frequency in L-band, so it can find its application in different areas. For example, in satellite communications or radionavigation systems, such as GPS. Keywords Bandpass filter

18.1


Microstrip filter
Electronic tuning system
ADS

Introduction

Bandpass filters play an important role in communication systems, as well as in radionavigation systems. [1–7] It can be used as a part of receivers or transmitters. These devices usually operate in ultra-high frequency range (UHF), which covers frequencies from 300 MHz up to 3 GHz. Modern communication systems should be reliable, low loss, low-powered and small-sized. Microstrip filters are suitable for these requirements. Nowadays, there are many types of microstrip filters [8]. The simplest structures are end-coupled and parallel-coupled [9]. Combline, interdigital, hairpin and open-loop [10, 11] filters are commonly used too. In this paper a microstrip parallel-coupled bandpass filter is V. Karpova (&)
N. Ivanov Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_18

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Fig. 18.1 General structure of parallel-coupled bandpass filter

represented. It consists of half-wavelength resonators. They are positioned so that adjacent resonators are parallel to each other along half of their length. The general structure is represented in Fig. 18.1. This type of filter has wider bandwidth than end-coupled filter, because it gives relatively large coupling between resonators. The advantage of parallel-coupled filter is an opportunity to connect the elements with variable parameters. It allows to obtain tunable filter, which can find its application in multi-band wireless communication systems. There are a lot of approaches to making the reconfigurable filters. Ferroelectric materials [8], varactors [12, 13], pin-diodes [14], MEMS [15] and liquid crystals [16] can be used. In this paper an investigation of bandpass filter with tuning frequency is represented. A variable capacitor is used as a tuning element, because it can be easily integrated into a structure.

18.2

Theory and Design Equations

The design of a filter with tunable center frequency is carried out in two stages. Firstly, an ordinary parallel-coupled filter should be implemented. After that an electronic tuning system should be connected to this general structure. In this paper the design of bandpass parallel-coupled filter with center frequency 1.2 GHz is represented. The filter design specifications are shown in Table 18.1. It is necessary to use 7 order Chebyshev filter to meet these requirements. The design of bandpass filter is based on lowpass prototype. Lowpass filter is the basic type of filters design. All types of filters can be obtained by frequency transformation on the basis of low-pass filter. The elements values of a ladder-type lowpass prototype are represented in Table 18.2.

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Table 18.1 Bandpass filter specifications

Specification

Value

Center frequency Bandwidth Insertion loss Attenuation in the stopband Return loss

1200 MHz 70 MHz 40 dB >10 dB

Table 18.2 Low-pass prototype coefficients

Coefficient

g0, g8

g1, g7

g2, g6

g3, g5

g4

Value

1

0.8664

1.227

2.092

1.419

The next step in filter design process is calculation of characteristic admittances Jj,i+1 of J-inverters, which could be obtained using formulas (18.1)–(18.3). J01 ¼ Y0

sffiffiffiffiffiffiffiffiffiffiffiffiffiffi p FBW 2 g0 g 1

Jj;j þ 1 pFBW 1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 2 gj gj þ 1 Y0 Jn;n þ 1 ¼ Y0

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p FBW 2 gn gn þ 1

ð1Þ ð2Þ

ð3Þ

To realize the J-inverters obtained above, the even- and odd-mode characteristic impedances of the coupling microstrip line resonators are determined by (18.4) and (18.5). "  # 1 Jj;j þ 1 Jj;j þ 1 2 ðZ0e Þj;j þ 1 ¼ 1þ þ Y0 Y0 Y0 "  # 1 Jj;j þ 1 Jj;j þ 1 2 ðZ0o Þj;j þ 1 ¼ 1 þ Y0 Y0 Y0

ð4Þ

ð5Þ

The obtained values are represented in Table 18.3 and can be verified using ideal elements CLIN in ADS. The next step of the filter design is to find the dimensions of coupled microstrip lines. They can be obtained using ADS LineCalc. This program computes the parameters of microstrip lines based on even and odd characteristic impedance and takes into account the information about the substrate.

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Table 18.3 Parallel-coupled filter parameters

№ 1, 2, 3, 4,

8 7 6 5

Z0e, Ohm

Z0o, Ohm

72.752 55.3174 53.3142 53.0691

38.7853 45.6225 47.0751 47.2675

The main substrate parameters are dielectric constant er, thickness H and dissipation factor tanD. The investigation shows, that as er increases, the insertion losses increase too and reflection coefficient decreases. However, these changes occur within 1 dB. In addition, the length L of a microstrip line significantly increases. This fact doesn’t allow to use a substrate with very high dielectric constant, especially when designing a high order filter. The changing of thickness H effects insertion losses. The higher H, the less this parameter becomes. As a result of these investigation a substrate Rogers RO 3006 was chosen. It has the following parameters: dielectric constant er = 6.15 (6.5 for design), dissipation factor tanD = 0.0020, thickness H = 2.02 mm. The dimensions of coupled lines are shown in Table 18.4. Figures 18.2 and 18.3 represent the schematic circuit and simulation results of bandpass filter. The obtained filter meets the requirements, shown in Table 18.1. The signal attenuation in a passband is due to conductor loss and dielectric loss of the substrate material.

18.3

Tunable Filter Design and Simulation

As previously described parallel-coupled filter can be tuned by capacitive load, which changes the electrical length of resonators. A reverse-biased varactor diode can be used as a tuning element. In this paper two types of electrically tunable systems are represented and compared. The first way to change the center frequency is to connect variable capacitors to each open end of microstrip line, as shown in Fig. 18.4. Figure 18.5 illustrates the simulated frequency response for capacities varying from 0.2 to 0.97 pF. The simulation result exhibits an insertion loss and return loss better than 3 dB and 10 dB respectively with 15% tuning range. The value of the microstrip filter input power without external elements depends on the properties of Table 18.4 Physical dimensions of coupled lines

№ 1, 2, 3, 4,

8 7 6 5

W, mm

S, mm

L, mm

2.162 2.737 2.766 2.768

0.648 3.022 4.297 4.537

29.602 28.897 28.990 28.985

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Fig. 18.2 Bandpass filter based on real elements

Fig. 18.3 S-parameters of bandpass filter based on real elements

the substrate and its thickness and can reach kilowatts. The power of variable capacitors usually doesn’t exceed 10 W. The filter size is 118.32  42.67 mm2. The additional elements don’t affect the dimensions of the filter. The other configuration of tunable filter is shown in Fig. 18.6. Here the capacitive load is connected only to one open end of each microstrip line [17]. The simulation results show, that this structure has smaller tuning range compared to previous one (Fig. 18.7).

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Fig. 18.4 Microstrip tunable filter (type 1)

Fig. 18.5 S-parameters of microstrip tunable filter

Capacitance vs. frequency plot for two different types of electronic tuning systems is represented in Fig. 18.8. The capacitors connected to each open end of microstrip line (type 1) have a greater impact on the center frequency than the other type of electronic tuning system (type 2).

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A Symmetrical Design of a Microstrip Tunable Bandpass Filter

Fig. 18.6 Microstrip tunable filter (type 2)

Fig. 18.7 S-parameters of microstrip tunable filter

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Fig. 18.8 Frequency dependence of capacitance graph

18.4

Conclusion

In this paper an electrically reconfigurable microstrip parallel-coupled bandpass filter has been represented. The tunability of the device was obtained by capacitive load added to one or two open ends of each microstrip line. EM simulated results show that with the capacitance increasing the center frequency decreases. When capacitors are connected to each open end of the microstrip line the frequency changes in the range from 1157 to 1400 MHz. For the other type of the filter, when capacitive load is connected to one open end, the tuning range is smaller (from 1390 to 1448 MHz). Insertion loss, attenuation in the bandstop and return loss are better than 3 dB, 40 dB and 10 dB respectively for the both filter types. The simulation was done by ADS software. The implementation of the tunable filter will be a part of a future investigation. It is planned to compare the simulated S-parameters with experimental results.

References 1. D.B. Akhmetov, A.S. Korotkov, I.A. Rumyancev, 2.4–2.5 GHz fractional-n frequency synthesizer with integrated VCO in 0.18 um CMOS for RFID systems, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), St. Petersburg (IEEE, 2018), pp. 64–68 2. I.A. Rumyancev, A.S. Korotkov, Survey on beamforming techniques and integrated circuits for 5G systems, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), St. Petersburg (IEEE, 2019), pp. 76–80 3. M.A. Ivanov, A.A. Podyacheva, I.A. Rumyancev, Vector modulator for 5G transceivers in 65 nm CMOS, in IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), St. Petersburg (IEEE, 2020), pp. 130–133 4. D.B. Akhmetov, A.S. Korotkov, D.V. Morozov, M.M. Pilipko, I.A. Rumyancev, Radio frequency identification system of internet of things based on CMOS integrated circuits, in IEEE East-West Design & Test Symposium (EWDTS), Novi Sad (IEEE, 2017), pp. 1–3

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5. V.D. Kuptsov, Noise factor optimization of surface acoustic wave filters, in IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO), Seville (IEEE, 2017), pp. 76–78 6. A. Kalyonov, D. Morozov, V. Repin, A. Mukhin, Research and development of a differential microwave attenuator based on BiCMOS SiGe technology with continuous attenuation control, in IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow (IEEE 2019), pp. 1620–1623 7. R. Shabardin, N. Shabardina, I. Mukhin, D. Morozov, L. Nedashkovskiy, The development of quadrature modulators and demodulators 1800 MHz–6 GHz with digital correction of parameters, in IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow (IEEE, 2019), pp. 1612–1615 8. J.-S. Hong, M.J. Lancaster, Microstrip Filters for RF/Microwave Applications (Wiley, Hoboken, 2001) 9. N.V. Ivanov, K.V. Mikhailov, Multilayer microstrip bandpass filter design using galvanic coupling, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), St. Petersburg (IEEE, 2019), pp. 66–68 10. N.V. Ivanov, A new approach to microstrip coupled-resonator bandpass filter design, in IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow (IEEE, 2018), pp. 201–203 11. N.V. Ivanov, A.S. Korotkov, S-band microstrip bandpass filter design based on new approach to coupling coefficients calculation, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), St. Petersburg (IEEE, 2018), pp. 60–63 12. B.-W. Kim, S.-W. Yun, Varactor-tuned combline bandpass filter using step-impedance microstrip lines. IEEE Trans. Microw. Theory Tech. 52(4), 1279–1283 (2004) 13. A.R. Brown, G.M. Rebeiz, A varactor-tuned RF filter. IEEE Trans. Microw. Theory Tech. 48 (7), 1157–1160 (2000) 14. F. Lin, M. Rais-Zadeh, A tunable 0.6 GHz–1.7 GHz bandpass filter with a constant bandwidth using switchable varactor-tuned resonators, in IEEE MTT-S International Microwave Symposium, Phoenix (IEEE, 2015), pp. 1–4 15. A. Ocera, P. Farinelli, P. Mezzanotte, R. Sorrentino, B. Margesin, F. Giacomozzi, A novel MEMS-tunable hairpin line filter on silicon substrate, in European Microwave Conference, Manchester (IEEE, 2006), pp. 803–806 16. M. Shen, Y. Huang, Z. Shao, A tunable microstrip dual-mode bandpass filter based on liquid crystal technology, in IEEE International Conference on Communication Problem-Solving (ICCP), Guilin (IEEE, 2015), pp. 464–466 17. S.V. Kaveri, Design of Tunable Edge Coupled Microstrip Bandpass Filter. Dissertation, Logan (2008)

Chapter 19

Implementation of Moshinsky Atom Model for Electron Gas in Quantum Dots Mher A. Mkrtchyan, David B. Hayrapetyan, Eduard M. Kazaryan, Hayk A. Sarkisyan, Dmitry A. Firsov, and Maxim Y. Vinnichenko Abstract In this paper, the behavior of the few-electron gas localized in a strongly prolate ellipsoidal quantum dot has been investigated. It is shown that, due to the specific geometry of the quantum dot, the motion of particles in the considered system can be separated into fast and slow subsystems. “Moshinsky atom” model has been chosen as interacting potential. Analytical expressions for energy spectra and wavefunctions have been calculated. The dependences of the energy spectra of the few-particle system on the radii of the semiaxes of a strongly prolate ellipsoidal quantum dot have been counted. Keywords Ellipsoidal quantum dot approximation

19.1


Electron gas
Moshinsky atom
Adiabatic

Introduction

The investigation of few-particle states in quantum dots (QDs) has academic and applied significances [1–3]. QDs are unique objects on which basis many fundamental principles of quantum physics of few-particle systems can be tested, and on the other hand, these promising structures can play the role of functional base for the semiconductor devices of new generation [3]. The problem of describing Coulomb interaction in QDs is already becoming much more complicated. In some cases, when the impurity center is localized in the center of the QD, the problem can be solved analytically [4]. Problems connected with the behavior of few- and many-particle systems in QD with a parabolic confining potential attracted attention of specialists after generalization of Kohn theorem [5–7] to the case of parabolic QD. In the paper [4] it was M. A. Mkrtchyan (&)
D. B. Hayrapetyan
E. M. Kazaryan
H. A. Sarkisyan Russian–Armenian University, Yerevan, Armenia e-mail: [email protected] D. A. Firsov
M. Y. Vinnichenko Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_19

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shown that center of mass and relative motions of electrons are separated due to the parabolic form of the confining potential, and as a result, the frequency of resonant absorption of long-wave radiation does not depend on the number of electrons. On the other hand, there are examples of exactly solvable many-particle systems localized in a parabolic well with pair-interaction between particles. One of these models is the Moshinsky atom [8–11]. Initially, this model was proposed to describe the behavior of nucleons in nuclei. Later, Johnson and Payne showed that in the case of a two-dimensional parabolic QD [12]. As stated above QDs with the strongly oblate or prolate ellipsoidal or lens-shaped geometries allow the use of adiabatic approximation. Moreover, the confining potential of the slow subsystem is parabolic. If we assume that the QD has the geometry of a strongly prolate ellipsoid, then in the direction of the axis of rotation (z-axis) the pair-interacting gas will be localized in a parabolic well [13, 14].

19.2

Theory

Let us consider an electron gas localized in a strongly prolate ellipsoidal QD. Confinement potential of QD we choose in the following form [15, 16] ( ^conf ð~ V rÞ ¼

0; 1;

x2 þ y2 a2 x2 þ y2 a2

þ þ

z2 c2 z2 c2

1 ; a  c; [1

ð1Þ

where a and c are the small and large semiaxes of the strongly prolate ellipsoidal QD, respectively. Since in the xy-directions the size-quantization energy is substantially larger than the electron-electron interaction, we take into account the interparticle interaction only in the z-direction. The wavefunction can be represented in the form rN Þ ¼ /ð~ q1 ðz1 Þ; . . .;~ qN ðzN ÞÞ
wðz1 ; . . .; zN Þ; Uð~ r1 ; . . .;~

ð2Þ

N    Q f ~ qj zj are one particle wavefunctions j¼1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi in 2D infinitely cylindrical quantum well with diameter dq ¼ 2a 1  z2 =c2 , and this wavefunctions have the following analytic form

q2 ðz2 Þ; . . .;~ q N ðzN ÞÞ ¼ where /ð~ q1 ðz1 Þ;~

  f ð~ qðzÞÞ ¼ Cnq ;jmj eimu Jm jnq ;m qðzÞ ;

ð3Þ

where nq ; m is radial and magnetic quantum numbers, Cnq ;jmj is normalization qffiffiffiffiffiffiffiffiffi 2lEnq , Enq is the energy of one-particle states in two-dimensional constant, jnq ;m ¼ h2 cylindrical quantum well.

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171

After subsisting (19.3) in Schrodinger equation it can be shown that electron gas is localized in 2D parabolic potential [16] Vconf ðzÞ ¼

lX2 z2 ; 2

ð4Þ

ha 

1;0 , a1;0 is the zero of the first-kind J0 ðjÞ Bessel function. where X ¼ p1ffiffi2 lac For pair-interacting N-particle system in this approximation we have following Schrodinger equation

( ) N N N X   h2 X @2w lX2 X 2    þ z þ t zi  zj w ¼ ðE  NE0 Þw ¼ eðN Þw; ð5Þ 2l i¼1 @z2i 2 i¼1 i i\j where E0 is the ground state of Enq . We chose “Moshinsky atom” [12] as interacting potential model so we can write N N  X X   2 t zi  zj  ¼ c zi  zj ; i\j

ð6Þ

i\j

where c is interaction parameter. After substitution (19.6) in Schrödinger equation and dimensionless it we obtain ( ) N N N  X 2 1X @2w 1X 2  þ n þg n i  nj w ¼ W ðN Þw; 2 i¼1 @n2i 2 i¼1 i i\j

ð7Þ

c 0 where n ¼ pz ffiffiffihffi ; W ðN Þ ¼ ENE hX ; g ¼ lX2 : lX

In order to solve Eq. (19.7), we transform the original coordinates into Jacobi coordinates in the following form [10, 17]: n þ...þn Z ¼ 1 pffiffiffiffi N ; Zi ¼ N

! rffiffiffiffiffiffiffiffiffiffi i1 i1 1 X n ; i ¼ 2; 3; . . .; N: ni  i i  1 k¼1 k

ð8Þ

By using such transformations, we can separate (19.7) in two independent parts (

)  X N  1 @2 Z2 1 @2 1 ~2 2  þ  þ X Zi w ¼ W ðN Þw; þ 2 @Z 2 2 @Zi2 2 2 i¼2

ð9Þ

~ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi where X 1 þ 2Ng. The first part of (19.9) describes the center of mass motion of the system and second part describes the relative one. So for energy spectrum of the system he have

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   N  X 1 1 ~ Encm ;fnrel g ¼ NE0 þ hX ncm þ nreli þ þ hXX ; i 2 2 i¼2

ð10Þ

where fncm ; nreli g are quantum numbers for center of mass and relative motion. Also we can derive the exact wavefunction (coordinate part) of the system !1=4  1=4 N pffiffiffiffi XZ 2 Y ~ 2 1 1 1 X Z2  2i ~ Zi : ffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p wðZ; Zi Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi e H ð Z Þ e H X n n cm rel i 2ncm ncm ! p 2nreli nreli ! p i¼2

ð11Þ

19.3

Results

Let us discuss of the obtained results. As it was mentioned above, the case of strongly prolate ellipsoidal QD made of InAs have been considered. The material parameters which have been used in the calculations are the following: l ¼ 0:023m0 , where m0 is the free electron mass, er ¼ 14:4, aB ¼ 30 nm is Bohr radius. In Figs. 19.1 and 19.2 the schematic view of the energy diagram for different geometrical parameters are presented. As can be seen from figures the center of mass and relative motion levels form zone structure. Difference between energy levels for two- and three- particle cases are changes for relative motion but don’t change for the center of mass motion. So by manipulating the a and c we can control not only the energy levels but also the interlevel distance (Fig. 19.3). Such analyze of the energy diagram allows to find the optimal parameters in order to obtain the most suitable intervals for enhancement of optical properties in future.

Fig. 19.1 Energy diagram for two-particle case for the following values: I. a ¼ 0:5aB ; c ¼ 2:5aB , II. a ¼ aB ; c ¼ 5aB

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Fig. 19.2 Energy diagram for three-particle case for the following values: I. a ¼ 0:5aB ; c ¼ 2:5aB , II. a ¼ aB ; c ¼ 5aB

Fig. 19.3 Dependences of the energies on the radii of semiaxes aðcÞ for the center of mass and relative (insert) motions for two-particle case

In Fig. 19.3, the dependencies of energy from QD semiaxes are presented. With the increase of QD semiaxes, the difference between energy decrease in center of mass motion case and almost unchanged in relative one. As can be seen from Fig. 19.4, the energy increase in both caseswith an increase of interaction parameter c, but in the center of mass motion case interlevel distance remains unchanged with interaction and increases in relative motion case.

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Fig. 19.4 Dependences of energies on interaction parameter c for the centre of mass and relative (insert) motions for two-particle case

19.4

Conclusion

In the present article, the behavior of few-electron gas localized in a strongly prolate ellipsoidal quantum dot has been studied. The interparticle interaction is considered only in axial direction and the Moshinsky model approximation has been used. According to this model, particles interact with each other according to the oscillatory law and the interaction energy is proportional to the square of the distance between the particles. In pair-interacting electron gas case, a change in the geometric parameters of QDs has a tiny effect on the energy states of the relative motion, while in the non-interacting gas case the relative motion energy reacts very sharply to the changes in the geometric parameters of the QD. Acknowledgements Dmitry Firsov and Maxim Vinnichenko are grateful for the support of the Ministry of Science and Higher Education of the Russian Federation (state assignment).

References 1. D. Bimberg, M. Grundmann, N.N. Ledentsov, Quantum Dot Heterostructures (Wiley, Hoboken, 1998) 2. P. Harrison, A. Valavanis, Quantum Wells, Wires and Dots: Theoretical and Computational Physics of Semiconductor Nanostructures (Wiley, Hoboken, 2016) 3. T. Chakraborty, Quantum Dots: A Survey of the Properties of Artificial Atoms (Elsevier, Amsterdam, 1996) 4. D.S. Chuu, C.M. Hsiao, W.N. Mei, Hydrogenic impurity states in quantum dots and quantum wires. Phys. Rev. B 46(7), 3898 (1992) 5. P. Maksym, T. Chakraborty, Quantum dots in a magnetic field: role of electron-electron interactions. Phys. Rev. Lett. 65(1), 108 (1990) 6. F.M. Peeters, Magneto-optics in parabolic quantum dots. Phys. Rev. B 42(2), 1486 (1990) 7. A.O. Govorov, A.V. Chaplik, Magnetoabsorption at Quantum Points. JETP Lett. 52(1), 31– 33 (1990)

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8. M. Moshinsky, How good is the Hartree-Fock approximation. Am. J. Phys. 36, 52–53 (1968) 9. P. Kościk, A. Okopińska, Correlation effects in the Moshinsky model. Few-Body Syst. 54 (7-10), 1637–1640 (2013) 10. H.T. Peng, Y.K. Ho, Statistical correlations of the N-particle Moshinsky model. Entropy 17 (4), 1882–1895 (2015) 11. R.J. Yaüez, A.R. Plastino, J.S. Dehesa, Quantum entanglement in a soluble two-electron model atom. Eur. Phys. J. D 56(1), 141 (2010) 12. N. Johnson, M. Payne, Exactly solvable model of interacting particles in a quantum dot. Phys. Rev. Lett. 67(9), 1157 (1991) 13. H.T. Ghaltaghchyan, D.B. Hayrapetyan, E.M. Kazaryan, H.A. Sarkisyan, Few-body magneto-absorption in prolate ellipsoidal quantum dot. Phys. Atomic Nuclei 80, 769–773 (2017) 14. H.T. Ghaltaghchyan, D.B. Hayrapetyan, E.M. Kazaryan, H.A. Sarkisyan, Few-body absorption in prolate ellipsoidal quantum dot. J. Phys. Conf. Ser. 673, 5 (2016) 15. H.A. Sarkisyan, D.B. Hayrapetyan, L.S. Petrosyan, E.M. Kazaryan, A.N. Sofronov, R.M. Balagula, D.A. Firsov, L.E. Vorobjev, A.A. Tonkikh, Realization of the Kohn’s theorem in Ge/Si quantum dots with hole gas: theory and experiment. Nanomaterials 9(1), 56 (2019) 16. J.A. Vinasco, A. Radu, A. Tiutiunnyk, R.L. Restrepo, D. Laroze, E. Feddi, C.A. Duque, Revisiting the adiabatic approximation for bound states calculation in axisymmetric and asymmetrical quantum structures. Superlattices Microstruct. 138, 106384 (2020) 17. P. Bouvrie, A. Majtey, A. Plastino, P. Sánchez-Moreno, L. Dehesa, Quantum entanglement in exactly soluble atomic models: the Moshinsky model with three electrons, and with two electrons in a uniform magnetic field. Eur. Phys. J. D 66(1), 15 (2012)

Chapter 20

Characterization of Nitride Silicon Layers Sin:x Enriched in Silicon at Different Stoichiometries by Photocurrent Spectroscopy Method and Mass Spectrometry of Secondary Ions Linda Boudjemila Abstract The main objective of researches in the field of photovoltaic is to increase the efficiency. This work aims a new concept which is the integration of nanoparticles in the anti-reflection layer SiNx (silicon nitride), in order to improve the performance of solar cells. To measure the chemical composition of silicon nitride (anti reflection layer), analyzes were carried out by Mass spectrometry of secondary ions (SIMS) and also by Photocurrent spectroscopy. The principle consists in obtaining silicon nanoparticles (Si-np) in a SiNx matrix at 7 different stoichiometry. The variation of the ratio of gases R = NH3/SiH4 al-lows to vary the concentration of silicon therefore the size of the nanoparticles due to the photo-current technique we determined the absorption levels in nc-Si using two different configurations (vertical and lateral). We have proved that a lateral transport configuration has better sensitivity than a vertical configuration and that it provides more information on the different absorption thresholds due to a very considerable reduction in the contribution of the substrate. Furthermore, the results of current photo spectroscopy have shown us the potential of nc-Si for applications in the photovoltaic field with a view to improving the spectral response in the visible range of silicon solar cells through absorption. The performance of optics has increased on the high energy range. From the photocurrent analysis, the ratio R = 3 has the highest current which agrees with the results observed by the I-V characterization. Nanocrystals are seen as a challenge for all researchers and engineers to enable nanomaterial.










Keywords Solar energy Photovoltaic application Photocurrent Photocurrent spectroscopy Infrared range Nanoparticles Nitride silicon si3n4













L. Boudjemila (&) University of Science and Technology Houari Boumedien Bab Ezzouar, 16111 Algiers, Algeria e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_20

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Anti-reflection layer enriched in silicon SiNx:H Deposition of SiNx by PECVD Nanocrystals SIMS




20.1




Introduction

The solar energy is one of the inexhaustible sources of energy on the Earth [1–9]. The electric energy is necessary for a person to conduct various experiments and studies [9–22]. It should be noted that the production of electric energy from solar is a safer process for the environment than others [4, 5, 7, 8, 11, 23–27]. The using of the solar panels allows to provide the autonomous operation of many research systems in space, in opening sea and other places [1, 2, 28–33]. In this case, the conversion of solar energy into electrical energy is very important to ensure the battery capacity (energy reserve), especially in the case of information transfer from an autonomous object [28, 29, 31, 33, 34]. Therefore, the researchers are focusing particularly of the coefficient increase of the conversion of solar energy to electric by using photovoltaics. The silicon is currently the most used material in photovoltaic systems. The semiconductor technology has been developed rapidly with the growth of microelectronics in recent decades. 90% silicon is the main material of microelectronics [35]. Photovoltaic’s third generation is the combination between the first generation (silicon cells), which were developed mainly from silicon, these cells have only one PN junction, and the second generation (thin films), which is based on a thin layer deposited on a substrate. In recent years, the reduction in the size of Silicon to nanostructure has open new functionalities that have made it possible to envisage several applications based on silicon nanocrystals (nc-Si). Composite thin films of a dielectric matrix containing silicon nanoparticles can find various applications in optoelectronics: third-generation photocells, Bragg mirrors, semiconductor lasers, etc. [36]. At this point, wide changes in physical properties occur, mainly in energy characteristics; electronic levels, modes of atomic vibrations and their interactions with photons. This promises a lot of results and progress. One of the goals set is to use these nanosturturs as converters of high-energy photons into red photons that are better absorbed by the main cell. The main attention will be paid to the analysis of the properties of absorption and charge transfer of nanostructured layers in order to verify their photoelectric efficiency and assess the feasibility of manufacturing multi-junction elements based on these nanomaterials.

20

Characterization of Nitride Silicon Layers Sin:x Enriched ...

20.2

179

Methods of Experimental Characterizations

20.2.1 Determination of the Composition of the Anti-reflection Layer To measure the chemical composition of silicon nitride (anti reflection layer), analyzes were carried out by Mass spectrometry of secondary ions (SIMS). The SIMS technique allows obtaining the concentrations of each of the components in the atomic percentage of samples, which also allows to find out the total composition of these samples depending on the depth. The evolution of stoichiometry, which depend of the gas precursor ratio R = NH3/SiH4, will be determined after the bombardment of the samples by an ion beam (primary ions) with energies from 0.1 keV to 50 keV.

20.2.2 Measurement of Diffuse Reflection and Determination of Energy Gap The main purpose of measuring reflectivity is to determine by scanning the solar spectrum the response of the sample by evaluating the absorption properties of Silicon nanoparticles depending on the stoichiometry of the SiNx layer. In this case, the wavelength range is well defined by the visible and near infrared range [37–40]. Obviously, this step is carried out immediately after applying a layer of silicon nitride with different stoichiometry.

20.2.3 Measurement of Photocurrent in Vertical and Lateral Configuration Photocurrent spectroscopy is a characterization technique that allows knowing details on the electronic states and the absorption spectrum of the material analyzed. The principle of this technique is based on the generation of carriers caused by the absorption of photons with energy equal or superior to material’s gap. Depending on the wavelength, the photo-generated carriers will be able to occupy the different energy levels of the semiconductor. In the case of nc-Si, this technique is particularly interesting since it makes it possible to explore and determine the absorption threshold (and indirectly the gap energy Eg) associated with the size distribution of the crystallites. Two types of measurement were carried out, measurement in vertical configuration to measure the entire cell current, and measurement in lateral configuration to measure photocurrents Figs. 20.1 and 20.2.

180

L. Boudjemila

Fig. 20.1 Lateral configuration

Fig. 20.2 Vertical transport configuration

20.3

Results and Discussions

Interest will be focused only on the SiNx anti-reflective layer of the samples. It consists of two essential elements, silicon Si and nitride N. According to these results, it is possible to develop alloys of silicon nitride which contains an excess of silicon compared to the standard stoichiometry Si3N4. It is an essential prerequisite for obtaining the precipitation and crystallization of silicon Nanocrystals in a matrix of silicon nitride. According to the obtained graphs, the more samples are rich in silicon, the more we notice a shift in the reflection spectrum to the near infrared, diagrams in Fig. 20.3. This suggests that the effective refractive index increases with the rate of Silicon within the SiNx layer [37]; we also notice that the fringes are not regularly spaced, which is due to the different concentration of silicon of the samples. To measure the photocurrent (PC) in our structures, we first used a vertical transport configuration in which the lighting is done through a contact grid and the photogenic carriers are collected between the contact grid and the substrate Fig. 20.4. We found that the photocurrent is dominated by the signal from the substrate. The contribution of the nitride layer does not appear in the vertical configuration since the photocurrent associated with the silicon nanoparticles risk being easily masked by the substrate. For this reason, measurements under lateral configuration are necessary. Although we have used a lateral configuration, the absorption of the substrate is still visible on our spectra. Indeed, the absorption threshold around 1.1 eV has been observed in our structures and is generally attributed to the absorption linked to the gap of crystalline silicon. Note that the SiNx layer has a low thickness ( > < 0; 02 þ 02  1 a c ; a0 [[ c0 ; V ðq0 ; u; z0 Þ ¼ 02 02 > q z > : 1; 02 þ 02 [ 1 a c

ð22:2Þ

where a0 and c0 are large and small semiaxes, respectively. In order to solve the equation, we will use the adiabatic approximation which, according to the geometry of our system, can provide the solution with quite high accuracy [26–28]. Following the technique of adiabatic method, the system is possible to present in the form of two subsystems: “fast” and “slow” ^ ¼H ^f þ H ^ s þ V ðq; u; zÞ: H

ð22:3Þ

To facilitate the process, we performed the following designations and moved to 3  ^ ¼ H^ 0 , q ¼ q0 , z ¼ z0 , f ¼ 2eFa2B m , ER ¼ h2 2 —effective dimensionless quantities: H ER

aB

aB

h

2m aB

22

Linear and Nonlinear Optical Properties …

195

2

Rydberg energy, aB ¼ mkhe2 —effective Bohr radius, k is dielectric permittivity. Now the Hamiltonians of subsystems can be presented as ^ f ¼  @  fz; H @z2
2 : 2 ^s ¼  @ þ 1 @ þ 1 @ H @q2 q @q q2 @u2 2

ð22:4Þ

Solving both “fast” and “slow” parts of Schrodinger equation and then combining the solutions we get the following results for the wave function and the charge carrier energy [26, 29] jmj bn q2  w ¼ Ceimu e 2 bn q2 2 ½C1 AiðnÞ þ C2 BiðnÞ
 jmj þ 1 e  an 2  ; jmj þ 1; bn q ;  1 F1 2 4bn   e ¼ an þ 2bn 2nq þ jmj þ 1 ;

ð22:5Þ

where C, C1 and C2 are normalization constants, an and bn are some constants depending on the electric field and geometrical parameters of SOEQD, n, nq and m are axial, radial and magnetic quantum numbers respectively, AiðnÞ and BiðnÞ are Airy functions, n ¼ f 1=3 z  f 2=3 ef ðqÞ, ef ¼ an þ b2n q2 is the electron energy in “fast” subsystem and 1 F1 ða; b; zÞ is a confluent hypergeometric function of the first kind.

22.3

Linear and Nonlinear Optical Properties

The optical (interlevel) transition of a charge carrier in low-dimensional quantum mechanical systems defines the optical absorption process. The transition occurs from an initial state i to a final state f by absorbing a photon. The analytical forms of the linear and the third-order nonlinear optical absorption coefficients and RI changes at a temperature of absolute zero are computed using the density matrix formalism [9, 10, 12, 25]  2 rffiffiffiffiffi rhCfi Mfi  l a ðxÞ ¼ x  2   eR Efi  hx 2 þ  hCfi ð1Þ

ð22:6Þ

196

G. Ohanyan

 4 4hCfi Mfi  r 2  2 i 2 Efi  hx þ  hCfi  1 0   h2 x2  C2fi Mff  Mii 2 3Efi2  4hxEfi þ  A  @1   2  2 Efi2 þ  hCfi 4Mfi 

rffiffiffi
 l I a ðx; IÞ ¼  x h e 2e0 nr c  ð3Þ

 2   rMfi  Efi   hx Dnð1Þ ðxÞ 1 ¼ 2   2  nr 2nr e0 Efi  hx 2 þ  hCfi  4 lcIrMfi  Dnð1Þ ðxÞ ¼ h n3r e0 nr

ð22:7Þ

ð22:8Þ



 Efi   hx 2 i 2 2  Efi   hx þ  hCfi 8 "  2  # 9 ; < 2  2 2Efi      Mff  Mii hx =   1   2  hx   hCfi   hCfi   2 Efi Efi   : hx ; Efi   4Mfi  Efi2 þ  hCfi

ð22:9Þ

 where Mfi ¼ e wi jzjwf is the electric dipole moment of the transition between the states i and f , c is the speed of light in vacuum, r is the electron density in the SOEQD (one electron per QD), I is the intensity of the incident electromagnetic field, l is the permeability of the system, nr is the relative refractive index of semiconductor, e0 is the dielectric constant of the vacuum,  hx is the incident photon energy and Cfi ¼ 1 sfi is the non-diagonal matrix element known as relaxation rate of final and initial states, sfi is the relaxation time.

22.4

Results and Discussion

In the current work, the analytical calculations are performed for a typical GaAs SOEQD. The following parameters have been used: m ¼ 0:067m0 , e ¼ 13:18, _ ER ¼ 5:275 meV, a ¼ 5aB , nr ¼ 3:2, Cfi ¼ 0:2 ps1 , r ¼ 3  1022 m3 , aB ¼ 104 A, c ¼ 0:5aB . The effect of the electric field on the absorption coefficient is depicted in Fig. 22.1. The dependence of the linear, third-order nonlinear and the total absorption coefficients were confined in the SOEQD as a function of the incident photon energy for three different values of external electric field. According to the figure, the absorption coefficients experience the blue shift which is explained by the rise of transition energy as a consequence of growth of the electric field [10, 26, 29]. One more important feature which should be mentioned here is a decrease of peaks of linear and nonlinear absorption coefficients with F. This result is expected since, according to Eqs. 22.6 and 22.7, the absorption coefficients depend on the

22

Linear and Nonlinear Optical Properties …

197

Fig. 22.1 Linear, nonlinear and the total absorption coefficient vs photon energy for three different F values

Fig. 22.2 Total absorption coefficient vs photon energy for four different I values

dipole matrix element which commonly falls under electric field [10, 22, 25]. However, the total absorption coefficient experiences the increase due to the fact that the nonlinear part decreases faster than linear one. In Fig. 22.2 the total absorption coefficient demonstrated depending on photon energy for different values of optical intensity. Since the nonlinear part of absorption coefficient strongly depends on the optical intensity the growth of I

198 Table 22.1 The saturation level of optical intensity in case of different sizes of SOEQD and F ¼ 10 kV=cm

G. Ohanyan aðaB Þ

cðaB Þ

IS ðMW=m2 Þ

5 5 5 5 5

0.4 0.5 0.6 0.7 0.8

17 11 8 6 5

Fig. 22.3 Total RI change as a function of photon energy for three different values of F

brings to the decline of the total absorption coefficient. The interesting here is the intensity value at which the nonlinear absorption coefficient starts to prevail over the linear part. From the figure we can see that saturation is taking place approximately at I ¼ 10 MW=m2 . In Table 22.1 the saturation value of light intensity IS is presented for different sizes of SOEQD. It is clear that Is declines with increase of semi-minor axis of SOEQD. Figure 22.3 shows the variation of linear, nonlinear and the total RI changes as a function of incident photon energy in case of different values of external electric field. Here we can see that increase of electric field does not affect the peaks of RI changes considerably [22, 25]. However, they experience a blue shift which can be explained similarly as in case of absorption coefficients. The total RI changes versus the photon energy and incident light intensity are presented in Fig. 22.4. Since only the nonlinear part of RI change depends on the optical intensity, total RI change decreases with I and in case of I ¼ 30 MW=m2 the nonlinear RI change even prevails over linear part and two additional peaks appear.

22

Linear and Nonlinear Optical Properties …

199

Fig. 22.4 Total RI change as a function of photon energy for four different values of I

22.5

Conclusion

To summarize, the linear and nonlinear optical properties have been analytically calculated using the adiabatic approximation and the matrix diagonalization method for GaAs SOEQD under the influence of an external electric field. The obtained results indicate that the electric field causes blue shifts of the peak positions of the absorption coefficients and RI changes. The increase of the optical intensity registered the decrease of the total absorption coefficient and RI change. Finally, the correlation of light intensity saturation value and semi-minor axis has been revealed and showed that saturation of the total absorption coefficient is taking place at lower values of optical intensity with an increase of semi-minor axis of SOEQD.

References 1. D.A. Baghdasaryan, D.B. Hayrapetyan, E.M. Kazaryan, Oblate spheroidal quantum dot: electronic states, direct interband light absorption and pressure dependence. Eur. Phys. J. B 88 (9), 223 (2015) 2. D.B. Hayrapetyan, Y.Y. Bleyan, D.A. Baghdasaryan, H.A. Sarkisyan, S. Baskoutas, E.M. Kazaryan, Biexciton, negative and positive trions in strongly oblate ellipsoidal quantum dot. Physica E Low Dimen. Syst. Nanostruct. 105, 47–55 (2019) 3. D.B. Hayrapetyan, E.M. Kazaryan, H.A. Sarkisyan, Implementation of Kohn’s theorem for the ellipsoidal quantum dot in the presence of external magnetic field. Physica E Low Dimen. Syst. Nanostruct. 75, 353–357 (2016) 4. D.B. Hayrapetyan, E.M. Kazaryan, H.A. Sarkisyan, On the possibility of implementation of Kohn’s theorem in the case of ellipsoidal quantum dots. J. Contemp. Phys. 48(1), 32–36 (2013)

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G. Ohanyan

5. G.L. Ohanyan, Effect of external hydrostatic pressure and temperature on the impurity states and diamagnetic susceptibility in strongly oblate ellipsoidal quantum dot. J. Contemp. Phys. (Armenian Acad. Sci.), 54(2), 160–167 (2019) 6. G. Wang, K. Guo, Excitonic effects on the third-harmonic generation in parabolic quantum dots. J. Phys. Condens. Matter 13(35), 8197 (2001) 7. S. Sauvage, P. Boucaud, F.H. Julien, J.M. Gérard, V. Thierry-Mieg, Intraband absorption in n-doped InAs/GaAs quantum dots. Appl. Phys. Lett. 71(19), 2785–2787 (1997) 8. V. Halonen, P. Pietiläinen, T. Chakraborty, Optical-absorption spectra of quantum dots and rings with a repulsive scattering centre. EPL (Europhys. Lett.) 33(5), 377 (1996) 9. G. Rezaei, Z. Mousazadeh, B. Vaseghi, Nonlinear optical properties of a two-dimensional elliptic quantum dot. Physica E Low Dimen. Syst. Nanostruct. 42(5), 1477–1481 (2010) 10. S. Baskoutas, C. Garoufalis, A.F. Terzis, Linear and nonlinear optical absorption coefficients in inverse parabolic quantum wells under static external electric field. Eur. Phys. J. B 84(2), 241–247 (2011) 11. D.A. Miller, Quantum well optoelectronic switching devices. Int. J. High Speed Electron. Syst. 1(01), 19–46 (1990) 12. L. Shi, Z.W. Yan, Electric field and shape effect on the linear and nonlinear optical properties of multi-shell ellipsoidal quantum dots. Superlattices Microstruct. 94, 204–214 (2016) 13. D.J. Mowbray, M.S. Skolnick, New physics and devices based on self-assembled semiconductor quantum dots. J. Phys. D Appl. Phys. 38(13), 2059 (2005) 14. H.S. Chen, C.K. Hsu, H.Y. Hong, InGaN-CdSe-ZnSe quantum dots white LEDs. IEEE Photonics Technol. Lett. 18(1), 193–195 (2005) 15. M.F. Frasco, N. Chaniotakis, Semiconductor quantum dots in chemical sensors and biosensors. Sensors 9(9), 7266–7286 (2009) 16. G. Rezaei, M.R. Vahdani, B. Vaseghi, Nonlinear optical properties of a hydrogenic impurity in an ellipsoidal finite potential quantum dot. Curr. Appl. Phys. 11(2), 176–181 (2011) 17. J. Yuan, W. Xie, L. He, An off-center donor and nonlinear absorption spectra of spherical quantum dots. Physica E Low Dimen. Syst. Nanostruct. 41(5), 779–785 (2009) 18. M. El Haouari, A. Talbi, E. Feddi, H. El Ghazi, A. Oukerroum, F. Dujardin, Linear and nonlinear optical properties of a single dopant in strained AlAs/GaAs spherical core/shell quantum dots. Opt. Commun. 383, 231–237 (2017) 19. Z. Zeng, C.S. Garoufalis, A.F. Terzis, S. Baskoutas, Linear and nonlinear optical properties of ZnO/ZnS and ZnS/ZnO core shell quantum dots: effects of shell thickness, impurity, and dielectric environment. J. Appl. Phys. 114(2), 023510 (2013) 20. G. Liu, K. Guo, C. Wang, Linear and nonlinear intersubband optical absorption in a disk-shaped quantum dot with a parabolic potential plus an inverse squared potential in a static magnetic field. Physica B 407(12), 2334–2339 (2012) 21. D.A. Baghdasaryan, E.S. Hakobyan, D.B. Hayrapetyan, H.A. Sarkisyan, E.M. Kazaryan, Nonlinear optical properties of cylindrical quantum dot with kratzer confining potential. J. Contemp. Phys. (Armenian Acad. Sci.) 54(1), 46–56 (2019) 22. G. Rezaei, S.S. Kish, Linear and nonlinear optical properties of a hydrogenic impurity confined in a two-dimensional quantum dot: effects of hydrostatic pressure, external electric and magnetic fields. Superlattices Microstruct. 53, 99–112 (2013) 23. E.S. Hakobyan, Nonlinear optical properties of cylindrical quantum dot with Kratzer confining potential in the presence of axial homogeneous electric field. J Phys. Conf. Ser. 1326(1), 012008 (2019). IOP Publishing 24. S. Baskoutas, E. Paspalakis, A.F. Terzis, Electronic structure and nonlinear optical rectification in a quantum dot: effects of impurities and external electric field. J. Phys. Condens. Matter 19(39), 395024 (2007) 25. İ. Karabulut, S. Baskoutas, Linear and nonlinear optical absorption coefficients and refractive index changes in spherical quantum dots: effects of impurities, electric field, size, and optical intensity. J. Appl. Phys. 103(7), 073512 (2008)

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26. K.G. Dvoyan, D.B. Hayrapetyan, E.M. Kazaryan, A.A. Tshantshapanyan, Electron states and light absorption in strongly oblate and strongly prolate ellipsoidal quantum dots in presence of electrical and magnetic fields. Nanoscale Res. Lett. 2(12), 601 (2007) 27. D.B. Hayrapetyan, G.L. Ohanyan, D.A. Baghdasaryan, H.A. Sarkisyan, S. Baskoutas, E.M. Kazaryan, Binding energy and photoionization cross-section of hydrogen-like donor impurity in strongly oblate ellipsoidal quantum dot. Physica E Low Dimen. Syst. Nanostruct. 95, 27– 31 (2018) 28. D.B. Hayrapetyan, K.G. Dvoyan, E.M. Kazaryan, Direct interband light absorption in a strongly oblated ellipsoidal quantum dot. J. Contemp. Phys. (Armenian Acad. Sci.) 42(4), 151–157 (2007) 29. M. Matsuura, T. Kamizato, Subbands and excitons in a quantum well in an electric field. Phys. Rev. B 33(12), 8385 (1986)

Chapter 23

Research on Transition Between Substrate Integrated Waveguide and Microstrip Line Chen’ Yu

Abstract Substrate integrated waveguide (SIW) was a new microwave transmission structure which appeared ten years ago, it consists of two rows of metal holes tightly arranged, making the electromagnetic wave propagation limited in the area of SIW. The SIW structure combines the high quality factor of the rectangular waveguide with the advantages of the microstrip structure such as easy integration, easy processing, small size and low cost. It has now been widely used in microwave and millimeter wave circuits. In microwave millimeter wave system, the circuit is not completely made up of SIW structure, but of a large amount of microstrip lines or other forms of circuits. In order to facilitate the integration of SIW and microstrip structure, it is necessary to design a transformation structure to realize the transition between SIW and microstrip line. Therefore, the conversion between SIW and microstrip line is the key to the application of SIW and millimeter wave circuits. In order to improve the transmission efficiency between substrate integrated waveguide and microstrip line, this paper describes the basic principle and structure of substrate integrated waveguide and microstrip line transition, through the analysis of the impedance matching and software HFSS simulation, discusses the SIW— microstrip line direct transition structure, type structure and convex concave type transition structure, and studies the influence of parameter change on the transmission performance, finally designs the rounded corners and chamfering two gradient structure. The simulation results after optimization showed that in the range of 5.95–6.67 GHz, the insertion loss is −0.3–−0.6 dB, the return loss is less than −20 dB, and the bandwidth is 689 MHz, achieving good transmission performance, which can be widely used in the design and testing of SIW devices. Keywords Substrate integrated waveguide (SIW) HFSS


Microstrip
Transition


C. Yu (&) The Nanjing Forestry University, Nanjing 10298, China e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_23

203

204

23.1

C. Yu

Introduction

Currently, in the radar, radio direction-finding and mobile communication systems, there is a decrease of the transmitting and receiving SHF devices for use in moving objects [1–9]. In radar, feeder paths [9–11] and fiber optic communication lines [12–15] they are replaced by integrated waveguides. This allows to reduce the number of elements in the radar station (FOCL), which needs temperature stabilization [16–20]. At the same time, the shielding structure is reduced to protect the transmitted SHF signal from interference [21–24]. Substrate integrated waveguide is a new type of waveguide [25–28]. This type of waveguide can be widely used in microwave and millimeter wave paths in radars, radio direction-finding and mobile communication systems [3, 6, 16–18, 29]. An important characteristic of this kind of waveguide is that its transmission characteristics are as good as those of rectangular waveguides, FOCL and feeder paths. Many problems arise during the using of transition in the SHF signal transmission system. The main problem is the transition between substrate integrated waveguide and the coplanar transmission line. The transition quality affects transmission performance, insertion loss and return loss [26, 29]. The increase in losses due to weak signals can lead to incorrect processing of information. In my article, the study of this transition and the selection of the optimal waveguide design are considered.

23.2

Design of Integrated Waveguide

The basic structure of the substrate integrated waveguide is composed of two parallel rows of metal through holes vertically embedded on the PCB dielectric substrate, and a metal surface with copper on the PCB. If the distance between each row of metalized vias can be adjusted properly, then, when the electromagnetic wave propagates in the SIW, most of the electromagnetic energy generated is limited to the space between the metal walls, and does not leak from the gap of the metal through hole. The upper and lower metal surfaces of the dielectric substrate are equivalent to the wide walls of a rectangular waveguide. Two parallel rows of metal vias are equivalent to the narrow wall of the rectangular waveguide. In this way, it can be analyzed as a rectangular waveguide. The three-dimensional schematic diagram of the substrate integrated waveguide is presented in Fig. 23.1. The microstrip line is a kind of transmission line commonly used in microwave integrated circuits. Because the quasi-TEM mode can be transmitted in the microstrip line and the TE mode is transmitted in the integrated waveguide of the substrate, a low-loss connection between them becomes possible. The structure diagram of the microstrip line is shown in Fig. 23.2.

23

Research on Transition Between SIW and Microstrip Line …

205

Fig. 23.1 The 3-dimensional schematic diagram of substrate integrated waveguide

Fig. 23.2 The schematic diagram of microstrip line structure

In order to realize the transmission between these two structures, the impedance matching must be done first [30]. Therefore, the characteristic impedance of the microstrip line Z0 must be calculated first. The signal loss is minimal when the microstrip line and SIW are impedance matched. It is established that the value of Z0 depends on frequency. This will allow me to calculate the future transition.

23.3

Results Simulation and Discussion

The key to realize the impedance matching of the substrate integrated waveguide to the microstrip line is to adjust the size of the SIW structure, make the equivalent substrate integrated waveguide impedance change slowly with frequency. In addition, the thickness of the SIW, the dielectric constant of the dielectric substrate, the pitch and diameter of the through holes have a large influence on the transmission performance of the SIW and the microstrip line. Therefore, it is important to select the appropriate SIW size according to the given design goals [31, 32]. In this simulation design, the dielectric substrate layer is selected as Rogers RT/ duroid 5880 tm, dielectric constant er = 2.2, thickness h = 1 mm, the diameter of metal via d = 0.6 mm, the center distance between adjacent vias p = 1.1 mm, and the size of the SIW resonant cavity is a  LSIW = 24  90.44 mm, transition line length l = 12.1 mm. To optimize the design, the high-frequency simulation software Ansoft HFSS is used as shown in Fig. 23.3 (graphs for comparing return loss parameters (S11) of direct transition, concave transition and convex transition).

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C. Yu

Fig. 23.3 The S11 parameter comparison chart

S11:direct transition S11:concave transition S11:convex transition

Fig. 23.4 The SIW simulation results

I found that the transmission performance of the convex transition structure is much better than that of the other two structures. Therefore, the convex conversion structure will be further optimized and improved to obtain a better solution. After optimizing the transition structure between the substrate integrated waveguide and the microstrip line, the final result is shown in Fig. 23.4. It can be seen from Fig. 23.4 that the cavity produces a total of three transmission poles. The return loss, that is, the reflection coefficient is less than −15 dB in the range of 5.95–6.69 GHz. In the range of 5.98–6.67 GHz, the return loss is less than −20 dB, and the bandwidth is about 689 MHz. At 6.04 GHz, the minimum reflection coefficient is −33.5 dB, and the insertion loss is −0.3–−0.6 dB.

23

Research on Transition Between SIW and Microstrip Line …

23.4

207

Conclusion

The transition between substrate integrated waveguide and microstrip line is an important prerequisite for testing and using SIW devices. The performed analysis of the impedance matching between SIW and microstrip line, and simulation allowed me to develop the two graded transition structures. The influence of the main parameters of the transport index are determined. The simulation results have shown that the designed structure has greater flexibility and practical value.

References 1. A.S. Podstrigaev, A.V. Smolyakov, V.V. Davydov, N.S. Myazin, M.G. Slobodyan, Features of the development of transceivers for information and communication systems considering the distribution of radar operating frequencies in the frequency range. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11118 (2018), pp. 509–515 2. V. Fadeenko, I. Fadeenko, V. Davydov, V. Reznik, V. Kruglov, A. Moroz, N. Popovskiy, V. Dudkin, D. Nikolaev, Remote environmental monitoring in the area of a nuclear power plant. IOP Conf. Ser. Earth Environ. Sci. 390(1), 012022 (2019) 3. A.S. Podstrigaev, A.V. Smolyakov, V.V. Davydov, N.M., Grebenikova, R.V. Davydov, New method for determining the probability of signals overlapping for the estimation of the stability of the radio monitoring systems in a complex signal environment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11660 (2019), pp. 525–533 4. A.A. Petrov, V.V. Davydov, D.V. Zalyotov, V.E. Shabanov, D.V. Shapovalov, Features of direct digital synthesis applications for microwave excitation signal formation in quantum frequency standard on the atoms of cesium. Journal Physics: Conference Series 1124(1), 041004 (2018) 5. G.L. Klimchitskaya, V.M. Mostepanenko, E.K. Nepomnyashchaya, E.N. Velichko, Impact of magnetic nanoparticles on the Casimir pressure in three-layer systems. Phys. Rev. B 99(4), 045433 (2019) 6. A.A. Petrov, V.V. Davydov, N.M. Grebenikova, Some directions of quantum frequency standard modernization for telecommunication systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11118 (2018), pp. 641–648 7. N.A. Lukashev, A.V. Moroz, V.V. Davydov, Compact microwave frequency standard on Hg-199 ions for navigation systems. J. Phys: Conf. Ser. 1236(1), 012068 (2019) 8. A.V. Moroz, V.V. Davydov, K.Y. Malanin, A.A. Krasnov, Development of a compensation system based on horn antennas for an active phased antenna array, in Proceedings of the 2019 Antennas Design and Measurement International Conference (ADMInC-2019), Saint-Petersburg, vol. 8969090 (2019), pp. 114–116 9. E. Savchenko, E. Velichko, New techniques for measuring zeta-potential of colloidal system, in Saratov Fall Meeting 2018: Optical and Nano-Technologies for Biology and Medicine. – International Society for Optics and Photonics, vol. 11065 (2019). p. 110651U 10. V.A. Lenets, M.Yu. Tarasenko, V.V. Davydov, N.S. Rodygina, A.V. Moroz, New method for testing of antenna phased array in X frequency range. J. Phys. Conf. Ser. 1038(1), 012037 (2018)

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11. A.A. Moroz, R.V. Davydov, V.V. Vavydov, A new scheme for transmitting heterodyne signals based on a fiber-optical transmission system for receiving antenna devices of radar stations and communication systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11660 (2019), pp. 710–718 12. V.B. Fadeenko, G.A. Pchelkin, V.V. Davydov, V.Yu. Rud, Features of the transmission of microwave signals in the range of 8-12 GHz in the maritime radar station over fiber-optic communication line. J. Phys. Conf. Ser. 1400(4), 044010 (2019) 13. S.E. Logunov, V.Yu. Rud, R.V. Davydov, A.V. Moroz, K.J. Smirnov, Optical method for studying the magnetic field structure. J. Phys. Conf. Ser. 1326(1), 012024 (2019) 14. A.V. Moroz, V.V. Davydov, Fiber-optical system for transmitting heterodyne signals in active phased antenna arrays of radar stations. J. Phys. Conf. Ser. 1368(2), 022024 (2019) 15. V.B. Fadeenko, V.A. Kuts, D.A. Vasiliev, V.V. Davydov, New design of fiber-optic communication line for the transmission of microwave signals in the X-band. J. Phys. Conf. Ser. 1135(1), 012053 (2018) 16. A.S. Podstrigaev, R.V. Davydov, V.Yu. Rud’, V.V. Davydov, Features of transmission of intermediate frequency signals over fiber-optical communication system in radar station. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11118 (2018), pp. 624–630 17. E.A. Sinicyna, A.A. Galichina, A.S. Lukiyanov, A.S. Podstrigaev, V.V. Davydov, A study of temperature dependence of phase shift in optoelectronic path of direction finder channels. J. Phys. Conf. Ser. 1236(1), 012075 (2019) 18. V. Davydov, V. Fadeenko, I. Fadeenko, N. Popovskiy, V. Rud, Multifunctional method for remote monitoring of the environment in the area of nuclear facilities, in E3S Web of Conferences, vol. 140 (2019), p. 07006 19. A.S. Podstrigaev, A.S. Lukiyanov, A.V. Smolyakov, V.V. Davydov, A.P. Glinuchkin, E.A. Sinicyna, The research of temperature instability influence of fiber optic communication line in phase direction finder channels on peleng accuracy. J. Phys. Conf. Ser. 1410(1), 012155 (2019) 20. N.S. Myazin, V.I. Dudkin, N.M. Grebenikova, V.V. Davydov, V.Y. Rud’, A.S. Podstrigaev, Fiber – optical system for governance and control of work for nuclear power stations of low power. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11660 (2019), pp. 744–756 21. N.A. Lukashev, A.A. Petrov, V.V. Davydov, N.M. Grebenikova, A.P. Valov, Improving performance of quantum frequency standard with laser pumping, in 18th International conference of Laser Optics 2018 (ICLO 2018), Saint-Petersburg, vol. 8435889 (2018), p. 271 22. N.M. Grebenikova, V.V. Davydov, N.A. Lukashev, A.P. Valov, Decrease of the frequency shift of the central resonance of the caesium-133 quantum frequency standard, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech 2019), Saint-Petersburg, vol. 8906867 (2019), pp. 17–20 23. G.A. Pchelkin, V.B. Fadeenko, V.V. Davydov, Features of the transmission of microwave signals at offshore facilities. J. Phys. Conf. Ser. 1368(2), 022045 (2019) 24. A.A. Petrov, V.V. Davydov, N.M. Grebennikova, On the potential application of direct digital synthesis in the development of frequency synthesizers for quantum frequency standards. J. Commun. Technol. Electron. 63(11), 1281–1285 (2018) 25. K. Wu, F. Boone, Guided-wave properties of synthesized nonradiative dielectric waveguide for substrate integrated circuits (SICs), in IEEE MTT-S International Microwave Symposium Digest (Phoenix, USA) (2001), pp. 723–726 26. H. Li, W. Hong, Substrate integrated waveguide based on LTCC, in IEEE MTT S International Microwave Symposium Digest (Philadelphia, USA), (2003), pp. 2045–2048 27. D. Deslandes, K. Wu, Integrated microstrip and rectangular waveguide in Planar form. IEEE Microwave Wirel. Compon. Lett. 11(2), 67–70 (2001) 28. Y. Cassivi, K. Wu, Hybrid planar NRD-guide magic-tee junction. IEEE Trans Microwave Theory Tech. (MTT-50(10)) (2002), pp. 2405–2408

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29. N.M. Grebenikova, V.V. Davydov, A.V. Moroz, M.S. Bylina, M.S. Kuzmin, Remote control of the quality and safety of the production of liquid products with using fiber-optic communication lines of the Internet. IOP Conf. Ser. Mater. Sci. Eng. 497, 012109 (2019) 30. J. Hirokawa, M. Ando, Single—layer feed waveguide consisting of Posts for Plane TEM— wave excitation in Parallel Plates. IEEE Trans. Antennas Propagmion 46(5), 625–630 (1998) 31. Z.C. Hao, W. Hong, J.X. Chen, K. Wu, Planar diplexer for microwave integrated circuits, in IEEE Proc- Microw.Antennas Propag., vol. 152, no. 6 (2005), pp. 456–464 32. H. Uehimura, T. Takeenoshita, M. Fujii, Development of a “Laminated Waveguide”. IEEE Trans. Microwave Theory Tech. 46(12), 2438–2443 (1998)

Part II

Photonics and Optical Information

Chapter 24

Visible Light Communication System with Changing Lighting Color Daniil S. Shiryaev , Olga A. Kozyreva , Ivan S. Polukhin , Aleksey I. Borodkin , Maksim A. Odnoblyudov , and Vladislav E. Bougrov Abstract Visible light communication (VLC) is an emerging solution in the wireless connectivity, which is utilized for more personalized and multi-purpose wireless communication systems in complementation to radio-frequency (RF) based one. We developed LED-based transmitter module with an option of illumination color tuning, a reciprocal (photodetector) module and set a duplex wireless link using developed modules. We conducted a computer simulation of the microwave matching and bias circuits for LEDs, fabricated a test printed circuit board (PCB) and measured the scattering matrix parameters of this PCB. Based on the developed PCB and obtained experimental results the final layout of transmitter module was designed and fabricated. The illuminator of transmitter module consisted of 16 RGBW LEDs, matched in bandwidth up to 170 MHz, in order to maintain data speed transfer as high as 170 Mbit/s. The variation of illumination color was realized via DALI protocol. We tested the system with both natural and artificial light as noise source. We conducted the experiment of data transmission via developed and fabricated VLC system and managed to achieve the EVM value of 9%. Pseudo random binary sequence (PRBS) was used as data package and various modulation schemes were applied: QPSK, p/4-QPSK, APSK16, QAM16. Finally, we connected the transmitting module directly to the Global Internet and reciprocal module to the user PC and managed to achieve download and upload data speed up to 60 Mbit/s at up to 4 m distance between the modules.




Keywords Visible light communication Optical wireless communication Li-Fi RGB LED Wireless communication










D. S. Shiryaev (&)
O. A. Kozyreva
I. S. Polukhin
A. I. Borodkin
M. A. Odnoblyudov
V. E. Bougrov ITMO University, Kronverksky Pr. 49, Saint Petersburg 197101, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_24

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Introduction

In recent years wireless network data traffic has increased significantly, due to the vast growth of the devices number using the access to the Internet, thus the complexity of wireless networks has increased, and the available bandwidth has decreased. In 2011 Li-Fi data transmission technology was proposed in order to solve the limitation of radio frequency range [1, 2]. Li-Fi technology allows to utilize LED lighting for wireless data transmission [3, 4], via the matching impedances of the high-frequency path. However, in practice, the bandwidth of white phosphor LEDs becomes limited by the inertia of phosphor [5, 6]. Utilizing three monochromatic light-emitting diodes: red, green and blue (RGB), is another method of obtaining the white luminous flux. In this case, the passband of LED becomes limited only by design of semiconductor chip. Also, RGB LEDs implementation allows us to use the wavelength division multiplexing, thus increasing the data rate [7, 8]. In this case, a filter system is needed on the photodetector module to select the wavelength. We used only the red chip of RGB LED for data transmission, taking into account the higher photodetector sensitivity in this spectral range, and as we did not set a goal to maximize the data transmission speed.

24.2

Development of VLC Transmitter Circuit

One of the aspects in developing VLC transmitter for hybrid Li-Fi and RF networks is compatibility of Li-Fi and RF devices, in particular, at the physical level of data transmission, which is the impedance matching. For efficient transmission of a high-frequency signal from the generator to the load, it is necessary to match the impedances of the load (in our case, VLC transmitter) and the high-frequency path. Otherwise, most of power will be reflected from the load, which will reduce the efficiency of the transmission line. The standard RF transmission line is usually of 50 X impedance, thus both the single RGBW LED and the whole VLC transmitter should be matched to 50 X line. At relatively low frequencies (up to 1 GHz), the matching of the load impedances and the high-frequency line can be achieved using lumped reactive elements. The quality of matching circuits in this case might be estimated by the Scattering parameters (S-matrix), namely, by the S11 parameter, which is the reflection coefficient of the circuit. To solve the problem of matching impedances, it was necessary to obtain the input impedance of the LED. A special circuit board consisting of three microstrip lines was developed, the first one is a microstrip line with an input and output SMA connector, the second is a microstrip line with two SMA connectors, but connected to the bias supply circuit, and the third is a strip line with one SMA connector

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connected to both the bias circuit and LED. We used the Rohde & Schwarz ZVA 40 vector network analyzer to measure S-matrix of circuit board and the AWR Microwave Office software to de-embed the S-parameters of the line with the bias circuit from the line with the LED circuit to exclude its influence on the measurement of the S-parameters of the LED, as well as the influence of the transmission line and SMA connector. The input impedance of the LED is shown in Fig. 24.1a in the Smith diagram. With a rather large difference in the values of matching impedances, a two element L-shaped circuit, which is an L-C (L-inductance, C-capacitance) or C-L circuit, is usually used. However, these circuits allow matching loads only in a rather narrow frequency range, since these matching circuits are resonant. For broadband matching, more complex, three element (T- or P-shaped) circuits are used. These two variants of the schemes practically do not differ from each other, since they are two consecutive L-circuits, therefore, usually the choice between them is based on which scheme has more convenient component ratings. Since the transmitting module will be connected to the Li-Fi system for transmitting data according to visible light, the operating frequency of the module was determined from the parameters of the modulating signal. The information signal is transmitted at a carrier frequency of 40 MHz with a bandwidth to 20 MHz. Therefore, the LED must be matched in a band of at least 30–50 MHz. Figure 24.1b shows a graph of the signal reflection coefficient from load versus frequency. A P-shaped matching circuit was simulated, which allows achieving a reflection coefficient S11 of the order of −25 dB in the entire working frequency band from 30 to 50 MHz. To measure the real reflection coefficient, a printed circuit board with a matching circuit built in the model was developed and fabricated. Measurements were also performed on a ZVA40 vector network analyzer. The simulated dependence of the reflection coefficient on the frequency practically correlates satisfactory with the measured values (Fig. 24.1b). The electrical circuit of the matching circuit is shown in Fig. 24.2.

a)

b)

Fig. 24.1 a Input impedance of the red LED chip; b Frequency dependence of reflection coefficient S11

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Fig. 24.2 A P-shaped matching circuit

The Fig. 24.2 shows a matching circuit consisting of: P-shaped circuit— L1 = 820 nH, C1 = 300 pF, C2 = 10 pF; high-frequency matching transformer TC9-1-75+; capacitor C3 = 240 pF. To calculate the nominal values of the elements of the P-shaped circuit, this one was presented as two L-shaped circuits, for each of which the nominal values of the elements were calculated. First, the equivalent impedance is calculated, to which both L-shaped circuits are matched [9].  Req ¼ R1 ðQ2 þ 1Þ;

ð24:1Þ

Req is the equivalent impedance; R1 is the impedance of the transmission line; Q is the quality factor of the matching scheme. Then, the impedances of the elements of the first L-shaped circuit were calculated. X30 ¼ Req
Q1 ; X1 ¼ R1 =Q1 ;

ð24:2Þ

X1 and X3 are the impedances of the elements. Similarly, the impedances of the elements of the second L-shaped circuit were calculated, after which they are added, and in accordance with the sign, the values of the elements are calculated. T- and P-shaped circuits are most effective in matching relatively close impedances, and since the LED has a rather low active part of impedance, a transformer is added to circuit. Otherwise, it would be necessary to increase the number of circuits for matching in the working frequency band. Capacitor C3 is used to compensate the inductance of the transformer windings.

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As a photodetector, the Thorlabs FDS100 photodiode was chosen, which has a fairly high speed, the rise and fall time of the signal is about 10 ns. In order to determine the required number of LEDs in the transmitting module, the thermal noise of the photodiode was calculated, since this type of noise is predominant. I 2 ¼ 4kB TB=R;

ð24:3Þ

I is the noise current of the photodiode; kB is the Boltzmann constant; T is the absolute temperature; B—bandwidth; R is the total equivalent impedance. Thus, the current caused by thermal noise was 2 nA. And the photocurrent from one LED at a distance of 2.5 m was 144 nA, which gives a signal-to-noise ratio SNR = 18 dB. Despite the fact that the figure seems quite high, this signal-to-noise ratio is not enough, given the fact that the signal with digital modulation is transmitted via the analogue communication channel, as well as the noise of the subsequent amplifier stage and demodulator. Therefore, for digital data transmission systems, the minimum SNR is about 25–30 dB. Using 16 LEDs at a distance of 2.5 m will create a photocurrent in the photodetector equal to approximately 2.3 lA, then the SNR was 31 dB, which is already enough for the developed communication system. The photocurrent of photodiodes was measured directly with a Keithley 6485 pico-ampermeter, when illuminated with light from one LED and from sixteen LEDs in a dark room.

Fig. 24.3 Part of the board topology after the power divider

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To transmit data through 16 LEDs, it is necessary that the signal comes to all LEDs in phase. For this, the Mini-Circuits SCA-4-10+ power splitter is used, and in order to have enough signal power, the Mini-Circuits PHA-101+ monolithic amplifier is used. Also, for the in-phase supply of the signal to the LEDs, it is necessary that the length of the transmission lines be the same. Figure 24.3 shows the topology of the board in the segment from the power splitter to the LEDs. In Fig. 24.3, the red lines show the transmission lines leading from the power splitter SP2 to the four RGBW LEDs and their matching circuits. The transmission line is made in microstrip design [10] with a conductor width of 0.7 mm, a conductor thickness of 35 lm and a dielectric thickness of 0.315 mm, which will make up a high-frequency path with a wave impedance of 50 X. Further, for the developed transmission scheme, a printed circuit board was designed and fabricated. Further, we invistigated the average value of the error vector magnitude (EVM) during data transmission between fabricated transmitter and receiver.

24.3

Description of Experiment

To estimate the quality of the communication channel the following measurement schematic was used: the Keysight M8195A arbitrary waveform generator was used, which was connected to the developed transmitting module, and the real-time oscilloscope Keysight UXR0204A operating in the vector spectrum analyzer mode, which is connected to the matrix of 4 Thorlabs FDS100 photodetectors. Figure 24.4 shows the measurement of the amplitude of the EVM or in the communication channel for visible light. A signal with the selected modulation scheme is applied from the generator, at a carrier frequency of 40 MHz with a band of 10 MHz, the pseudorandom binary sequence PRBS 27 − 1 is used as the transmitted information. Having passed through the free space, the signal was depicted in the oscilloscope, where, using the vector analysis program, a constellation diagram was built and the rms value of the EVM is determined. Fig. 24.4 The scheme of measurements of the EVM

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Results and Discussion

As a result, such modulation schemes as QPSK, p/4-QPSK, APSK16, QAM16 were determined optimal in terms of EVM. Figure 24.5 shows their signal constellations. By the type of signal constellations in this case, it is possible to determine the type of the noise affecting the system [11]. The developed system is more affected by additive white Gaussian noise (AWGN), which causes a symmetrical scatter of the diagram points around the true values. The integral value of the impacting noise depends on its spectral power density. Therefore, to reduce the integral value of the noise, it is possible to reduce the frequency bandwidth of the signal, but increase the positioning of the modulation, that is, increase the number of bits per symbol so as not to lose speed [12]. Also, measurements were made of the quality of the data transmission channel during a change in the color of illumination, and for phase manipulations, the average value of EVM was about 19%, and for quadrature amplitude modulation QAM16, the value of EVM was about 10%. We think that the decrease in the quality of data transmission in the communication channel via visible light when using phase-shift modulation schemes is associated with the physical nature of the photodetector. Photodiodes detecting the total intensity of the light incident on them, so when the phase of the signal changes, its intensity changes slightly, almost at the noise level, because of which the receiver does not distinguish this change as receiving a signal. And when the amplitude of the signal changes, the photodiodes clearly detect a change in the total intensity of the light incident on them. Therefore, althogh the use of phase-shift modulation schemes is quite possible for developed circuits, still the amplitude digital modulations showed lower EVM [13]. We also measured the speed of the Internet connection by connecting the developed transmitting module to the Wi-Fi modem and using the frequency down-shift circuit to obtain necessary carrier frequency. We used circuits described in our previous works [14, 15], but tested the developed RGB-LED matrix instead of phosphorus LED one.

a)

b)

c)

d)

Fig. 24.5 Constellation diagrams for signals: a QPSK, EVM = 13%; b p/4-QPSK, EVM = 16%; c APSK16, EVM = 12%; d QAM16, EVM = 8%

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The measured speed of the Internet connection, organized on the basis of a transmitting module with 16 RGBW LEDs, was about 65 Mbit/s downlink and about 25 Mbit/s uplink at a distance between the transmitter and receiver of about 2 m. At a distance of 4 m, an Internet connection speed of 42 Mbit/s on the downlink and 7.65 Mbit/s on the uplink was obtained. So, for example, to watch streaming video with a resolution of 1440 p (2 k) with a frame rate of 60 frames per second, an Internet connection speed of 24 Mbit/s is required, which is almost two times lower than the data rate of the developed communication system in visible light.

24.5

Conclusion

The scheme was developed for matching the impedances of the LED and the high-frequency line for efficiently supplying a high-frequency information signal with a reflection coefficient S11 to −25 dB in the frequency bandwidth from 30 to 50 MHz. The transmitting module based on 16 RGBW LEDs was developed, the illumination color of which can be changed using the DALI protocol when the corresponding controller is connected to the board of the transmitting module and to a personal computer. The type of the dominant noise affecting the optical communication channel is also determined and a method for reducing its impact is proposed. It is shown that digital amplitude modulations are more preferable for developed system than phase shift-keying. Illumination color variation does not affect the communication channel when transmitting QAM signal. As a result, the real speed of the Internet connection as high as 60 Mbit/s at the distances up to 4 m was obtained via the developed and fabricated system. Acknowledgements The results were achieved during the implementation of the State Support Program of the LRC, in the Leading Research Center “National Center of the Quantum Internet” of ITMO University, Grant No. 0000000007119P190002.

References 1. H. Haas et al., What is lifi? J. Lightwave Technol. 34(6), 1533–1544 (2015) 2. H. Haas, High-speed wireless networking using visible light. SPIE Newsroom 1(1) (2013) 3. S. Rajbhandari et al., A review of gallium nitride LEDs for multi-gigabit-per-second visible light data communications. Semicond. Sci. Technol. 32(2), 023001 (2017) 4. S.I. Ivanov, A.P. Lavrov, I.I. Saenko, Main characteristics study of analog fiber-optic links with direct and external modulation in transmitter modules, in 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (2018), pp. 264–267 5. G. Cossu et al., 3.4 Gbit/s visible optical wireless transmission based on RGB LED. Opt. Express 20(26), B501–B506 (2012) 6. S.A. Blokhin et al., High-speed semiconductor vertical-cavity surface-emitting lasers for optical data-transmission systems. Tech. Phys. Lett. 44(1), 1–16 (2018)

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7. Y. Wang et al., 4.5-Gb/s RGB-LED based WDM visible light communication system employing CAP modulation and RLS based adaptive equalization. Opt. Express 23(10), 13626–13633 (2015) 8. D. Kiesewetter, V. Malyugin, The crosstalk compensator for fiber-optic data transmission systems with division multiplexing, in 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT) (2016), pp. 1–4 9. Vincent F. Fusco, Microwave Circuits: Analysis and Computer-Aided Design (Prentice-Hall, Englewood Cliffs, 1987) 10. N.V. Ivanov, A.S. Korotkov, S-band microstrip bandpass filter design based on new approach to coupling coefficients calculation, in 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (2018), pp. 60–63 11. V.P. Nguyen, A. Gorlov, A. Gelgor, An intentional introduction of ISI combined with signal constellation size increase for extra gain in bandwidth efficiency, in Internet of Things, Smart Spaces, and Next Generation Networks and Systems (Springer, Cham, 2017), pp. 644–652 12. A.S. Ovsyannikova, et al., Approaching the shannon limit by means of optimal FTN signals with increased size of PAM signal constellation, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (2019), pp. 132–135 13. M. Liu et al., Research on spectrum optimization technology for a wireless communication system. Symmetry 12(1), 34 (2020) 14. O.A. Kozyreva et al., Wireless local data transmission network through LED lighting compatible with IEEE 802.11 protocol communication systems. J. Phys. Conf. Ser. 1236(1), 012085 (2019) 15. A.I. Borodkin et al., Errors in simplex data transmission channel based on visible light communication. J. Phys. Conf. Ser. 1326(1), 012030 (2019)

Chapter 25

Chromatic Dispersion in Subcarrier Wave Quantum Cryptography Fedor Kiselev , Roman Goncharov , and Eduard Samsonov

Abstract We report on the chromatic dispersion role in subcarrier wave quantum key distribution system. We built a mathematical model of the change in multimode weak coherent phase-coded states in a fiber channel under the influence of chromatic dispersion and estimated the asymptotic secure key rate. The absence of any compensation will limit the maximum channel length to 53 km. We propose a method to mitigate dispersion effect which significantly increases the system’s reach. All theory shown is experimentally confirmed. Keywords Subcarrier wave · Multimode states · Chromatic dispersion · Quantum communication

25.1 Introduction Nowadays a large number of papers related to experimental fiber-based quantum key distribution (QKD) [1] systems were proposed [2–8]. Due to the fact that very weak signal pulses are used there (the mean photon number is less than unity in the discrete variable schemes), typical problems of fiber optics become critical. Raman scattering, fiber losses, and chromatic dispersion require more detailed consideration than in classic counterparts. Regarding the latter, the method of digital dispersion compensation cannot be applied due to the aforementioned low signal power; therefore, this problem requires alternative approaches to the solution, since there is a significant increase of quantum bit error rate (QBER) due to the dispersion. F. Kiselev · R. Goncharov (B) · E. Samsonov ITMO University, Kronverkskiy, 49, St. Petersburg 197101, Russia e-mail: [email protected] F. Kiselev e-mail: [email protected] © Springer Nature Switzerland AG 2020 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_25

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We propose a solution with respect to the Subcarrier Wave (SCW) quantum cryptography scheme [9–11] where the information is stored in sidebands, so coherent phase-coded states in a fiber channel considered there. One should mention that chromatic dispersion effect was previously studied for Coherent One-Way (COW) QKD protocol [8] and for SCW QKD scheme with double phase modulator (PM-PM) [12]. Notwithstanding, the method proposed in this paper was not previously presented. Here, in this article, we combine classical chromatic dispersion model and mathematical model of SCW QKD protocol. We also show how to notably increase the distance over which a secure key can be distributed without using of devices such as dispersion-shifted fiber or chirped Bragg gratings. We also compare key generation rates for all described cases.

25.2 Materials and Methods 25.2.1 SCW QKD Setup The experimental SCW QKD setup [9] is shown in Fig. 25.1. On the sender side (Alice) coherent monochromatic laser beam with optical frequency ω0 is modulated by electro-optical phase modulator with a frequency Ω and a phase ϕ1 . Output field energy obtained due to energy distribution from the carrier wave to the 2S sidebands at frequencies ωk = ω0 + kΩ, where integer k is limited by ±S. The modulation index and beam intensity are chosen optimal. The phase ϕ1 changes randomly encode Alice’s bit at a transmission window with duration T . The generated multi-mode weak coherent phase-coded state passes through the quantum channel to the receiver side (Bob) where the second modulation is carried out with the same frequency, but a different phase ϕ2 . That is, there is an interference process followed by a reconciliation of bases, a typical step for many QKD systems.

Fig. 25.1 Schematic diagram of the SCW QKD system. Diagrams in circles show the simplified intensity spectrum

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25.2.2 Chromatic Dispersion Impact in SCW QKD From [13] it is known, that one can use Taylor expansion within the vicinity of some frequency to show frequency dependence on propagation constant of fundamental mode in single-mode optical fiber β(ω) ≈ β0 + β1 Δω +

β2 β3 Δω2 + Δω3 , 2 6

(25.1)

where Δω = ω − ω0 . Further, considering in the approximation only two sidebands that are most affected by dispersion, we can write the following phase shifts, depending on the length of the quantum channel L   β2 Φ+ = β1 + Ω Ω L 2

(25.2)

 β2 Φ− = − β1 − Ω Ω L 2

(25.3)



for “right” and “left” sideband, respectively. Since we consider the relative phase shift, it does not matter which side each sideband is from, but for convenience we denote them in this way. The intensity measured by Bob can then be expressed as follows I B = 4 |b|2 (1 + cos(ϕ2 − ϕ1 − Lβ1 Ω) · cos(−Lβ2 Ω 2 /2)),

(25.4)

where b is the classical electromagnetic field amplitude. Considering visibility as a measure of distinction of two quantum states in one basis (in our case, received signal with phases 0 and π ), we obtain    I B,max − I B,min    V = I B,max + I B,min     (1 + cos(−Lβ2 Ω 2 /2)) − (1 − cos(−Lβ2 Ω 2 /2))   =  (1 + cos(−Lβ2 Ω 2 /2)) + (1 − cos(−Lβ2 Ω 2 /2))    = cos(−Lβ2 Ω 2 /2) .

(25.5)

We also consider another approach based on numerical split step Fourier method solving classical pulse propagation via nonlinear Schrödinger equation, where all sidebands were considered. In the next section it is shown that the results obtained by the two methods coincide for a given pulse duration.

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Knowing the intensity, one can get the mean photon number at the given frequency μ=

E pulse , ω0

(25.6)

where E pulse is the pulse energy. To estimate chromatic dispersion effect in fiber-based SCW QKD protocol we combine the quantum model of the proposed system described in detail in [14] and the classical model shown above. We use Δ1 = μcons (L) − μcons (0) as a lost fraction of photons due to the dispersion in case of constructive interference and the increment fraction value in destructive case Δ2 = μdest (L). Then in matching bases we observe mean photon number n  (ϕ1 , ϕ2 ) for phase shifts 0 and π     S   2  ε  + Δ1 , n  (0, Δϕ) = η(L)η B μ0 1 − (1 − ϑ) d00

(25.7)

    S   2  n  (0, π + Δϕ) = η(L)η B μ0 1 − (1 − ϑ) d00 ε  + Δ2 ,

(25.8)

where Δϕ is an average phase deviation of the modulating signal due to non-ideal synchronization system, η B and η(L) describe the losses in Bob’s module and in S (ε) is the Wigner channel, respectively, μ0 is initial mean photon number and dnk d-function describing the amplitude change for the signal mode after electro-optical modulator [15]. Taking into account the typically large optical losses in the quantum channel, we use the click probability of a single-photon detector for a time window in the linear approximation for continuous operation [14] Pdet (ϕ1 , ϕ2 ) = η D n  (ϕ1 , ϕ2 ) + γdar k T,

(25.9)

where η D is a detector’s quantum efficiency and γdar k is a dark count frequency. Then we can express the quantum bit error rate Q as follows Q=

Pdet (0, π + Δϕ) . Pdet (0, Δϕ) + Pdet (0, π + Δϕ)

(25.10)

One of the most important characteristics for any QKD protocol is the secure key generation rate. For the obtained model, we evaluate this value in the asymptotic approximation for the case of collective attacks using Devetak-Winter approach [14, 16]

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Fig. 25.2 Schematic diagram of new proposed SCW QKD setup

K =

Pdet (0, Δϕ) + Pdet (0, π + Δϕ) [1 − h (Q) − χ ] , 2T

(25.11)

where the numerator takes into account  detection, h(x) is  the probability ofS correct −μ0 (1−1d00 (2ε)) is Holevo bound. Shannon binary entropy, and χ = h 0.5 − 0.5e

25.2.3 Approach to Reduce the Chromatic Dispersion Effect We propose to cut out all sidebands on one side (let the right sidebands remain) using cascade simultaneous filtering, as it shown in Figure 25.2. In this experimental case, continuous wave signal with relatively high power was used, and we again obtain visibilities for several channel lengths. In this case, the simplified mean number of photons appearing on Bob’s detector will change to n (0, Δϕ) =

μ0 η(L)ηB

2   S   S S  S in  Δϕ    d0n  (ε1 )dn  n (ε1 )e   + Δ1 , n=1

n (0, π + Δϕ) =

μ0 η(L)ηB

(25.12)

n  =0

2   S   S S  S in  (π+Δϕ)    d0n  (ε1 )dn  n (ε1 )e   + Δ2 n=1

n  =0

(25.13) with the recalculated parameters marked with prime. The Holevo bound must also be recalculated, since the quantum state in the channel has been changed



S      1 d S (ε1 )2 1 − eiπk χ =h 1 − exp −μ0 0k 2 k=0 −1  2    S iπk . ϑ d (ε1 ) 1 − e + 0k

k=−S

(25.14)

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25.3 Results First, we describe all parameters used in calculation (Table 25.1). In Fig. 25.3 experimental results compared with analytical and numerical models described in Sect. 25.2.2. The experimental minimum is not zero that can be explained by low-power nonlinear response and different real attenuation for different frequencies. We cannot observe such a change (decrease and increase) in the secure key generation rate, since taking into account the dispersion QBER dramatically rises from 0 to the distance between 150 and 200 km, that can be seen in Fig. 25.4, therefore, the QKD system is not able to generate a secret key anymore at a quantum channel length of 53 km. We show in Fig. 25.5 that visibility in case of the proposed scheme remains close to maximum value, and the numerical model fits with an experimental data.

Table 25.1 Parameters of the model μ0 = 4 m = 0.319, m  = 0.455 μ = 0.2 T = 10 ns ϑ = 10−3 Δϕ = 5◦ η B = 6.4 dB, ηB = 8 dB η D = 25% γdar k = 25 Hz

Fig. 25.3 Sideband interference visibility using pulses with FWHM of 5 ns

Mean photon number in the carrier before modulation Modulation index (for both cases) Mean photon number in side modes after modulation Duration of the transmission window Central mode transmission Average value of the phase mismatch Losses in the receiver module (for both cases) Detector’s quantum efficiency Dark count frequency

25 Chromatic Dispersion in Subcarrier Wave Quantum Cryptography

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Fig. 25.4 Asymptotic secure key rate K dependence on distance in SCW QKD system under the chromatic dispersion effect

Fig. 25.5 Sideband interference visibility in proposed scheme with sidebands only from the one side of the spectrum

Fig. 25.6 Asymptotic secure key rate K dependence on distance in SCW QKD system for different cases described in legend

Figure 25.6 shows the secure key generation rates for all described cases. A significant increase in the distance in the proposed modified scheme is observed, which is slightly less than the distance in the common SCW QKD scheme without consideration of dispersion effects.

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25.4 Discussion The proposed scheme can be a full replacement for the common SCW QKD setup, despite the smaller maximum achievable distance and lower maximum secure key generation rate, which is explained by additional losses on the used spectral filters.

25.5 Conclusion We analyzed the chromatic dispersion impact in SCW QKD performance by analytical and numerical models. Experimental results confirm the validity of the mathematical model. We estimated asymptotic secure key rate integrated results from the classical model to quantum one. Finally, we formalize a new method to mitigate chromatic dispersion via additional spectral filtering system leaving sidebands only on one side of the spectrum. Also, the validity of the new model has been proven. Acknowledgements This work was funded by Government of Russian Federation (Grant No. MK-777.2020.8).

References 1. C.H. Bennett, G. Brassard, Quantum cryptography: public key distribution and coin tossing. Theoret. Comput. Sci. 560, 7–11 (2014) 2. B. Korzh, C.C.W. Lim, R. Houlmann, N. Gisin, M.J. Li, D. Nolan, B. Sanguinetti, R. Thew, H. Zbinden, Provably secure and practical quantum key distribution over 307 km of optical fibre. Nat. Photonics 9(3), 163 (2015) 3. A. Boaron, G. Boso, D. Rusca, C. Vulliez, C. Autebert, M. Caloz, M. Perrenoud, G. Gras, F. Bussières, M.J. Li et al., Secure quantum key distribution over 421 km of optical fiber. Phys. Rev. Lett. 121(19), 190502 (2018) 4. H.L. Yin, Z.B. Chen, Coherent-state-based twin-field quantum key distribution. Sci. Rep. 9(1), 1–7 (2019) 5. J. Dynes, I. Choi, A. Sharpe, A. Dixon, Z. Yuan, M. Fujiwara, M. Sasaki, A. Shields, Stability of high bit rate quantum key distribution on installed fiber. Opt. Express 20(15), 16339–16347 (2012) 6. K. Patel, J. Dynes, I. Choi, A. Sharpe, A. Dixon, Z. Yuan, R. Penty, A. Shields, Coexistence of high-bit-rate quantum key distribution and data on optical fiber. Phys. Rev. X 2(4), 041010 (2012) 7. I. Choi, Y.R. Zhou, J.F. Dynes, Z. Yuan, A. Klar, A. Sharpe, A. Plews, M. Lucamarini, C. Radig, J. Neubert et al., Field trial of a quantum secured 10 Gb/s DWDM transmission system over a single installed fiber. Opt. Express 22(19), 23121–23128 (2014) 8. M. Mlejnek, N.A. Kaliteevskiy, D.A. Nolan, Modeling high quantum bit rate QKD systems over optical fiber. In: Quantum Technologies, vol. 10674 (International Society for Optics and Photonics, 2018), p. 1067416 9. A. Gleim, V. Egorov, Y.V. Nazarov, S. Smirnov, V. Chistyakov, O. Bannik, A. Anisimov, S. Kynev, A. Ivanova, R. Collins et al., Secure polarization-independent subcarrier quantum key distribution in optical fiber channel using BB84 protocol with a strong reference. Opt. Express 24(3), 2619–2633 (2016)

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10. V. Chistiakov, A. Kozubov, A. Gaidash, A. Gleim, G. Miroshnichenko, Feasibility of twinfield quantum key distribution based on multi-mode coherent phase-coded states. Opt. Express 27(25), 36551–36561 (2019) 11. E. Samsonov, R. Goncharov, A. Gaidash, A. Kozubov, V. Egorov, A. Gleim, Subcarrier wave continuous variable quantum key distribution with discrete modulation: mathematical model and finite-key analysis. Sci. Rep. 10(1), 10034 (2020) 12. J. Mora, A. Ruiz-Alba, W. Amaya, J. Capmany, Dispersion supported BB84 quantum key distribution using phase modulated light. IEEE Photonics J. 3(3), 433–440 (2011) 13. G.P. Agrawal, Fiber-Optic Communication Systems, vol. 222 (Wiley, Hoboken, 2012) 14. G. Miroshnichenko, A. Kozubov, A. Gaidash, A. Gleim, D. Horoshko, Security of subcarrier wave quantum key distribution against the collective beam-splitting attack. Opt. Express 26(9), 11292–11308 (2018) 15. G.P. Miroshnichenko, A.D. Kiselev, A.I. Trifanov, A.V. Gleim, Algebraic approach to electrooptic modulation of light: exactly solvable multimode quantum model. JOSA B 34(6), 1177– 1190 (2017) 16. I. Devetak, A. Winter, Distillation of secret key and entanglement from quantum states. Proc. R. Soc. A Math. Phys. Eng. Sci. 461(2053), 207–235 (2005)

Chapter 26

Development of a Method for Assessing of the Oxygen Supply of Tissues Based on a Multi-channel Spectrum Analyzer Maria S. Mazing , Anna Yu. Zaitceva , and Yuriy J. Kislyakov

Abstract The aim of this work is to develop a non-invasive optical method for assessing the oxygen supply of tissues and the general functional state of a person, based on a spectrophotometric method for determining the concentration of physiological forms of hemoglobin in the blood, with further use of intelligent analysis of multidimensional data to visualize the results, optimize and automate the diagnostic system. The optical system is based on a multichannel optical analyzer with operating wavelengths in the range 450–650 nm. As a result of the experiment with the participation of 10 subjects and visualization of measurements using the developed optical system, it was shown that each subject has his own individual “image” of the functional state, described as a combination of numerical readings of six sensors in arbitrary units. In addition, it turned out that all subjects were divided into two groups according to the adaptive response of the body to the functional load suggested to the subjects in the experiment. The obtained results confirm the effectiveness of the proposed method for assessing the functional state of a person and the prospects of using the developed optical diagnostic system in practical medicine.




Keywords Optical system Spectrophotometry Non-invasive diagnostic method

26.1


Functional state assessment


Introduction

Currently, rapid diagnostic methods are under intensive development [1–6]. Their diversity shows the high demand for these methods [3, 4, 7–12]. In this regard, the question of the use of non-invasive systems based on the spectrophotomeric method for assessing the general functional state of the body and for diagnosing of a wide M. S. Mazing (&)
A. Yu. Zaitceva
Y. J. Kislyakov Institute of Analytical Instrumentation of the Russian Academy of Sciences, St. Petersburg 190103, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_26

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range of human diseases at an early stage in express mode is being raised [13–22]. The latter is extremely important, since many devices, for example, based on nuclear magnetic resonance (NMR), can only be used in stationary rooms or specialized mobile diagnostic [12, 21, 23–30]. Of particular practical interest are non-invasive devices operating according to the absorption spectroscopy methodology based on the different optical properties of oxygenated and deoxygenated hemoglobin fractions. Devices of this class can safely and painlessly determine the saturation of hemoglobin with oxygen in the tissues and organs of the body. As shown by numerous studies, human blood contains a large amount of information about human health [13–18, 25, 28, 30]. Despite the large number of existing methods for processing optical radiation [13–18], they do not always allow its reliable acquisition. In addition, pulse oximeters are not always accurate in the rapid assessment of the oxygen status of tissues and the general condition of the body as a whole, while information is often needed outside of treatment facilities. Therefore, a progress in the development of optical non-invasive systems, especially for personal use, is extremely relevant. The development of hybrid photodetectors with high sensitivity [31–33] made it possible to register weak signals of laser radiation reflected from the vessels. This allows you to use the entire spectrum of visible radiation for research. In our work, we present a laboratory model of a portable tissue oximeter, which is a comprehensive training system capable of recognizing the status of oxygen supply to human tissues and automatic detection of oxygen saturation deviations in tissues and organs.

26.2

The Method of Research

The tissue oximeter under development is an integrated optical system consisting of a receiving optical-electronic part, a collection system, and a computer data processing module. The functional block diagram of the optical system is shown in Fig. 26.1. The developed optical system is based on the photoplethysmography method in the “reflection” scheme, in which the photodetector and radiation source are on the same surface. A photodetector registers back-scattered radiation in tissues. The photoplethysmography method is based on differences in the absorption spectra of oxyhemoglobin and reduced hemoglobin in the blood. The optoelectronic module is based on a six-channel integrated optical spectrum analyzer of the visible range. The six-channel optical spectrum analyzer microcircuit combines the necessary compo-nents for spectral analysis: a six-channel optical sensor, a 16 bit analog-to-digital converters and a universal asynchronous receiver-transmitter (UART). The six-channel sensor operates at wavelengths of 450/500/550/570/600/ 650 nm with full width half maximum is 40 nm [19, 20, 31–33]. The radiation source is integrated into the analyzer chip and is a white light LED. The information and computing module of the optical system provides general control and

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Fig. 26.1 The functional block diagram of the optical system

processing of the resulting data array, which is a mathematical simulation using statistical algorithms for the analysis of multidimensional data (including machine learning algorithms) and image visualization. In order to assess the performance of the developed optical system oximetric studies of tissues were conducted in 10 subjects aged 25–30 to monitor the dynamics of oximetric indicators under the functional load, which is a breath hold for the maximum possible time. All subjects at the time of the experiment had no diseases and pathological conditions of the cardiovascular and respiratory systems. Functional loads based on breath holding are traditionally used in biomedical research as a test to determine the functional state of cardiorespiratory systems and assess the body’s resistance to oxygen deficiency, while the nature of the observed changes in a specific function after the load is compared with its value at rest. Each subject during the experiment was in a sitting position, the diagnostic system was installed on the left hand wrist with the inability to shift the sensor module relative to the initial installation site during all periods of the experiment to minimize measurement error. The recorded readings of six sensors at different periods of the experiment (resting state, the moment of respiration resumption after a delay, recovery period) were entered into a table, which is an array of numerical readings of sensors in arbitrary units, the relative change of which reflects the adaptive response of the test organism to the functional load. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standard.

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The Experimental Results and Discussion

Based on the obtained tabular values for each subject, radial diagrams were constructed, each of which is a polygon with six angles and with six rays extending from a common center to the vertices of the corners of the geometric figure. Each ray is an axis along which the readings of each of the six sensors in arbitrary units are plotted at a specific point in time (state of rest, the beginning of breath holding, the end of holding and resuming breathing, the recovery period). The diagrams constructed during data processing represent the “images” of the subjects, reflecting the status of oxygen supply to the tissues. The dynamics of the image change during the functional load characterizes the various adaptive responses of the body to hypoxic effects. According to the obtained results, all the subjects were divided into 2 groups, each of which significantly differs in a certain tendency to change the “image” after the functional load. The averaged normalized images for 2 groups are presented in Fig. 26.2(a, b) relative to the resting state. In addition, the measurement results were analyzed by the method of principal components, with preliminary normalization of the variables. A multivariate statistical data analysis algorithm was carried out to visualize the obtained measurements in order to reduce the dimensionality of variables and predict the applicability of factor analysis. The representation of images in the form of points in the two-dimensional space of two principal components is shown in Fig. 26.3. According to the results of multivariate data, we can conclude that each subject has a different adaptive response of the body to the functional load. The experimental results were confirmed by an independent biomedical study. Thus, the study needs to expand the statistical data for a full factorial analysis with a large array of experimental data for its clustering with subsequent training of the optical system.

Fig. 26.2 a, b The averaged normalized images of the oxygen status of tissues of two groups of subjects

a)

b)

State of rest Recovery aer load

State of rest Recovery aer load

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Fig. 26.3 Images of the oxygen status of the test subjects in the space of the two principal components, found by 6 variables for 10 subjects. Variables are the numerical readings of each of the six sensors after the functional load, normalized to readings obtained at state of rest

26.4

Conclusion

The results of the study indicate the effectiveness of the developed method for monitoring the oxygen supply of human tissues. Further research will be aimed at expanding the possibilities of obtaining statistical data, revealing correlation dependencies in order to create a trained non-invasive optical diagnostic system capable of recognizing the adaptive response of the body. Acknowledgements The work is performed in the project’s boundaries of the State Task № 075-01073-20-00 “Microfluidic devices and systems for imitation and research processes in living organisms” № CУ HИP 074-2019- 0010.

References 1. V. Yushkova, G. Kostin, S. Rud, V. Dudkin, L. Valiullin, The development of small and medium-sized businesses, as the basis for a balanced development of agriculture in Russia. IOP Conf. Ser. Earth Environ. Sci. 390(1), 012016 (2019) 2. S. Van, A. Cheremisin, A. Glinushkin, V. Yushkova, Application of new architectural and planning solutions to create an ecological city (on the example of Shanghai, China), in E3S Web Conference, vol. 140 (2019), p. 09008 3. V.V. Davydov, E.N. Velichko, N.S. Myazin, V.Y. Rud’, A method for studying the magnetic susceptibility of colloidal solutions in ferrofluidic cells. Instrum. Exp. Tech. 61(1), 116–122 (2018) 4. V.V. Davydov, V.I. Dudkin, N.S. Myazin, V.Y. Rud’, On the possibility of studying condensed media in the express mode using the nuclear-magnetic-resonance method. Instr. Exp. Tech. 61(1), 140–147 (2018) 5. A. Koucheryavy, I, Bogdanov, A. Paramonov, A, The mobile sensor network life-time under different spurious flows intrusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 8121 (2013), pp. 312–317

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6. N. Myazin, Y. Neronov, V. Dudkin, V, Davydov, V. Yushkova, Environmental express monitoring of territory and water bodies at various stages of construction and improvement, in MATEC Web of Conferences, vol. 245 (2018), p. 11013 7. N.S. Myazin, V.V. Davydov, V.V. Yushkova, V.Yu. Rud’, A new method of determining the state of water and agricultural areas in real time. Environ. Res. Manag. 75(2), 28–35 (2019) 8. B. Gizatullin, M. Gafurov, A. Rodionov, G. Mamin, C. Mattea, S. Stapf, S. Orlinskii, Proton-radical interaction in crude oil—a combined NMR and EPR Study. Energy Fuels 32 (11), 11261–11268 (2018) 9. A.I. Zhernovoi, A.A. Komlev, S.V. D’yachenko, Magnetic characteristics of MgFe2O4 nanoparticles obtained by glycine–nitrate synthesis. Tech. Phys. 61(2), 302–305 (2016) 10. S.V. Dyachenko, M.A. Vaseshenkova, K.D. Martinson, I.A. Cherepkova, A.I. Zhernovoi, Synthesis and properties of magnetic fluids produced on the basis of magnetite particles. Russ. J. Appl. Chem. 89(5), 690–696 (2016) 11. R.V. Davydov, V.I. Antonov, V.V. Yushkova, N.M. Grebenikova, V.I. Dudkin, A new algorithm for processing the absorption and scattering signals of laser radiation on a blood vessel and human tissues. J. Phys: Conf. Ser. 1236(1), 012079 (2019) 12. B. Yavkin, M. Gafurov, M. Volodin, G. Mamin, S.B. Orlinskii, EPR and double resonances in study of diamonds and nanodiamonds. Exp. Methods Phys. Sci. 50, 83–113 (2019) 13. H. Yu, B. Liu, Successful use of pharyngeal pulse oximetry with the oropharyngeal airway in severely shocked patients. Anaesthesia 62(7), 734–736 (2007) 14. R. Golpe, A. Jiménez, R. Carpizo, J.M. Cifrian, Utility of home oximetry as a screening test for patients with moderate to severe symptoms of obstructive sleep apnea. Sleep 22(7), 932– 937 (1999) 15. R.N. Khizbullin, Dual-channel optical pulse oximeter on the base of laser sensors to solve actual problems in medical practice. Fotonika 1, 144–157 (2017) 16. A.S. Grevtseva, V.V. Davydov, VYu. Rud’, Development of methods for results reliability raise during the diagnosis of a person’s condition by pulse oximeter. J. Phys: Conf. Ser. 1135(1), 012056 (2018) 17. R.V. Davydov, M.S. Mazing, V.V. Yushkova, A.V. Stirmanov, VYu. Rud, A new method for monitoring the health condition based on nondestructive signals of laser radiation absorption and scattering. J. Phys: Conf. Ser. 1410(1), 012067 (2019) 18. A.S. Grevtseva, V.V. Davydov, K.V. Greshnevikov, VYu. Rud, A.P. Glinushkin, Method of assessment the degree of reliability of the pulse wave image in the rapid diagnosis of the human condition. J. Phys: Conf. Ser. 1368(2), 022072 (2019) 19. N.A. Ivliev, V.V. Podlipnov, R.V. Skidanov, A compact imaging hyperspectrometer. J. Phys: Conf. Ser. 1368(2), 022053 (2019) 20. V.A. Blank, Y.S. Strelkov, R.V. Skidanov, Axicon for imaging spectrometer. J. Phys: Conf. Ser. 1368(2), 022003 (2019) 21. M.Y. Marusina, A.V. Fedorov, V.E. Prokhorovich, N.V. Tkacheva, A.L. Mayorov, Development of acoustic methods of control of the stress-strain state of threaded connections. Meas. Tech. 61(3), 297–302 (2018) 22. A.Yu. Zaitceva, L.P. Kislyakova, Yu.Ya. Kislyakov, S.A. Avduchenko, Analytical multisensory trainable system for diagnosing vocational aptitude of military medical specialists by ion content in the expired breath condensate. J. Phys. Conf. Ser. 1400(1), 033022 (2019) 23. M.Y. Marusina, E.A. Karaseva, Automatic segmentation of MRI images in dynamic programming mode. Asian Pacific J. Cancer Prev. 19(10), 2771–2775 (2018) 24. M.Ya. Marusina E.A. Karaseva, Application of fractal analysis for estimation of structural changes of tissues on MRI images. Russ. Electron. J. Radiol. 8(3), 107–112 (2018) 25. T.N. Kiryakova, M.Ya. Marusina, P.V. Fedchenkov, Automatic methods of contours and volumes determination of zones of interest in MRI images. Russ. Electron. J. Radiol. 7(2), 117–127 (2017)

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Chapter 27

Possibilities of Using Optical Solitons in High-Speed Systems Elena I. Andreeva

and Ivan A. Potapov

Abstract The fiber optic data system has many different important parameters, one of which is the product of the bit rate by the transmission distance (span length). The occurrence of nonlinear distortion of the pulse shape of the symbol, when the initial pulse power becomes high, leads to the problem of increasing span length. To increase this length and the bit rate in a standard single mode fiber (SSMF) and in a nonzero dispersion shifted (NZDS) fiber, the pulse propagation mode can be used. And to increase the power of the input optical pulse, the special types of optical fibers with the big effective cross section or the combination of different fibers are used. In describing the evolution of a soliton, we must take into account linear effects, such as fiber group velocity dispersion and optical losses in fiber, nonlinear effects, such as self-phase modulation and Raman self-frequency shift and the signal-noise ratio of the pulse source. It can be shown that the maximum optical system capacity can be achieved using an NZDS fiber. Keywords Fiber optics transmission system

27.1


Optical soliton

Introduction

The information capacity defined as the product of the bit rate B by the transmission distance z (the length of the span) is one important parameter of the fiber optic data system. Information capacity of a data transmission system directly depends on symbol pulse width—To and their peak power P. Increase in speed of data transmission assumes reduction of duration of symbol pulse: the bit rate B = (Q* To)−1, where Q—is on-off time ratio, that leads to broadening of a spectrum of symbol E. I. Andreeva
I. A. Potapov (&) The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, St. Petersburg 193232, Russian Federation e-mail: [email protected] E. I. Andreeva e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_27

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pulse that accelerates dispersive broadening of symbol pulse, on the one hand, and at the high power causes manifestation of nonlinear effects in the fiber light guide with another. To achieve bit rate 10 Gbit/s and more the symbol pulses of the picosecond and subpicosecond width must be used. However, subpicosecond optical pulses width experience strong dispersion broadening during propagation along a fiber-optic communication line. Therefore, the dispersion compensation fibers (DCF) must be used at the end of the link to compensate accumulated dispersion. But such fibers usually make a big optical loss, for the compensation of which optical amplifiers can be used. But optical amplifiers create additional noise, which leads to a deterioration in the signal-noise ratio. Therefore, dispersion compensation methods at the physical level are of interest for the high bit rate long haul fiber-optics systems. So, such nonlinear effects as the self-phase modulation are one way to compensation the optical symbol pulse dispersion broadening. Nonlinear effects of different nature have different effects on momentum evolution [1–21]. In the region of abnormal dispersion, the self-phase modulation can be used to compensate for the dispersion expansion of the pulse. However, other nonlinear effects may have a negative effect on bit pulse dynamics. In this way, at the subpicosecond width of the symbol pulse, Raman self-scattering can have a significant influence on its evolution. The features of propagation of optical pulses of subpicosecond width in the fiber considering both linear (fiber dispersion and optical losses) and nonlinear (self-phase modulation, Raman self-frequency shift) effects, as well as influence of noise of the source of these pulses are considered. It is shown that it is possible to optimize the parameters of the fiber-optics communication system in order to increase its information capacity.

27.2

The Features of the Optical Soliton Propagation

In order to overcome the dispersion limitation can be used the solitons as the symbol pulses in bit stream. The initial balance between the dispersion and nonlinearity represents the fundamental optical soliton when the initial power Po of the solitons: Po ¼ jb2 j=ðcs2o Þ; where b2 is the fiber dispersion d2b/dx2, b is the propagation constant, x = 2pf is the frequency, c nonlinear Kerr coefficient, so = To/1.763. The initial soliton energy Eo = 2Pso. For SSMF (Standard Single Mode Fiber) b2 = 18 ps2/km, and b2 = 2 ps2/km for NZDS (Non-Zero Dispersion Shifted Fiber) fiber can be used. In a real fiber there is some small loss. When fiber loss is included, the total energy E(z) in a pulse decay with the distance along the fiber and is proportional to exp(−2az), z is distance, a is fiber loss.

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If we assume a level loss in SSMF of 0,2 dB/km, a = 0,023 km−1. If the loss length a1 is long compared to the dispersion length LD: LD ¼ s2 =b2 , the pulse width changes as sðzÞ ¼ so expð2azÞ: The self-frequency shift of a soliton is extremely sensitive to the pulse width [5, 6]: DxR ðzÞ ¼

8 jb2 jgðso ÞTR z 15 s4

TR is the Raman parameter, typically TR = 3 fs for the optical fiber [5], g(so) is a weak function of so. For optical pulses of subpicosecond width: g(so) * 1. The change in position of the center tR from its original value (Fig. 27.1) tR ¼

  1 b22 TR gðso Þ 1  expð8azÞ z  15 s4o 8a a

This frequency shift in center of each pulse in a bit stream would not cause problem to data carrying capacity. But if width fluctuates of the initial pulse as result of laser noise an error can occur at the end of fiber-optics communication system. The self-frequency shift result to the change in position of the center tR from its original value. In practical the fluctuations in the solitons pulse width DT can be induced by changes in the energy DE from the laser as well as noise in the input pulse: DE/E = DT/T. The change in the output pulse position DtR is obtained: DtR ¼

  4 b22 TR Dso gðso Þ 1  expð8azÞ z  15 s4o so a 8a

Let’s define the admissible value of ΔtR less than half the clock interval: ΔtR < 2/ B, where B is the bit rate. For soliton systems, Q = 10 is usually taken. Then the permissible range of information transfer z can be estimated from (1), for the case

Fig. 27.1 The change in position of the center tR from its original value for To = 1 ps in fiber with dispersion b2 = 2 ps2/km at a loss level of 0.2 dB/km (a = 0.023 km−1)

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Fig. 27.2 The choice of the propagation range z of symbol pulses depending on their initial duration To for the case when the input fluctuations DTo/To = 0.001, provided that the fluctuation value DtR of the position of the center of the bit pulse does not exceed half the clock interval, in a fiber with biased dispersion with b2 = 2 ps2/km at a loss level of 0.2 dB/km (a = 0.023 km−1)

when the input fluctuations DTo/To = 0.001 (signal/noise ratio of the symbol pulses source 103). With an increase in the initial width To, the self-scattering effect weakens and the permissible range z increases. However, the length of the soliton propagation regime also depends on To (Fig. 27.2).

27.3

Results

Figure 27.3 shows the information capacity, defined as the product of the bit rate B by the transmission distance z for soliton system. When using NZDS fibers with a symbol pulse width To nearly 1 ps, the maximum information capacity 15 Tbit
km is achieved.

Fig. 27.3 The information capacity B*z in the optical fibre with biased dispersion with b2 = 2 ps2/km as a function of the initial pulse width To

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Possibilities of Using Optical Solitons …

27.4

245

Conclusion

We have shown that when using the fiber with predetermined parameters, it is possible to determine a range of initial symbol pulse durations To at which it is possible to realize a maximum range z at a high bit pulse rate B such that the time jitter cased source amplitude fluctuation will be small.

References 1. E. Andreeva, M. Bylina, S. Glagolev, P. Chaimardanov, Proc. Telecommun. Univ. 4(1), 5–12 (2018) 2. A. Shcherbakov, E. Andreeva, Optical Fiber Technology, vol. 2, pp. 127–133 3. E. Andreeva, M. Bylina, S. Glagolev, P. Chaimardanov, Proc. Telecommun. Univ. 4(2), 26– 35 (2018) 4. E. Andreeva, M. Bylina, S. Glagolev, P. Chaimardanov, Proc. Telecommun. Univ. 4(3), 5–16 (2018) 5. Y. Kivshar, G. Agrawal, The Institute of Optics University of Rochester, New York, USA (2003) 6. D. Wood, J. of Lightwave Technol. 8, 1097–1106 (1990) 7. M. Tarasenko, V. Davydov, N. Sharova, V. Lenets, T. Yalunina, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 10531 (2017), pp. 227–232 8. G. Agrawal, Nonlinear Fiber Optics (5th Academic Press, San Diego, 2013) 9. M. Tarasenko, V. Lenets, K. Malanin, N. Akulich, V. Davydov, J. Phys. Conf. Ser. 1038(1), 012035 (2018) 10. L. Mollenauer, G. Gordon, Solitons in Optical Fibers (Academic Press, San Diego, 2006) 11. A. Podstrigaev, R. Davydov, V.Yu. Rud’, V. Davydov, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11118 (2018), pp. 624–630 12. A. Hasegawa, Massive WDM and TDM Soliton Transmission Systems (Academic Press, New York, 2002) 13. V. Fadeenko, D. Kuts, V. Vasiliev, V. Davydov, New design of fiber-optic communication line for the transmission of microwave signals in the X-band. J. Phys. Conf. Ser. 1135(1), 012053 (2018) 14. M. Ferreira, Nonlinear Effects in Optical Fibers (Wiley, Hoboken, 2011) 15. N. Grebenikova, V. Davydov, A. Moroz, M. Bylina, M. Kuzmin, IOP Conf. Ser. Mater. Sci. Eng. 497, 012109 (2019) 16. G. Gordon, Opt. Lett. 11(10), 662–664 (1986) 17. A. Moroz, R. Davydov, V. Davydov, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11660 (2018), pp. 710–718 18. N. Myazin, V. Dudkin, N. Grebenikova V.Y. Rud’, A. Podstrigaev, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11660 (2019), pp. 744–756 19. Moroz A.: J. Phys. Conf. Ser. 1410(1), p. 012212 (2019) 20. G. Agrawal, Nonlinear fiber optics: its history and recent progress. Opt. Soc. Am. B J. 28(12), 1–10 (2011) 21. R. Davydov, V. Antonov, A. Moroz, IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), vol. 8906791 (2019). pp. 295–297

Chapter 28

Fluorescence Quenching of Tetraphenylporphyrin-Fullerene Molecular Complexes Marina A. Elistratova , Margarita O. Koroleva , and Irina B. Zakharova Abstract Results of 5,10,15,20-tetraphenylporphyrin (H2TPP) fluorescence quenching studies are presented. In combination with an effective acceptor, which can be fullerene C60, it is possible to use a porphyrin-fullerene donor-acceptor pair for the development of solar cells, artificial photosynthesis systems and effective photodetectors. Self-quenching of photoluminescence observed at high concentrations of solutions, which prevents obtaining adequate spectroscopic results. We show that it was possible to study high concentration solutions without self-quenching using low absorption excitation. Therefore, the concentration range where quenching does not occur was investigated. It was shown that the fluorescence saturation at concentrations more than 0.4 mmol/l is caused by the phenomenon of an internal filter. The study of fluorescence quenching of porphyrins is of great importance in the research of the porphyrin-containing composites photoluminescence. Fluorescence quenching is considered in porphyrin-fullerene mixtures with C60 concentration of 0.3–0.6 mmol/l and at H2TPP concentrations at which fluorescence is not saturated. A fluorescence decrease proportionally to the fullerene concentration is observed. This result is explained by the presence of photoinduced charge transfer followed by nonradiative charge recombination.







Keywords Fluorescence Quenching Tetraphenylporphyrin Molecular complexes Photoindused charge transfer





Fullerene


M. A. Elistratova Ioffe Institute, 26 Polytechnicheskaya Street, St. Petersburg 194021, Russia e-mail: [email protected] M. O. Koroleva (&)
I. B. Zakharova Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Street, St. Petersburg 195251, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_28

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Introduction

Besides to the important biological function [1], materials from the porphyrin group are actively used in the semiconductor industry [2] due to their donor properties [3] and the presence of intense absorption [4] in ultraviolet and visible spectral regions. Numerous studies devoted to porphyrins optical properties [5] are known in the literature. Moreover, different authors observed dissimilarities in photoluminescence spectra form of porphyrin solutions. Spectral shifts associated with aggregation processes in concentrated solutions have been previously studied [6–9]. At the same time, differences in the intensities of separated porphyrin peaks can also be associated with photoluminescence quenching in concentrated solutions. It is necessary to minimize self-quenching with an increase of the concentration of solutions to obtain correct results in optical spectroscopy. Quenching makes it difficult to correctly study of the effect of aggregation on photoluminescence. Molecular complexes of porphyrins with fullerene C60, which is a strong acceptor, are of particular interest in photoelectronics [10, 11]. Porphyrins and fullerene can form molecular complexes already in solutions. However, this requires close interaction, i.e. sufficiently large concentrations of both materials. The phenomenon of concentration saturation of the tetraphenylporphyrin fluorescence are studied in this work. The concentration at which fluorescence saturation is not observed is determined. At such a fixed concentration, fluorescence quenching of tetraphenylporphyrin with fullerene C60 was studied in the range of H2TPP:C60 molar ratios from 1:1 to 3:1.

28.2

Methods and Materials

5,10,15,20-tetraphenylporphyrin (H2TPP, Sigma-Aldrich 99% low chlorin) toluene solution was chosen as a material for this study. Solutions were considered in concentrations of 2.5
10−5–1.6
10−3 mol/l. Solutions were placed in a quartz transparent cell with a thickness/volume of the working part of 3 mm to measure fluorescence spectra. Solution of tetraphenylporphyrin and fullerene C60 mixture in toluene were made for this study in the following way: fullerene powder was added into the prepared volume of a tetraphenylporphyrin solution of a given concentration, mixed mechanically and kept for 24 h in a thermostat at the temperature of 40 °C in a sealed volume. This allowed to keep the tetraphenylporphyrin concentration unchanged at various ratios fullerene. Fluorescence spectra were measured in the pulse mode. The emission was excited by a nitrogen laser at a wavelength of 337 nm, with a pulse duration of 10 ns, a pulse repetition rate of 100 Hz, an average power of 3 mW. A signal registration delay time was selected in the maximum of photoluminescence intensity. It is worth noting that the additional comparisons of the spectra in the pulsed and stationary modes did not show significant differences, which is consistent with

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the measured average photoluminescence decay time of 1.5 ns. Optical absorption spectra were measured using a Shimadzu UV 3600 Plus spectrophotometer.

28.3

Results and Discussions

In addition to fluorescence quenching in the presence of a quencher, self-quenching phenomena are possible when a luminophore and a quencher is the same substance. To study such phenomena, fluorescence spectra of 5,10,15,20-tetraphenylporphyrin solutions at various concentrations were measured at 300 K and shown in Fig. 28.1. Spectra comply with literature data in the shape and position of the maxima [12, 13]. They represent of two fluorescence maxima that correspond to two excited transitions in the energy structure of tetraphenylporphyrin—the electronic transition (k = 650–670 nm) and the electron-vibrational one (vibronic) (k = 710–730 nm). As it seen from Fig. 28.2, there is no direct concentration quenching, i.e., a decrease of fluorescence intensity in this range of solution concentrations, is not observed. This fact differs from the data in [7], in which concentration quenching was already observed with solution concentrations of 10−5 mol/l. Despite the presence of a significant overlap (see Fig. 28.3) of the absorption bands edge Q(1-0) with a short-wavelength emission band Q(0-0) (650 nm), a decrease of the relative intensity of the emission peak with concentration increasing due to the Förster transfer is not observed, in contrast to [14]. Such differences in the experimental data on concentration quenching can be associated with the experimental equipment, namely, with a different excitation wavelength, which leads to significantly lower values of the extinction coefficient at a wavelength of 337 nm compared with the absorption values at 405 nm in [14].

Fig. 28.1 Fluorescence spectra of H2TPP in toluene at 300 K (excitation wavelength —337 nm) at different concentrations

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Fig. 28.2 Separated peaks fluorescence intensity depending on concentration

Fig. 28.3 Fluorescence (red line) and absorption (black line) spectra of a H2TPP solution in toluene with a concentration of 4
10−4 mol/ l. Measured at 300 K

The dependences of corresponding fluorescence peaks intensities on concentration are shown in Fig. 28.2. There is the concentration saturation is observed starting from 4
10−4 mol/l. It may be noted that a slightly red shift of the spectra is observed at high concentrations (see Fig. 28.1). This is caused by the processes of molecular aggregation—the formation of molecular complexes of several molecules in solutions with concentrations close to ultimate solubility. In this case, the concentration region in where the spectrum red-shifting is observed coincides with the data in [7]. A possible reason for the decrease in the fluorescence intensity growth rate with the concentration increase is the effect of the internal filter, since there are no changes in the relative peaks intensities. The internal filter effect is observed at high optical densities in concentrated solutions and consists in a decrease in the intensity of exciting light in the depth of samples (in this case in the cuvette volume). Front layers of samples become as a filter that absorbs most of the exciting light.

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Fig. 28.4 Fluorescence spectra of H2TPP/C60 toluene solutions

Fluorescence intencity, arb. unitns

The illumination of samples become uneven, which leads to a decrease in the growth rate of the photoluminescence intensity. The exciting light is almost completely absorbed, and fluorescence can only be observed from a thin surface layer at very high concentrations. In this case, the shape of the fluorescence spectrum is not distorted if the optical density is sufficiently small in the region of overlapping absorption and fluorescence spectra. The absence of the spectrum shape distortion indicates the absence of self-absorption by the fluorescent component. Quenching in the presence of a quencher was studied using solutions of tetraphenylporphyrin and fullerene C60. Results are shown in Fig. 28.4. Tetraphenylporphyrin can form both covalently bonded supramolecular structures and molecular donor-acceptor complexes with fullerene. Earlier we showed in [15], that they can form a molecular complex, calculated the parameters of complexes and the possibility of photoinduced charge transfer between the components of the complexes, that is promising for use in photonics. In the method described above, the concentration of porphyrin was 4
10−4 mol/l and did not change. At the same time the concentration of C60 changed from 0 to 4
10−4 mol/l. The tetraphenylporphyrin concentration was selected from the region of a linear fluorescence dependence, which excludes the internal filter effect. In this case, the decrease of the fluorescence intensity occurs almost in proportion to the concentration of fullerene. There is no change in the shape of H2TPP/C60 solutions spectra. This means that the luminescent component in the mixture is tetraphenylporphyrin. There is 6 times decrease of fluorescence, which is demonstrated in Stern-Volmer coordinates in Fig. 28.5. A close linear relationship in the Stern-Volmer coordinates is characteristic of a solution with one fluorophore. Deviations from linearity in this case may be associated with the experimental inaccuracy. Fullerene is a strong acceptor, and photoexcitation causes a fast (less than 100 ps) photoinduced charge transfer to fullerene with subsequent nonradiative recombination, which is characteristic of dynamic quenching, in comparison with H2TPP 4·10-4 mol/l H2TPP:C60 3:1 H2TPP:C60 2:1 H2TPP:C60 1:1

12 10 8 6 4 2 0 600

650

700

Wavelengh, nm

750

800

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Fig. 28.5 Fluorescence intensities of separated peaks of H2TPP/C60 solutions in Stern-Volmer coordinates and electronic transitions of H2TPP/C60 complexes

tetraphenylporphyrin emission time (1.5 ns). A qualitative scheme of such transitions is presented in Fig. 28.5. It is possible to roughly estimate the quenching efficiency. It is about 80%, which is a high rate. Meanwhile the Stern-Volmer constant is Ksv = 1.3
10−3.

28.4

Conclusions

The study showed that upon excitation at a wavelength of 337 nm, which provides lower values of the extinction coefficient compared to 405 nm, concentration quenching is not observed up to high values of solution concentrations. This allows to study aggregation processes, the molecular complexes formation and fluorescence quenching by interaction with C60 in highly concentrated solutions. The internal filter effect begins from the H2TPP concentration more than 0.5
10−3 mol/l. The addition of C60 in a molar ratio of 1:1 reduces the fluorescence intensity by about 4 times. This allows us to estimate that the quenching occurs with an efficiency of 80%. The fluorescence quenching by fullerene is associated with the photoinduced charge transfer. High efficiency of fluorescence quenching indicates of an effective charge transfer between the components of the tetraphenylporphyrin-fullerene molecular complex. Effective photoindused charge transfer allows using a tetraphenylporphyrin-fullerene pair for the development of photodetectors and solar cells.

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References 1. M. Pernot, T. Bastogne, N.P. Barry, B. Therrien, G. Koellensperger, S. Hann, M. Barberi-Heyob, Systems biology approach for in vivo photodynamic therapy optimization of ruthenium-porphyrin compounds. J. Photochem. Photobiol. B 117, 80–89 (2012) 2. S. Verma, H.N. Ghosh, Exciton energy and charge transfer in porphyrin aggregate/ semiconductor (TiO2) composites. J. Phys. Chem. Lett. 3(14), 1877–1884 (2012) 3. T. Yamamoto, J. Hatano, T. Nakagawa, S. Yamaguchi, Y. Matsuo, Small molecule solution-processed bulk heterojunction solar cells with inverted structure using porphyrin donor. Appl. Phys. Lett. 102(1), 5 (2013) 4. J. Schmitt, V. Heitz, A. Sour, F. Bolze, H. Ftouni, J.F. Nicoud, B. Ventura, Diketopyrrolopyrrole-porphyrin conjugates with high two-photon absorption and singlet oxygen generation for two-photon photodynamic therapy. Angew. Chem. 127(1), 171–175 (2015) 5. M.B.M. Krishna, V.P. Kumar, N. Venkatramaiah, R. Venkatesan, D.N. Rao, Nonlinear optical properties of covalently linked graphene-metal porphyrin composite materials. Appl. Phys. Lett. 98(8), 081106 (2011) 6. I.B. Zakharova, M.A. Elistratova, N.M. Romanov, O.E. Kvyatkovskii, Specific features of the electron structure of ZnTPP aggregated forms: data of optical measurements and quantum-chemical calculations. Semiconductors 52(13), 1708–1714 (2018) 7. M.A. Elistratova, I.B. Zakharova, G.V. Li, R.M. Dubrovin, O.M. Sreseli, The effect of crystallization conditions on the spectral characteristics of tetraphenylporphyrin thin films. Semiconductors 53(1), 51–54 (2019) 8. Z. Zhou, C. Cao, Q. Liu, R. Jiang, Hybrid orbital deformation (HOD) effect and spectral red-shift property of nonplanar porphyrin. Org. Lett. 12(8), 1780–1783 (2010) 9. J. Tang, L. Niu, J. Liu, Y. Wang, Z. Huang, S. Xie, L.A. Belfiore, Effect of photocurrent enhancement in porphyrin–graphene covalent hybrids. Mater. Sci. Eng. C 34, 186–192 (2014) 10. D.M. Guldi, C. Luo, M. Prato, E. Dietel, A. Hirsch, Charge-transfer in a p-stacked fullerene porphyrin dyad: Evidence for back electron transfer in the “Marcus-inverted” region. Chem. Commun. 5, 373–374 (2000) 11. D.M. Guldi, Fullerene-porphyrin architectures; photosynthetic antenna and reaction center models. Chem. Soc. Rev. 31(1), 22–36 (2002) 12. S. Oshima, T. Kajiwara, M. Hiramoto, K. Hashimoto, T. Sakata, Excited tetraphenylporphine on a silver surface: fluorescence quenching and interference effects. J. Phys. Chem. 90(19), 4474–4476 (1986) 13. G.P. Gurinovich, A.N. Sevchenko, K.N. Solov’ev, The spectroscopy of the porphyrins. Soviet Phys. Uspekhi 6(1), 67–105 (1963) 14. M. Ghosh, S. Nath, A. Hajra, S. Sinha, Fluorescence self-quenching of tetraphenylporphyrin in liquid medium. J. Lumin. 141, 87–92 (2013) 15. M.A. Elistratova, I.B. Zakharova, N.M. Romanov, V.Y. Panevin, O.E. Kvyatkovskii, Photoluminescence spectra of thin films of ZnTPP–C60 and CuTPP–C60 molecular complexes. Semiconductors 50(9), 1191–1197 (2016)

Chapter 29

Gold Nanoparticle Array Formation by Low-Temperature Annealing Polina Bespalova, Yakov Enns, Tatyana Kunkel, Vasilii Balanov, Anastasiya Speshilova, Alexandr Vorobyev, Maxim Mishin, and Platon Karaseov Abstract Gold nanoparticle (GNP) formation on silicon by low temperature annealing is studied. Thin gold film deposited on (100) Si annealed in argon ambient at temperatures from 200 to 275 °C turns to spherical cap nanoparticles up to 20 nm in height, as revealed by atomic force microscopy. Nanoparticle shape and size distribution depends on the annealing temperature. Visible light reflection spectrum changes strongly with the change of GNP distribution. A model of light reflection from the Si-GNP surface is developed. Numerical simulation of optical reflection spectra is performed. Results show qualitative agreement between experimental and calculated data. Keywords Gold nanoparticle formation Light reflection spectra Solar cells




29.1


GNP
Si native oxide
Annealing


Introduction

Noble metal nanoparticles attract a lot of interest as they can be used in a number of important practical applications. In particular, gold nanoparticles (GNP) are widely used in catalysis [1, 2], sensing [2, 3], solar energy harvesting and storage [4–7, 23]. Arrays of 4f metal nanoparticles such as Ag, Au, Cu, etc. exhibit strong light P. Bespalova (&)
A. Speshilova
P. Karaseov Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia e-mail: [email protected] Y. Enns
A. Vorobyev
M. Mishin Alferov University, 194021 St. Petersburg, Russia T. Kunkel Moscow Institute of Physics and Technology (National Research University), 141701 Dolgoprudny, Moscow Region, Russia V. Balanov Solution Chemistry of Advanced Materials and Technology Laboratory, ITMO University, 197101 St. Petersburg, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_29

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absorption bands in visible part of spectrum determined by surface plasmonic resonance [8]. Formation of ordered arrays needs to have good technologic equipment which has to be profitable and have possibilities for low cost mass production. Technologically basic equipment (and process) could make improvement of catalytic, optic and electrical properties with of final product. A number of bottom-up strategies were developed to form GNPs. Wet synthesis techniques, like noble metal halide reduction are well established [2, 9]. Colloids filled with spherical nanoparticle of several nm in diameter with narrow size distribution can be achieved his way. However, wet-grown GNP surface is inherently covered with passivating molecules, which affect its catalytic, optical and electrical properties [5–7, 10]. GNP aggregation can be disadvantage as well. Several applications require pure GNP surface [10]. The use of thin gold films for preparing GNP-activated substrates was suggested as they hold advantages over the colloid gold. Several groups have studied possible ways to transform thin metallic films deposited on a substrate into nanosized particles. In particular, ion beam irradiation has been proposed as a useful tool [11–13, 25]. However, accelerated ion bombardment inevitably introduces radiation damage to the substrate by different mechanisms [14] thus reducing area of possible application of this technique. At the same time, it is established that thin metal films can disintegrate into particles upon annealing at temperatures below the melting point [15, 16]. That process is referred to as solid-state dewetting [15]. It occurs while the material remains in the solid state. Dewetting can be used for thin film nanostructuring and metal nanoparticle formation [15–17]. Typical reported temperatures for this process are about 500–900 °C [1]. Such temperatures give ample energy for atom migration and formation of spheroidal-like nanocluster arrays. Shape of particles is determined by a total energy of system and wetting angle between metal particle and substrate (which is defined by free surface energy). Dielectric matrix used in combination with embedded gold nanoparticles allows tailoring the properties of nanocomposite material thus reaching unique combinations. Promising matrix materials are organoplastics or semiconductor oxides [18–21]. However, high temperature (500–900 °C) treatment is not possible in this case due to decomposition of organic substance or phase transition and morphology transformation of oxides. Iron oxide is an example of oxide matrix that has a unique combination of magnetic, optic, electrical and catalytic properties, at the same time being widespread in nature. On the other hand, this material in thin film form is very sensitive to synthesis temperature especially during chemical vapour deposition (CVD) process and temperature increase above 300 °C causes phase transition and changes properties of iron oxide [22, 24]. Typical temperatures used in thin semiconducting iron oxide film production do not exceed 290 °C. At the same time, formation of GNP by thin gold film heat treatment can possible at the temperatures lower than the critical temperature of phase transition (300 °C). The aim of this work is to investigate GNP formation by low-temperature annealing (200–275 °C).

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29.2

257

Methods

29.2.1 Experimental Techniques To perform experimental investigation of nanoparticle formation thin gold film was deposited on (001) silicon substrate by thermal evaporation. Native oxide was not etched before placing substrates into the vacuum chamber. The average thickness of the deposited film was 1.5 nm. The formation of gold nanoparticles was carried out by annealing in a quartz reactor in argon ambient (flow rate 150 ml/min, pressure 102 Pa) for two hours. Morphology of the film has been studied by atomic force microscopy in the tapping mode using NTEGRA AURA device manufactured by NT MDT (Russia). Visible light reflection spectra were measured by Shimadzu UV-3600 Plus device in 180–2200 nm range with a spectral resolution of 1 nm.

29.2.2 Modeling AFM images were treated with specially developed algorithm realised using Python language in Blender three-dimensional visualization software. The algorithm is based on the searching for maximum height h and the nearest minima to determine the diameter c of the particle lying in the surface plane. Three-dimensional reconstruction of nanoparticle ensemble was created on the base of obtained data. Each nanoparticle was treated as a spherical cap with polar angle Q, diameter C, and particle volume V. This algorithm allows us to process particles with a polar angle greater than p. Schematic of a spherical cap and corresponding parameters are shown in the Fig. 29.1. Preliminary filtering has been applied to the experimental image in order suppress noise. Effect of GNP on light reflection spectra was numerically analyzed by COMSOL code simulation using full-wave approximation. Samples were modeled as a set of the following sequential layers: Si (>4 lm thick)—native SiO2 (2–10 nm thick)— GNP ensemble—Air. Periodical GNP positions in an ideal hexagonal cell were assumed. Floquet periodicity conditions were applied at the lateral directions of the elementary simulation cell. The geometric parameters of the gold nanoparticle were

Fig. 29.1 Schematic drawing of a spherical cap representing a nanoparticle with indicated polar angle Q and diameter C (a), and elementary simulation cell used in reflectance spectra simulation

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chosen to be a spherical cap with parameters (C, Q) derived from experimental data analysis for each annealing mode as described in the previous section.

29.3

Results and Discussion

29.3.1 Nanoparticle Formation To perform experimental investigation of GNP formation 1.5 nm thick Au film was deposited on Si samples. Gold layer thickness was measured by Rutherford backscattering technique after deposition. Samples were then annealed in argon ambient at temperatures 200, 225, 250 and 275 °C. Surface morphology of the samples was investigated by atomic force microscopy. Figure 29.2(a) demonstrates typical 3D AFM image obtained from the sample annealed at 275 °C. From the AFM image it can be observed that the continuously deposited film has broken into a set of randomly distributed nanoparticles after the thermal treatment. It can be seen that the substrate is not damaged during the annealing and only the deposited metallic film is affected by the process. The particle shape is predominantly sphere-like. On the base of these observations spherical cap have been chosen as a geometric figure to model experimental structures and determine space parameters of GNP ensembles. An example of a sample surface reconstruction is shown in Fig. 29.2(B). GNPs are drawn as spherical cap figures, native oxide, which was not etched out, is shown as the purple layer between the substrate and GNPs. This model was used to analyze shape distributions of the nanoparticles. For samples obtained at all annealing temperatures, modeling of the structure obtained was performed. The corresponding histograms are shown in Fig. 29.3. From Fig. 29.3(a) it is seen that the polar angle of the spherical cap decreases continuously with annealing temperature increase. The diameter of the cap (see Fig. 29.3(b)) also decreases, but the spread is much larger in this case. Thus, the degree of formation of nanoparticles into a spherical shape increases with annealing temperature increase.

A

B

Fig. 29.2 200  200 nm 3D AFM height map measured from the sample annealed at 275°C (A), and corresponding reconstruction of GNP shape (B). Native oxide layer is shown with violet color

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25

200 225 250 275

a

20 15 10 5 0

0

20

40

60

80

100

Fraction of particles (%)

Fraction of particles (%)

30 15

200 225 250 275

b 10

5

0 10

120

20

30

40

50

Cap diameter (nm)

Cap angle (degrees)

Fig. 29.3 Experimental (symbols) and corresponding Gaussian approximation (lines) values of a spherical cap angle (panel a) and diameter (panel b) obtained by AFM analysis

Table 29.1 Average values of nanoparticle shape and distribution

T, °C

C, nm

Q, degrees

z

200 225 250 275

30.32 31.78 25.09 25.29

34.11 49.66 64.75 72.89

1.20 0.99 0.72 0.65

Some overlapping of GNPs could be seen in Fig. 29.1(a), i.e. distance between upper points of some nanoparticles is less than sum of their diameters. This let us investigate the relationship between the annealing temperature and the distance between particles. To do this we introduce z parameter as the ratio of the total projection area of the nanoparticles onto the substrate plane to the sample surface area. When z value is close to 1 it means what there is large number of intersections, and also that the film has not transformed to separate particles yet. Statistical analysis of a model structures have been made to determine average size and shape of GNPs as a function of annealing temperature. Corresponding values of a spherical cap angle and diameter derived from the histograms shown in Fig. 29.3 are presented in the Table 29.1. It is seen from Table 29.1 that the annealing temperature increase leads to an increase in the distance between particles. A change in temperature in the range of 225–250° significantly increases the efficiency of particle formation. Indeed, spherical cap polar angle rapidly increases whereas diameter decreases.

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29.3.2 Optical Reflectance All the structures were used to determine the influence of GNP on reflection of the samples. Measured experimental spectra are shown in Fig. 29.4. Two well-known peaks at short wavelength part are present on the bare Si spectrum. It is clearly seen that reflection in this part of the spectrum is effectively suppressed even by not fully separated particles formed at 200 °C well separated GNP ensemble obtained at 250 °C and 275 °C is effective in reflection suppression in the whole spectral range. To understand the effect of nanoparticle shape and distribution on spectral characteristics we performed numerical simulation of reflection. Simulation results are presented in Fig. 29.5. Simulated reflectance spectra of bare silicon substrate and GNP covered structures are shown in Fig. 29.5(a). 4 nm thick native oxide layer was assumed on top of Si substrate. Suppression of reflected intensity is seen, which is in a good qualitative agreement with experimental data. On the other hand, additional reflection peak appear in the long wavelength part. Same, but less pronounced peak is seen in Fig. 29.4. Both experimental and simulated peaks shift to the shorter wavelengths and decreases with the formation of well separated and pronounced GNPs. We suppose high intensity in the simulated peak arises due to a well-ordered arrangement of nanoparticles. More work is needed to make this question clear. Figure 29.5(b) reveals that the intensity of the short wavelength reflection peaks decreases with increasing thickness of the native oxide layer. This can be used to determine native oxide thickness.

100

Reflected intensity (%)

Fig. 29.4 Reflectance spectra of bare silicon substrate and GNP structures obtained by annealing at different temperatures as indicated in the legend

Bare Si 200 oC 225 oC 250 oC 275 oC

90 80 70 60 50 40 30 20

200 300 400 500 600 700 800 900 1000 1100 1200

Wavelength (nm)

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60 50 40 30

a 400

600

800

Wavelength (nm)

1000

Reflected intensity (%)

Reflected intensity (%)

70 T=200 C T=225 C T=250 C T=275 C Model SiO2

70

h(SiO2) 0 nm h(SiO2) 2 nm h(SiO2) 10 nm h(SiO2) 15 nm

60

h(SiO2) 5 nm h(SiO2) 7,5 nm h(SiO2) 12,5 nm

50 40 30

b 400

600

800

1000

1200

Wavelength, nm

Fig. 29.5 Numerically simulated reflectance spectra of 4 nm thick native oxide on bare silicon substrate and GNP covered structures on the same SiO2 layer (a). Model structure corresponding to 250 °C on oxide layer of different thickness as indicated in the legend (b)

29.4

Conclusion

In conclusion, gold nanoparticle arrays were obtained on the surface of silicon by low-temperature (200–275 °C) annealing. Sample properties were studied by AFM and optical reflectance measurements. Particles start to form even at 200 °C but their intersection is significant. An increase in the annealing temperature leads to particle split and formation of more spherically shaped GNPs. The reflection intensity of the structures decreases with increasing annealing temperature. This can be due to change of GNP shape and in part can be associated with an increase in the thickness of the silicon dioxide layer. A model of an ideal periodic structure is constructed and corresponding reflection spectra were calculated. Results show good qualitative agreement between experimental and simulated spectra. Better description of the features seen in the experiment can be obtained within the model that takes into account particle size and shape distribution, as well as random nanoparticle dispersion. Acknowledgements The work was supported by the Russian Ministry of Science and Higher Education (project № FSRM-2020-009).

References 1. G.J. Hutchings, J.K. Edwards, Application of gold nanoparticles in catalysis. Front. Nanosci. 3, 249–293 (2012) 2. D.T. Thompson, Using gold nanoparticles for catalysis. Nanotoday 2, 40–43 (2007) 3. P. Suchomel, L. Kvitek, R. Prucek, A. Panacek, A. Halder, S. Vajda, R. Zboril, Simple size-controlled synthesis of Au nanoparticles and their size-dependent catalytic activity. Sci. Rep. 8(1), 4589 (2018) 4. J. Wang, Electrochemical biosensing based on noble metal nanoparticles. Microchim. Acta 177, 245–270 (2012)

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5. H. Malekzad, P.S. Zangabad, H. Mirshekari, M. Karimi, M.R. Hamblin, Noble metal nanoparticles in biosensors: recent studies and applications. Nanotechnol. Rev. 6(3), 301–329 (2017) 6. H.A. Atwater, A. Polman, Plasmonics for improved photovoltaic devices. Nat. Mater. 9, 205– 213 (2010) 7. C.F. Guo, T. Sun, F. Cao, Q. Liu, Z. Ren, Metallic nanostructures for light trapping in energy-harvesting devices. Light Sci. Appl. 3(1–12), e161 (2014) 8. M. Dhiman, A. Maity, A. Das, R. Belgamwar, B. Chalke, Y. Lee, K. Sim, J.-M. Nam, Y. Polshettiwa, Plasmonic colloidosomes of black gold for solar energy harvesting and hotspots directed catalysis for CO2 to fuel conversion. Chem. Sci. 10, 6594–6603 (2019) 9. D. Singh, S. Juneja, A. Ghosal, Energy harvesting: role of plasmonic nanocomposites for energy efficient devices, in Integrating green chemistry and sustainable engineering, ed. by C. Ul-Islam (Wiley, Hoboken, 2019) 10. S.A. Maier, Plasmonics: fundamentals and applications (Springer, Cham, 2007) 11. M. Sengani, A.M. Grumezescu, V. Devi Rajeswari, Recent trends and methodologies in gold nanoparticle synthesis – a prospective review on drug delivery aspect. OpenNano 2, 37–46 (2017) 12. G. Gupta et al., Absorption spectroscopy of gold nanoisland films: optical and structural characterization. Nanotechnology 20(2), 025703 (2009) 13. R. Lo Savio et al., Control of the micrometric scale morphology of silicon nanowires through ion irradiation-induced metal dewetting. J. Phys. D Appl. Phys. 44(12), 125302 (2011) 14. I. Saleem, B.P. Tilakaratne, Y. Li, J. Bao, D.N. Wijesundera, W.-K. Chu, Cluster ion beam assisted fabrication of metallic nanostructures for plasmonic applications. Nucl. Instrum. Methods Phys. Res. B 380, 20–25 (2016) 15. M.S. Tuzhilkin, P.G. Bespalova, M.V. Mishin, I.E. Kolesnikov, K.V. Karabeshkin, P.A. Karaseov, A.I. Titov, Formation of Au nanoparticles and features of etching of a Si substrate under irradiation with atomic and molecular ions. Semiconductors 54(1), 137–143 (2020) 16. A.I. Titov, P.A. Karaseov, AYu. Azarov, S.O. Kucheyev, Effects of the density of collision cascades: separating contributions from dynamic annealing and energy spikes. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 267(16), 2701–2704 (2009) 17. C.V. Thompson, Solid-state dewetting of thin films, in Annual review of materials research, vol. 42, ed. by D.R. Clarke (Palo Alto, San Francisco, 2012), pp. 399–434 18. C.M. Müller, R. Spolenak, Dewetting of Au and AuPt alloy films: a dewetting zone model: J. Appl. Phys. 113 (2013). 13 p. 094301 19. F. Niekiel, P. Schweizer, S.M. Kraschewski, B. Butz, E. Spiecker, The process of solid-state dewetting of Au thin films studied by in situ scanning transmission electron microscopy. Acta Mater. 90, 118–132 (2015) 20. M.C.A. Fantini, F.F. Ferreira, A. Gorenstein, Theoretical and experimental results on Au – NiO and Au – CoO electrochromic composite films. Solid State Ionics 153, 867–872 (2002) 21. M.V. Mishin, et al., The mechanism of charge carrier generation at the TiO 2 — n-Si heterojunction activated by gold nanoparticles. Semicond. Sci. Technol. 33 (2018). 075014 9 p 22. M.V. Mishin, et al. Substrate modification influence on properties of nanocomposite based on TiO2 and gold nanoparticles. J. Phys. Conf. Ser. 1236(1) (2019). 8 p. 012025 23. P.G. Bespalova, A.A. Vorobyev, S.A. Kondrateva, M.V. Mishin, Degradation of NiO-GNPs electrochromic coating, in Proceedings of 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), December 2019. https://doi.org/10.1109/ eexpolytech.2019.8906795 24. P.G. Bespalova, A.A. Vorobyev, T.S. Kunkel, A.L. Shakhmin, M.V. Mishin, Characterization of iron oxide coatings prepared by MOCVD method from Fe(CO)5, in Materials Today : Proceedings, Accepted paper (2020). https://doi.org/10.1016/j.matpr.2019.12.391 25. C. Yan et al., Molecule oxygen-driven shaping of gold islands under thermal annealing. Appl. Surf. Sci. 258, 377–381 (2011)

Chapter 30

Computer Modeling of Fiber Optic Current Sensor Valentina Temkina , Andrei Medvedev , and Alexey Mayzel

Abstract Despite all the advantages of using the fiber optic current sensors in high-voltage power networks, this technology has the significant weaknesses. They are the instability of the sensor readings and insufficient measurement accuracy. We observed parasitic response to the set of the entire factors in the real prototype of the fiber optic current sensor. Development of the current sensor computer model allowed us to analyze the influence of each factor on the measurement result individually and in complex without reconstructing the optical scheme every time. We used LabVIEW programming environment and Jones matrices formalism to develop the model of the sensor. We tested an algorithm that uses additional sinusoidal modulation and harmonic ratio analysis. The real-time model generated the raw photodetector signal that arises at the output of the sensor optical scheme when magnetic field is exposed to the sensing element, as well as demodulated signal of the current sensor. We have studied the effect of polarization mismatches at the fiber joints, the imperfections of the quarter-wave plate and the reflector at the measurements accuracy. Our model makes it easy to reconfigure the optical scheme of the current sensor to explore implementations of different configurations.




Keywords Fiber optic current sensor Current measuring Jones matrices formalism LabVIEW





Computer model


V. Temkina (&)
A. Medvedev
A. Mayzel Peter the Great St. Petersburg Polytechnic University (SPbPU), Polytechnicheskaya, 29, St. Petersburg 195251, Russia e-mail: [email protected] A. Medvedev e-mail: [email protected] A. Mayzel e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_30

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Introduction

The evolution of Smart Grid networks and the foundation of digital substations entail the unavoidable replacement of legacy electromagnetic current transformers with digital measuring devices. For this reason, optical current transformers have been rapidly developed recently. These transformers are designed to measure with high accuracy the amplitude and spectral distribution of the industrial frequency current and convert the measured values into a digital data stream in accordance with the standard IEC 61850-9-2(2011) for use by automated systems [1–6]. The primary current sensing element of optical current transformers is a special fiber that operates basing on the effect of light modulation in a specific media (spun fiber) when it is exposed to an external magnetic field (the Faraday Effect), which is created by a current flowing through the conductor [7]. As a result, the signal proportional to the measured current is generated at the output of the fiber-optic current sensor (FOCS). This sensor is designed for voltage classes 110–220 kV and above, however, it can be used at lower voltages. The FOCS is characterized by high sensitivity and immunity of the optical circuit to electromagnetic interference, except for the sensitive element. It has a small size and weight. In addition, the entire measuring (optical) part of the FOCS, which is located in the high voltage region, is made of dielectric materials. It ensures a minimum probability of electrical breakdown. However, despite all the advantages of using of the fiber optic current sensors in high-voltage power networks, this technology has the significant weaknesses [8, 9, 10]. First of all, specialists are faced with the instability of the sensor readings and insufficient measurement accuracy. They are caused by many factors such as external noise, temperature, imperfections of optical scheme, amplitude deviation of modulator etc. [11–18]. It is necessary to exclude each of these influences to improve the sensor accuracy and stability. However, we observe parasitic response to the set of the entire factors in the real prototype of the FOCS. Therefore, development of the current sensor computer model is relevant because it allows us to analyze the influence on the measurement result of each factor individually and in complex without reconstructing of the optical scheme each time. Another weak point of the FOCS is the cost. The purpose is to create a device that will be cheaper than its analogues. The computer model of the FOCS is also useful in this case, because makes it easy to reconfigure the optical scheme of the current sensor to explore implementations of different configurations.

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30.2

265

Mathematical Description of the Fiber Optic Current Sensor Circuit

We have applied the formalism of the Jones matrices for the modeling of the FOCS based on a well-known optical scheme (see Fig. 30.1). The sensor operation principle and a detailed description of the optical scheme are presented in [7]. The elements located between points 2 and 8 of the optical scheme are made from polarization-maintaining (PM) fiber. The quarter-wave plate is also made from PM fiber, but with a large value of the beat length. The sensitive fiber preserves circular polarization of light. This is spun high-birefringence fiber. According to the Jones matrices formalism, each element of the optical scheme in Fig. 30.1 can be represented by the matrix 2  2, which describes the transformation of the polarization state of light propagating through the element. Then the common matrix [T] of the entire optical system can be obtained by successive multiplication of the matrices of each individual element in the order opposite to the direction of light propagation in the scheme. T ¼ R10
R2
P
R3
R4
M
R5
R6
Fdl
R7
R8
Fk=4
R9
S
Fmir
S
R9
Fk=4
R8
R7
Fdl
R6
R5
M
R4
R3
P
R2
R1 ;

ð30:1Þ wherein [R1−10] is the rotation matrix by angle a1−10, [P] is the polarizer matrix, [M] is the modulator matrix, [Fdl] is the delay line matrix, [Fk/4] is the quarter-wave plate matrix, [S] is the sensor matrix, [Fmir] is the mirror matrix. The numbers 1–10 indicate the junction of optical elements or fiber splicing in accordance with Fig. 30.1. When light propagates in an opposite direction through optical elements, it is necessary to transform their matrices. Let the element matrix in a forward direction be described by the equation  B¼

b11 b21

 b12 : b22

ð30:2Þ

When light passes backwards through a reciprocal optical element, its matrix is transposed and off-diagonal elements of the matrix are inverted [19]

Fig. 30.1 Optical scheme of the fiber optic current sensor. The numbers 1–10 indicate the junction of optical elements or fiber splicing

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b11 B¼ b12

 b21 : b22

ð30:3Þ

It should be noted that we have used the basis of linear polarizations to form Jones matrices. In this context, the sensitive fiber was described by the nonreciprocal rotation matrix since the plane of linear polarization of light in this fiber is rotated by a certain angle due to the Faraday Effect. A birefringence modulator and a quarter-wave plate were described by phase plate matrices. When a light wave falls at an angle of 90° on a mirror and is reflected from it, the direction of light propagation changes to the opposite. Therefore, in order to preserve a right-handed coordinate system, it is necessary to retain the direction of one axis and change the direction of one another by 180°. In other words, the mirror matrix in a linear coordinate system can be represented as the matrix of a half-wave plate [19]. In addition, rotation matrices [R1−10] were introduced to consider the rotation of the coordinate axes of any optical element relative to another element or the relative selected basis. As well as these matrices were necessary to explore the influence of splicing fibers or junction of element with the polarization axes mismatch on the accuracy of FOCS measurements. Thus, when all the optical elements are ideal and a1 = a2 = a4 = a5 = a6 = a7 = a10 = 0°, a3 = a8 = 45°, a9 = − 45°, the common matrix of the system [T] is determined by Eq. (30.4). Here we have taken into consideration the main function of the fiber delay line in the modulator matrices. The delay line should provide such a delay between the light waves propagating in the forward and reverse directions that during the wave passing from the phase modulator to the mirror and back, the phase of the modulating voltage changes sign to the opposite.  ½T  ¼





    1  1 0 cos 45  sin 45 e2jumod 0

1 0 0 sin 45 cos 45 e2jumod    jp 0      cos 45  sin 45 sin 45 cos 45 4 0 e

jp sin 45 cos 45  sin 45 cos 45 0 e 4     uF uF  uF cos 2 cos 2  sin u2F sin 2 1 0

 sin u2F cos u2F sin u2F cos u2F 0 1       jp   sin 45 cos 45 e 4 0
cos 45  sin 45
jp  sin 45 cos 45 sin 45 cos 45 0 e4  1        2jumod cos 45  sin 45 1 0 0 e

; 1 sin 45 cos 45 0 0 0 e2jumod

ð30:4Þ

where in umod —the phase shift between two orthogonal linearly polarized modes introduced by the modulator, uF =2—the rotation angle of the plane of linear polarization of light, which is proportional to the current in the conductor.

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It is possible to link the electric field vector components of the beam passed through the FOCS [Dout] with the vector components of the source beam [Din] using a common matrix of the element system [T] ½Dout  ¼ ½T 
½Din :

ð30:5Þ

Then the intensity of the light beam is calculated using the following equation  T I ¼ Dout
½Dout :

ð30:6Þ

To obtain the measured current signal, the calculated intensity was subjected to digital phase detection, based on the ratio analysis of modulation frequency harmonics [7].

30.3

Computer Modeling of the Fiber Optic Current Sensor

30.3.1 Building of the Fiber Optic Current Sensor Model in LabVIEW Simulation of the FOCS optical circuit, as well as the signal detection algorithm, was implemented in the LabVIEW programming environment. The advantages of this approach are visibility and the ability to add, exclude or replace various optical elements without rewriting the program code. In addition, it is not always possible to conduct an analytical evaluation of the parasitic influence on the FOCS accuracy. Measurement error and its analysis are significantly complicated by the presence of all harmful factors simultaneously. In this regard, the proposed FOCS modeling simplifies the research. It does not require the analytical formula construction, it is necessary only to set parameters for external influences. Each matrix in Eq. (30.4) was modeled as a separate virtual instrument (VI). For example, Fig. 30.2 shows the program code that forms the modulator matrix. It is stored in LabVIEW as a separate VI, a function, which will later be called in the higher-level program for the FOCS model. Figure 30.3 shows a part of the top-level program for the FOCS model. It demonstrates the matrix of circuit elements as VI and their connection in the common program. The matrices of each element are located in separate frames, which can be easily added not only to the edges of the common system, but also to the middle, and can also be easily removed from it without the program disintegrating.

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Fig. 30.2 Program code in LabVIEW for forming the modulator matrix

Fig. 30.3 Fiber optic current sensor modeling in LabVIEW (the part of block diagram)

The FOCS model used additional phase modulation with a 40 kHz harmonic signal and digital phase detection. A harmonic signal with a frequency of 50 Hz was set as a signal simulating an electric current and acting on a sensitive element. Digital processing of the interference signal generated at the output of optical scheme carried out based on the analysis method of the harmonics ratio of modulation frequency [7]. Thus, the real-time model generated the raw photodetector signal that arises at the output of the sensor optical scheme when magnetic field is exposed to the sensor element, as well as demodulated signal that characterized this effect (see Fig. 30.4).

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Fig. 30.4 Interference signal (on the right), demodulated signal and its spectrum (on the left), obtained during the fiber optic current sensor modeling

Based on Fig. 30.4, it can be seen that the demodulated signal is identical to the measured harmonic current signal with a frequency of 50 Hz. The developed FOCS model can be expanded. For example, we can add noise to the system, polarizing mismatches at the junctions of fibers, take into account the imperfection of the quarter-wave plate and mirror, and investigate the influence of these factors on the accuracy of FOCS measurements.

30.3.2 Research of the Influence of Polarization Mismatches on the Accuracy of FOCS Measurements The case of polarization mismatches occurrence in the circuit element located after modulator and before the sensitive fiber, namely at point 5 of the FOCS optical circuit, was investigated. For this purpose, we assume a5 6¼ 0 (see Fig. 30.1). Using the developed model of the FOCS, the dependence of the measured current amplitude error and the total harmonic distortion on the angle of mismatch at point 5 was obtained (see Fig. 30.5). The demodulated signal and its spectrum in the ideal case and with polarization mismatch by 10° demonstrated on the Fig. 30.6.

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Fig. 30.5 Current amplitude error vs angle of polarization mismatch (on the left). The total harmonic distortion vs angle of polarization mismatch (on the right)

Fig. 30.6 Demodulated current signal and its spectrum. The angle of polarization mismatch is 0° (on the left) and 10° (on the right)

The study showed that the greater the mismatch angle, the more distorted the demodulated current signal is. Namely, the error of the current amplitude increases and the third and fifth harmonics of the current appear in the spectrum of the demodulated signal. In addition, the average signal level at the output of the optical circuit (interference signal) decreases. According to the technical documentation, it is known that the FOCS must meet the accuracy class 0.2. This means that the current amplitude error must not exceed 0.2%. Thus, we can conclude that the permissible tolerance of polarization mismatches at one of the optical circuit points located after the modulator and before the sensitive fiber is ±2° to achieve such accuracy of the FOCS. The more different junctions of fiber elements in the optical scheme, the smaller the permissible tolerance of polarization mismatches for each point is.

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30.3.3 Research of the Quarter-Wave Plate and Mirror Imperfections Influence on the Accuracy of FOCS Measurements The fiber quarter-wave plate is one of the most important elements of the FOCS optical circuit. It is used to convert linearly polarized light modes into circular polarized modes. This is necessary because the circular polarized optical radiation is preserved in the sensitive fiber. In addition, when reflected from a mirror, modes with circular polarization change the direction of rotation to the opposite. So when the light passes back through the quarter-wave plate, the x-mode turns into the y-mode and vice versa. As a result, the sensor’s current sensitivity is doubled. A quarter-wave plate is a piece of birefringent fiber only a few millimeters in size, but the accuracy of the overall system depends on its precision. During the research, it was found that the incorrect manufacturing of a quarter-wave plate, expressed in a mismatch in its length, leads to a significant increase in the amplitude error when measuring current and the appearance of nonlinear distortions (see Figs. 30.7 and 30.8). In contrast to polarizing mismatch influence at the points of element junctions or fiber splicing, the quarter-wave plate also affects the contrast of the interference signal at the optical circuit output (see Fig. 30.9). The signal contrast was calculated using the equation V¼

Imax  Imin
100%; Imax þ Imin

ð30:7Þ

where I is the intensity of signal at the optical circuit output.

Fig. 30.7 Current amplitude error (on the left) and Total harmonic distortion (on the right) vs. Length of phase plate

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Fig. 30.8 Demodulated current signal and its spectrum in the case of ideal quarter-wave plate (on the left) and a phase plate with a length of 0.1k (on the right)

Fig. 30.9 Contrast of the interference signal vs length of phase plate

This shows that non-ideal quarter-wave plates leads to a reduction of the signal contrast, and thus limit the dynamic range of the current sensor. It is due to the fact that modes with elliptical polarization are formed at the output of the phase plate. These modes are not converted to orthogonal modes when reflected from a mirror. As a result, when light passes backwards through the phase plate, the output is represented again with elliptically polarized modes, rather than linear ones. The conclusion of this research is that the permissible tolerance of the incorrect of the quarter-wave plate length is ± 0.01k to achieve the FOCS accuracy class 0.2. With such tolerance, the contrast of the interference signal is reduced by only 0.62%. In turn, the reflection coefficient of the mirror affects only the average signal level at the output of the optical circuit under normal light incidence.

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30.3.4 Reconfiguration of the FOCS Optical Circuit The FOCS model developed in the LabVIEW programming environment and based on the Jones matrices formalism allows us to easily reconfigure the optical circuit of the current sensor to explore implementations of different configurations. This is possible because the matrix of each element is programmed as a separate virtual instrument in the model, which must be connected in a certain sequence in the common system. In this way, we can model a different FOCS optical circuit than the classic one without rewriting the program code. For example, there is the FOCS circuit, working as “pass-through” scheme (see Fig. 30.10). The light is propagated only in a straight direction in this scheme. However, it will lead to a loss of sensitivity. In this case, the common matrix of the system is described by the following equation T ¼ R12
R11
P
R10
R9
Fk
R8
S
R7
Fk
R6
R5
M
R4
R3
P
R2
R1 : 4

4

ð30:8Þ Here a1 = a2 = a4 = a5 = a11 = a12 = 0°, a3 = a6 = a9 = a10 = 45°, a7 = a8 = −45°. Fig. 30.10 “Pass-through” optical scheme of the fiber optic current sensor

Fig. 30.11 Interference signal (on the right), demodulated signal and its spectrum (on the left), obtained during the modeling of the “pass-through” current sensor optical scheme

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Fig. 30.12 Demodulated current signal and its spectrum in the case of ideal quarter-wave plate (on the left) and a phase plate with a length of 0.1k (on the right) for the current sensor “pass-through” optical scheme

We transformed the sensor model according to the “pass-through” scheme by simple manipulations of the program code, namely removing unnecessary frames and connecting virtual instruments in a different sequence. The modeling result is demonstrated in Fig. 30.11. The research of the incorrect quarter-wave plate influence on the measurement error of the current sensor showed that the current amplitude error is negative and the signal form is significant distorted (see Fig. 30.12).

30.4

Conclusion

The fiber-optic current sensor model based on the Jones matrices formalism was developed and implemented in the LabVIEW programming environment. This allowed us to investigate the influence of each entire factor on the measurement accuracy individually, which is not possible in a real sensor prototype. The research showed that the introduction of polarization mismatches in the element junctions and fiber splicing leads to an increase in the current amplitude error, as well as the rise of high current harmonics amplitudes and the appearance of nonlinear distortions. If the quarter-wave plate is incorrect, nonlinear distortions are more pronounced. There is a significant increase in the current amplitude error. In addition, the imperfection of the quarter-wave plate greatly impairs the contrast of the interference signal, which leads to a decrease in the dynamic range of the sensor. The degradation of the reflector in the circuit leads to a reduction in the interference signal level. The developed FOCS model in LabVIEW also makes it easy to reconfigure the optical scheme of the current sensor to explore implementations of different configurations.

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References 1. P. Ripka, Electric current sensors: a review. Meas. Sci. Technol. 21(11), 112001 (2010) 2. F. De Nazaré, M. Werneck, Hybrid optoelectronic sensor for current and temperature monitoring in overhead transmission lines. IEEE Sens. J. 12(5), 1193–1194 (2012). 5976366 3. D. Kiesewetter, V. Malyugin, A. Reznik, N. Zhuravleva, Spectral-correlation method of investigation of highvoltage electrical insulation components, in 2018 IEEE XXVII International Scientific Conference Electronics - ET, (IEEE, Sozopol, 2018) pp. 1–3 4. R. Decosta, B. Altschul, Mode analysis for energetics of a moving charge in Lorentz- and CPT-violating electrodynamics. Phys. Rev. D 97(5), 055029 (2018) 5. A. Boev, V. Gubin, S. Morshnev, J. Przhijalkovskij, M. Rjabko, A. Sazonov, N. Starostin, J. Chamorovskij, Fibre-optic current sensor. Patent RU 2437106 C2, G01R 15/24, G01R 19/ 2. Date of publication: 20.12.2011 Bull. 35 6. F. Rahmatian, J.N. Blake, Applications of high-voltage fiber optic current sensors, in 2006 IEEE Power Engineering Society General Meeting (2006) 7. V. Temkina, A. Medvedev, A. Mayzel, A. Mokeev, Fiber optic current meter for IIoT in power grid, in NEW2AN 2018, ruSMART 2018 by O. Galinina, S. Andreev, S. Balandin, Y. Koucheryavy (eds.), LNCS, vol. 11118, (Springer, Cham, 2018), pp. 631–640 8. P. Agruzov, I. Pleshakov, E. Bibik, S. Stepanov, A. Shamray, Fast magnetooptical modulation in optofluidic devices based on ferrofluid filled microstructured optical fibers, in 2015 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, paper CK_P_13 (OSA, 2015) 9. A. Vlasov, Y. Plotnikov, A. Ashirov, A. Aleynik, A. Varlamov, A. Stam, The method for protection of sensitive fiber optic components from environmental noise and vibration impacts, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), (IEEE, St. Petersburg, 2019), pp. 305–307 10. M. Shlyagin, P. Agruzov, I. Pleshakov, A. Prokofiev, E. Bibik, Incident-power-dependent refractive index of ferrofluid in magnetic field measured with a fiber optic probe. Optik 186, 418–422 (2019) 11. A. Varlamov, M. Plotnikov, A. Aleinik, P. Agrusov, I. Il’ichev, A. Shamray, A.A. Vlasov, Acoustic vibrations in integrated electro-optic modulators on substrates of lithium niobate Tech. Phys. Lett. 43 994–997 (2017) 12. A. Kostromitin, L. Liokumovich, K. Muravyov, P. Skliarov, Methods for measuring the auxiliary modulation step in interferometric fiber optic sensor. J. Phys: Conf. Ser. 1326, 012016 (2019) 13. L. Liokumovich, K. Muravyov, P. Skliarov, N. Ushakov, Signal detection algorithms for interferometric sensors with harmonic phase modulation: miscalibration of modulation parameters. Appl. Opt. 57, 7127–7134 (2018) 14. L. Liokumovich, A. Medvedev, K. Muravyov, P. Skliarov, N. Ushakov, Signal detection algorithms for interferometric sensors with harmonic phase modulation: distortion analysis and suppression. Appl. Opt. 56, 7960–7968 (2017) 15. A. Kostromitin, P. Skliarov, L. Liokumovich, N. Ushakov, Laser frequency noise measurement by forming an interference signal with subcarrier frequency, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), (IEEE, St. Petersburg, 2019), pp. 336–338 16. M. Lenner, A. Frank, L. Yang, T. Roininen, K. Bohnert, Long-term reliability of fiber-optic current sensors. IEEE Sens. J. 20(2), 823–832 (2019)

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17. W. Wang, X. Wang, J. Xia, The nonreciprocal errors in fiber optic current sensors. Opt. Laser Technol. 43(8), 1470–1474 (2011) 18. G. Müller, L. Yang, B. Gülenaltin, A. Frank, K. Bohnert, Temperature compensation of fiber-optic current sensors, in Proceedings of SPIE - The International Society for Optical Engineering, vol. 9157, p. 915705 (2014) 19. R. Bhandari, Transpose symmetry of the Jones matrix and topological phases. Opt. Lett. 33 (8), 854–856 (2008)

Chapter 31

Photometry Setup for Dynamic Dye Concentration Measurement Ilya Kolokolnikov , Ilya Lavrenyuk , Ekaterina Savchenko , Maksim Baranov , and Elena Savchenko

Abstract Human body has a high transmittance coefficient for near infrared light. Therefore, dyes with notable absorbance peaks in the near infrared range have a variety of applications in diagnostics. Whereas one of the main liver functions is blood purification, the rate of dye removal from the blood is a direct indicator of liver function. Indocyanine green is used to measure liver function with plasma disappearance rate parameter. Dynamic measurement of dye concentration in human blood in vivo allows calculation plasma disappearance rate. Thereby, our setup is intended to be applied for liver function assessment. Indocyanine green changes its absorbance spectra when bounded with plasma proteins. To simulate conditions of a medical test we use 10% albumin solution as a medium for indocyanine green. Bounded with albumin indocyanine green has absorption peak on 805 nm, similar to this bounded in human blood plasma, while in water solution indocyanine green has absorption peak on 790 nm. Absorption analysis method of solution concentration determination is considered. We present automated photometry setup for measurement of the dye concentration in solution.




Keywords Absorbance Near infrared disappearance rate Liver function





Indocyanine green
Plasma

I. Kolokolnikov (&)
I. Lavrenyuk
E. Savchenko
M. Baranov Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya St., St. Petersburg 195251, Russia e-mail: [email protected] E. Savchenko Computer Information Systems, International University of Kyrgyzstan, 17A/1 Tolstoy St., Bishkek 720007, Kyrgyz Republic © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_31

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Introduction

In this work we operate with indocyanine green which is a dye widely used in medical practice to measure cardiac output, to diagnose circulatory disorders, to visualize the vascular retina of the eye, to assess liver function, etc. [1, 2]. Medical applications of indocyanine green are provided by the fact that indocyanine green has a high absorbance peak in the near infrared, and most human body tissues have a high transmittance in the near infrared [3]. Liver function estimation by measuring dye elimination rate in blood is a diagnostical instrument of high importance and precision for hepatic diseases research [4]. The indocyanine green clearance test prognostic value was approved for such significant diseases as cirrhosis, diabetes, oncological diseases etc. Besides, plasma disappearance rate of indocyanine green is used for perioperative observance. Indocyanine green is also fluorophore with high emission peak in near infrared [5]. Accordingly, fluorescence analysis method could be applied. Different optical methods are used in medical diagnostics [4, 6–9]. Nevertheless, due to intention to use method for concentration measurements in vivo absorption analysis [10] method was chosen because of it less requirements to the conditions of the test.

31.2

Materials and Methods

Absorption analysis method is based on the main radiation absorption law, named Beer–Lambert–Bouguer law which describes the attenuation of light passing through the medium depending on medium properties. The mathematical expression of the law contains an exponential dependence between intensity of light transmitted through the absorbing layer and such properties of the layer as width, absorbing substance concentration and coefficient for substance spectral properties [11] I ¼ I0 expðkczÞ

ð1Þ

where I0—reference light intensity for zero concentration or without absorption layer, z—light path length through tested medium, I—intensity of light traveled distance z through the absorption layer, c—solute concentration, k—coefficient describing light absorption by one particle depending on wavelengths. Absorbance is described as minus decimal logarithm of transmittance [11]     I I0 A ¼ lg ¼ lg I0 I

ð2Þ

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279

where A is absorbance and expressed by [12] A ¼ ecz;

ð3Þ

where e is absorptivity. To study the concentrations of indocyanine green in the laboratory, a measuring setup was developed (Fig. 31.1). Light emission diode is chosen to coincide indocyanine green absorptivity spectra in the near infrared. Thereby it ought to emit at wavelengths as close as possible to 805 nm. In this way the most suitable option found is LED810L with emission peak on 808 nm. Emission light from a diode passes though the test solution. Part of the radiation is absorbed, and part of the radiation is backscattered [13]. A photodetector with I2C interface is used to detect transmitted radiation. Applied photodetector consists of two photodiodes, corresponding to different channels, and integrating ADCs. The resolution of ADCs is 16-bit, and the sample rate is 400 kHz. Each channel can be processed separately and independently. First channel covers visible and infrared diapasons and has maximum of responsivity at 700 nm, second channel covers only infrared diapason and has maximum of responsivity at 800 nm. The sensitivity of the system is controlled by adaptive analog gain settings. The combining and processing of both outputs could help to enhance the performance of the overall system. As long as the research involves many similar experiments, in a large number of iterations, with a measurement frequency of several times per second and, therefore, too large data volumes for manual processing, the experiment process had to be automated. A board based on the STM32F103C8T6 microcontroller is selected to be setup controller. The choice was made according to the following parameters: this board is one of the cheapest ready-made implementations of STM32 microcontrollers, it has an extremely compact design, and at the same time it fully complies with low demands on the frequency of operations. In the experimental setup the data acquisition occurred with a period of 50 ms. This is equal to 20 measurements per second, which is enough for the accurate estimation of dynamics of changes in the concentration of indocyanine green. Received signal is distorted by high-frequency components (pulse) and by DC component (constant level of illumination of the environment). At the stage of signal processing received signal was filtrated by the Butterworth low pass filter and median filter. Fig. 31.1 The experimental setup: 1—cuvette; 2—emitting diode; 3—photodetector; 4—controller; 5—USB interface

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Results and Discussion

The instrumental accuracy of the method corresponds to the quantization error of the ADC of the photodetector. Figure 31.2 shows an example of a signal obtained during the measurement of a dyed 20% albumin solution. Fluctuations in optical density can be conditioned by fluctuations of dye concentration, intensity measurement error is mostly determined by photodetector quantization error. The measurement result is calculated by averaging the signal values [14–16]. Figure 31.3 presents the results of measurements of indocyanine green solutions of various concentrations and an exponential approximation of the observed dependence of the transmitted light intensity on concentration.

Fig. 31.2 Example of the photodetector signal upon measurement process demonstrating absorption fluctuations of the tested medium

Fig. 31.3 Experimental concentration dependence of transmitted light intensity and exponential approximation

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Ten series of samples were applied in measurements to observe concentration dependence of transmitted light intensity and to calculate standard deviation. The mismatch of experimental dependence with the exponent at low concentration may result from the use of a non-monochromatic radiation source. At higher concentrations, the experimental dependence is close to the expected one.

31.4

Conclusion

In this work the photometry setup for measurement of the dye concentration in solution was presented. To obtain experimental results on the estimation of green indocyanine concentration in various solutions, the methodology for experimental studies and the special program was developed for collecting and outputting data. In experimental studies measurements were made with 20% albumin solution as a medium for indocyanine green. Bounded with albumin indocyanine green has absorption peak at 805 nm, similar to this bounded in human blood plasma, while in water solution indocyanine green has absorption peak at 790 nm. Absorption analysis method of solution concentration determination is considered. The theory we suggested allow concentration measurements by referent method. The preliminary results obtained for assessing the concentration of indocyanine green in various fluids can serve as the basis for further studies [12]. This development can be included in the Smart Medical Autonomous Distributed System for Diagnostics Based on Machine Learning technology [17]. In further work we plan to use finger-piece design of setup for measurements with patients during medical tests. Acknowledgements This research work was supported by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020”.

References 1. J.T. Alander, A review of indocyanine green fluorescent imaging in surgery. Int. J. Biomed. Imaging (2012) 2. U. Mayr, L. Fahrenkrog-Petersen, G. Batres-Baires, et al., Large-volume paracentesis effects plasma disappearance rate of indocyanine green in critically ill patients with decompensated liver cirrhosis and intraabdominal hypertension. Ann. Intensive Care 78 (2018) 3. E. Levesque, et al., Current use and perspective of indocyanine green clearance in liver diseases. Anaesth. Crit. Care Pain Med. (2015) 4. A. De Gasperi, E. Mazza, M. Prosperi, Indocyanine green kinetics to assess liver function: ready for a clinical dynamic assessment in major liver surgery. World J. Hepatol. 8(7), 335 (2016) 5. O.B. Kuznetsova, E.A. Savchenko, A.A. Andryakov, E.Y. Savchenko, Z.A. Musakulova, Image processing in total internal reflection fluorescence microscopy. J. Phys: Conf. Ser. 1236 (1), 012039 (2019)

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6. E.K. Nepomnyashchaya, E.N. Velichko, E.T. Aksenov, T.A. Bogomaz, Laser correlation spectroscopy for immune testing, in International Conference Laser Optics (ICLO) (2018), p. 561 7. R.V. Davydov, V.I. Antonov, V.V. Yushkova, V.V. Davydov, Y. Rud’, K.J. Smirnov, A new method of processing a pulse wave in rapid diagnosis of the human health. J. Phys: Conf. Ser. 6, 066037 (2019) 8. S.E. Logunov, V.V. Davydov, M.G. Vysoczky, M.S. Mazing, New method of researches of the magnetic fields force lines structure. J. Phys: Conf. Ser. 1038, 012093 (2018) 9. V. Privalov, V.G. Shemanin, On the determination of the minimum pulse energy in laser probing using harmonics of an Nd: YAG laser. Opt. Spectrosc. 82, 809–811 (1997) 10. V.E. Privalov, V.G. Shemanin, Optimization of a differential absorption and scattering lidar for sensing molecular hydrogen in the atmosphere. Tech. Phys. 44, 928–931 (1999) 11. H. Abitan, H. Bohr, P. Buchhave, Correction to the Beer-Lambert-Bouguer law for optical absorption. Appl. Opt. 47(29), 5354–5357 (2018) 12. V.E. Privalov, A.V. Rybalko, P.V. Charty, V.G. Shemanin, Effect of noise and vibration on the performance of a particle concentration laser meter and optimization of its parameters. Tech. Phys. 52, 352–355 (2007) 13. M.A. Bisyarin, O.I. Kotov, A.H. Hartog, L.B. Liokumovich, N.A. Ushakov, Influence of a variable Rayleigh scattering-loss coefficient on the light backscattering in multimode optical fibers. Appl. Opt. 56, 4629 (2017) 14. A. Pergushev, V. Sorotsky, A. Ulanov, Criteria for selection envelope tracking power supply parameters for high peak-to-average power ratio applications, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (2019), pp. 13–16 15. A. Pergushev, V. Sorotsky, Signal distortion decreasing in envelope tracking power amplifiers, in 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (2018), pp. 44–47 16. A. Pergushev, V. Sorotsky, A. Ulanov, Output voltage PWM conversion inaccuracies in envelope tracking power supply for high peak-to-average power ratio applications, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (2019), pp. 9–12 17. E. Velichko, E. Nepomnyashchaya, M. Baranov, M.A. Galeeva, V.A. Pavlov, S.V. Zavjalov, E. Savchenko, T.M. Pervunina, I. Govorov, E.A. Komlichenko, Concept of smart medical autonomous distributed system for diagnostics based on machine learning technology. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11660 LNCS (2019) pp. 515–524

Chapter 32

Estimation of Nanoparticles Sizes by Laser Correlation Spectroscopy Methods Zoja Zabalueva , Elina Nepomnyashchaya , Elena Velichko , Ge Dong , and Tatyana Kudryashova Abstract Analysis of nanoparticles sizes in suspensions is a dramatically important task nowadays because of mainstreaming of nano dispersed liquids. In this paper, we suggest the cross-correlation method and device for nanoparticle size estimating in monodisperse suspensions. It is based on dynamic laser light scattering modified by two-channel detection for reduction of multiple scattering noises. We obtained several experimental cross-correlation and autocorrelation functions for aqueous suspensions of latex microspheres of different diameters. The decay rate of the received cross-correlation functions coincides with the decay rate of autocorrelation functions and determines particles diameter in low-concentrated suspensions. Since the cross-correlation method, in contrast to the autocorrelation method, allows one to study turbid suspensions without dilution, that has great prospects for assessing the dispersion of liquid samples of different degrees of turbidity. Keywords Cross-correlation


Autocorrelation
Particle size estimating

Z. Zabalueva (&)
E. Nepomnyashchaya
E. Velichko
T. Kudryashova Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia e-mail: [email protected] E. Nepomnyashchaya e-mail: [email protected] E. Velichko e-mail: [email protected] T. Kudryashova e-mail: [email protected] G. Dong School of Aerospace Engineering, Tsinghua University, Beijing 100084, China e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_32

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The method of dynamic light scattering is currently often used for dispersion analysis of solutions, suspensions, and other liquid media with small suspended particles [1–3]. The classical method of dynamic light scattering, based on the analysis of the autocorrelation function of the laser light scattering signal from the sample under study, allows one to estimate quickly the size distribution of particles in a liquid, the sizes of which can be from several nanometers to several microns [4–7]. The convenience of the dynamic light scattering method is explained by the fact that the optical system does not require calibration using model samples. These advantages of the method make it convenient for laboratory research, as well as for quality control of various areas of production [2, 8, 9]. With all the advantages of the dynamic light scattering method as a powerful tool for the dispersion analysis of liquid media with small particles, it cannot be used to study turbid samples with a high-volume concentration of particles [10]. This is due to the appearance of multiple scattering of light in the sample, that is, light that is scattered sequentially from two or more particles before leaving the cell with the sample and entering the photodetector. It is possible to get rid of the influence of multiple scattering on the measurement results by using the cross-correlation method [11]. The cross-correlation method consists in simultaneously registering not one scattering signal from the sample, but two at once [12]. Due to the fact that the photodetectors are located very close to each other, the recorded signals have the same useful component created by single scattering, and different noise components created by multiple scattered light [4]. When calculating the cross-correlation function of such signals, the contributions from multiple scattering are suppressed and it is possible to extract a useful signal. Thus, the cross-correlation method allows one to explore turbid liquid samples without diluting them. We developed an original scheme of a cross-correlation spectrometer, with which we estimated the particle size in a liquid monodisperse sample. Principles of operation of the spectrometer and the results obtained with it for aqueous suspensions with particles of known size are considered in this paper.

32.2

The Principle of the Cross-Correlation Spectrometer Operation

Figure 32.1 shows a cross-correlation spectrometer scheme. This spectrometer uses a two-beam scattering scheme, which implies the presence of two incident beams that intersect in the cell with the sample. Two beams are created by a helium-neon laser [13, 14] with a wavelength of 633 nm, a beam-splitting cube and a mirror, and a collecting spherical lens ensures the intersection of the beams inside the cell. We used a lens with a focal length of 30 cm.

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Fig. 32.1 Cross-correlation spectrometer scheme. 1— laser, 2—beam splitter, 3— mirror, 4—collecting spherical lens, 5—cell with a liquid sample, 6—optical fiber, 7—photodetector, 8— analog-to-digital board, 9— computer

When laser radiation is scattered from a large number of particles randomly moving in a fluid, a speckle light field arises around the cell. The average size of single-scattering speckles depends on the laser wavelength, the distance from the scattering plane to the spectral observation plane, and on the size of the illuminated area. In our case, this size is the diameter of the beams inside the cell [15]. Moreover, the smaller the diameter of the illuminated area, the larger the speckle size [16, 17]. Light enters the input aperture of two multimode optical fibers with a core diameter of 50 lm and is detected by photodetectors [18–20]. An analog-to-digital scheme digitizes the scattering signals, after which they enter the computer and are processed. The dynamic light scattering method uses one incident beam and one photodetector, so the size of speckles does not play a big role. Since both the dynamic light scattering method and the cross-correlation method, the incident beams are focused inside the cuvette, as a result of which a constriction is obtained there, a sufficiently large size of single-scattering speckles is provided for recording the scattering signal by one photodetector with high signal-to-noise ratio [21, 22]. But in the cross-correlation method, it is necessary that the same useful signal [10] gets to the input aperture of the optical fibers, that is, that one speckle falls directly into two optical fibers. Therefore, the size of speckles in the cross-correlation method plays a decisive role. Optical fibers are arranged vertically one above the other in a plane perpendicular to the plane of the incident beams [23]. With a distance between the centers of the optical fibers of 100 lm [24], the average size of the single-scattering speckles in the vertical direction turned out to be 150 lm. To increase the level of the useful signal relative to the noise level [21, 25, 26], we placed a cylindrical collecting lens with a focal length of 14 cm close to the cell, which compresses the speckle field horizontally. We placed the optical fiber in the focus of this lens. In other cross-correlation schemes, to record the intensity of the scattered light at two closely spaced points a translucent mirror is used. Next, one photodetector registers transmitted light, and the other—reflected. In such a scheme, a very accurate adjustment is required, which creates a problem when assembling a cross-correlation spectrometer. In our scheme, due to the use of a multi-fiber

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patching cord, it is possible not only to receive light at two points at a distance of 100 lm from each other, but also to increase this distance, if necessary, in increments of 100 lm, using signals from other fibers of this patching cord. In the presented cross-correlation setup, we refused to use the correlator. Thanks to the digitization of scattering signals by an analog-to-digital board and sending them to a computer, it becomes possible to improve the signal processing algorithms and change the counting frequency. In the future, this will help to choose the optimal counting frequency and calculation procedure.

32.3

Results

Figures 32.2 and 32.3 show the experimental autocorrelation and cross-correlation functions of scattering signals for four samples. Black color on the graphs indicate theoretical dependencies, which are described by the formula [11] gðsÞ ¼ Ae2Cs :

ð1Þ

Here g(s) is the normalized temporal autocorrelation or cross-correlation function of scattering intensity, A is the proportionality coefficient, Г is the quantity determined by the particle diffusion coefficient and the scattering vector module in accordance with the formula [4] C ¼ Dq2 ;

ð2Þ

where q is the modulus of the scattering wind defined by the formula [4] q ¼ ð4pn=kÞ sinðh=2Þ;

ð3Þ

Fig. 32.2 Autocorrelation and cross-correlation functions for aqueous monodisperse suspensions of latex microspheres with a diameter 300 nm for the concentration of 5 mkl a and 1.25 mkl b of microspheres per 1 ml of water

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Fig. 32.3 Autocorrelation and cross-correlation functions for aqueous monodisperse suspensions of latex microspheres with a diameter of 100 nm for the concentration of 10 mkl a and 2.5 mkl b of microspheres per 1 ml of water

and D is the diffusion coefficient associated with the particle diameter d by the formula [4] D ¼ kB T=ð3pgd Þ:

ð4Þ

In Eqs. (3) and (4), n is the refractive index of the solvent, k—is the wavelength of laser light, h is the scattering angle of 90°, kB—is the Boltzmann constant, T is the absolute temperature, and η is the viscosity of the solvent. In Figs. 32.2 and 32.3, we see that in the initial section, the autocorrelation and cross-correlation functions coincide with the theoretical ones, while the auto-correlation and cross-correlation functions for one sample coincide with the decay rate Г. For samples of different concentrations with particles of the same size, the decay rates of the correlation functions coincide. At the same time, for samples with particles of different sizes, the decay rates of the correlation functions are different, which indicates the possibility of estimating the particle size in the sample by the decay rate of the autocorrelation or cross-correlation function using Eqs. (3)–(4). The correlation functions obtained as a result of measurements with monodisperse samples can be approximated by smooth curves in accordance with Eq. (1) only for the delay time from 0 to about 0.2 ls; then, as the delay time increases, noise begins to dominate more and more [30], which may be due to insufficient success matched parameters, such as the focal lengths of the lenses and the distance between the receiving optical fibers. In the future, it is planned to investigate this issue and select the parameters so that the correlation functions are less noisy. In Figs. 32.2 and 32.3, one of the cross-correlation functions differs noticeably from the other three. This can be caused by the fact that the noise changes from implementation to implementation; therefore, for large values of the delay time s, the behavior of the function is random. Nevertheless, in the initial section of the

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curve, the cross-correlation function is weakly noisy and particle size can be estimated from it. From the experimental results it turns out that the particle size in a monodisperse sample can be estimated from the decay rate Г, having obtained its value by approximating the experimental function by a function of the form (1). Moreover, the value of Г in cross-correlation should depend on the average particle size and should not depend on their concentration, which makes the cross-correlation method the most preferable among optical methods of analysis of variance. With its help, highly turbid samples can be examined without dilution.

32.4

Conclusion

The difference between the developed scheme and other similar ones is that the receiving scattered radiation system is simplified, and instead of the correlator, an analog-to-digital board and a computer are used. Due to these changes, we tried to improve the cross-correlation scheme, significantly reducing the sensitivity of the receiving system to the accuracy of positioning of elements and making the signal processing block more universal. Due to the fact that the cross-correlation function is used in the cross-correlation method, the contributions from multiple scattering in the sample, which creates noise, are excluded from consideration [27]. In the presence of multiple scattering, the dynamic light scattering method cannot be used due to incorrect results, while the cross-correlation method can be used [11]. This gives the cross-correlation method a great advantage in the study of turbid liquid media without dilution. The results obtained showed the fundamental possibility of estimating particle size by the methods of autocorrelation and cross-correlation. In the future, it is planned to identify those limits of application of the cross-correlation method in size and concentration when autocorrelation is unsuitable for analysis of variance, while cross-correlation can be successfully applied. This task is quite complicated, but its solution will reveal the area of the most effective application of the method. Acknowledgements This research work was supported by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020”.

References 1. E. Nepomnyashchaya, E. Velichko, T. Bogomaz, Diagnostic possibilities of dynamic light scattering technique, in Progress in Biomedical Optics and Imaging - Proceedings of SPIE (2019). https://doi.org/10.1117/12.2509847 2. B. Berne, R. Pecora, Dynamic light scattering: with applications to chemistry, biology, and physics. Courier Corporation (2000)

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3. W. Zhou, M. Su, X. Cai, Advances in nanoparticle sizing in suspensions: dynamic light scattering and ultrasonic attenuation spectroscopy. KONA Powd. Particle J. 34, 168–182 (2017) 4. W.V. Meyer, D.S. Cannell, A.E. Smart, T.W. Taylor, P. Tin, Multiple-scattering suppression by cross correlation. Appl. Opt. 36, 7551 (1997). https://doi.org/10.1364/ao.36.007551 5. P.V. Shalaev, D.S. Kopitsyn, U.E. Kurilova, S.A. Dolgushin, The study of the geometric parameters and zeta potential of gold nanorods and nanostars based on light scattering methods, in Optics InfoBase Conference Papers. OSA - The Optical Society (2017). https:// doi.org/10.1117/12.2282714 6. P.V. Shalaev, P.A. Monakhova, Experimental study of polystyrene and gold nanoparticles using dynamic light scattering and nanoparticle tracking analysis, in Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020, Institute of Electrical and Electronics Engineers Inc. (2020), pp. 2549–2552. https://doi.org/10.1109/EIConRus49466.2020.9039346 7. J. Stetefeld, S.A. McKenna, T.R. Patel, Dynamic light scattering: a practical guide and applications in biomedical sciences (2016). https://doi.org/10.1007/s12551-016-0218-6 8. P.N. Pusey, D.W. Schaefer, D.E. Koppel, R.D. Camerini-Otero, R.M. Franklin, A study of the diffusion properties of r 17 virus by time-dependent light scattering. Le Journal de Physique Colloques 33, C1-163-C1-168 (1972). https://doi.org/10.1051/jphyscol:1972129 9. D. Issaad, H. Moustaoui, A. Medjahed, L. Lalaoui, J. Spadavecchia, M. Bouafia, N. Djaker, Scattering correlation spectroscopy and raman spectroscopy of thiophenol on gold nanoparticles: comparative study between nanospheres and nanourchins. J. Phys. Chem. C 121, 18254–18262 (2017) 10. A.J. Adorjan, J.A. Lock, T.W. Taylor, P. Tin, W.V. Meyer, A.E. Smart, Particle sizing in strongly turbid suspensions with the one-beam cross-correlation dynamic light-scattering technique. Appl. Opt. 38, 3409 (1999). https://doi.org/10.1364/ao.38.003409 11. W. Witt, H. Geers, L. Aberle, Measurement of particle size and stability of nanoparticles in opaque suspensions and emulsions with photon cross correlation spectroscopy (PCCS). Particul. Syst. Anal. (2003) 12. M. Kadobianskyi, I. Papadopoulos, T. Chaigne, R. Horstmeyer, B. Judkewitz, Scattering correlations of time-gated light. Optica 5, 389–394 (2018) 13. V. Privalov, V.G. Shemanin, On the determination of the minimum pulse energy in laser probing using harmonics of an Nd: YAG laser. Opt. Spectrosc. 82, 809–811 (1997) 14. V.E. Privalov, V.G. Shemanin, Optimization of a differential absorption and scattering lidar for sensing molecular hydrogen in the atmosphere. Tech. Phys. 44, 928–931 (1999). https:// doi.org/10.1134/1.1259407 15. Z. Zabalueva, E. Nepomnyashchaya, E. Velichko, Investigation of scattering intensity dependencies on the optical system parameters in cross-correlation spectrometer, in Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics, EExPolytech 2019 (2019). https://doi.org/10.1109/EExPolytech.2019.8906792 16. S.S. Ulyanov, V.V. Tuchin, Use of low-coherence speckled speckles for bioflow measurements. Appl. Opt. 39, 6385 (2000). https://doi.org/10.1364/ao.39.006385 17. Tuchin, V.: Handbook of Optical Biomedical Diagnostics (2002) 18. K.J. Smirnov, V.V. Davydov, S.F. Glagolev, G.V. Tushavin, High speed near-infrared range sensor based on InP/InGaAs heterostructures. J. Phys: Conf. Ser. 1124, 22014 (2018). https:// doi.org/10.1088/1742-6596/1124/2/022014 19. S.E. Logunov, V.V. Davydov, M.G. Vysoczky, O.A. Titova, Peculiarities of registration of magnetic field variations by a quantum sensor based on a ferrofluid cell. J. Phys: Conf. Ser. 1135, 12069 (2018). https://doi.org/10.1088/1742-6596/1135/1/012069 20. K.J. Smirnov, V.V. Davydov, Y.V. Batov, InP/InGaAs photocathode for hybrid SWIR photodetectors. J. Phys: Conf. Ser. 1368, 022073 (2019). https://doi.org/10.1088/1742-6596/ 1368/2/022073

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21. E. Nepomnyashchaya, E. Velichko, O. Kotov, Determination of noise components in laser correlation spectroscopic devices for signal-to-noise ratio estimation, in Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics, EExPolytech 2019. Institute of Electrical and Electronics Engineers Inc. (2019), pp. 321–324. https://doi. org/10.1109/EExPolytech.2019.8906887 22. V.E. Privalov, A.V. Rybalko, P.V. Charty, V.G. Shemanin, Effect of noise and vibration on the performance of a particle concentration laser meter and optimization of its parameters. Tech. Phys. 52, 352–355 (2007). https://doi.org/10.1134/S1063784207030115 23. I. Chapalo, O. Kotov, A. Petrov, Dual-wavelength One-Directional Multimode Fiber Interferometer With Impact Localization Ability, 9 May 2018 (2018). https://doi.org/10.1117/ 12.2307154 24. A. Petrov, E. Velichko, V. Lebedev, I. Ilichev, P. Agruzov, M. Parfenov, A. Varlamov, A. Shamrai, Broad-band fiber optic link with a stand-alone remote external modulator for antenna remoting and 5G wireless network applications, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Springer, Heidelberg, 2019), pp. 727–733. https://doi.org/10.1007/978-3030-30859-9_64 25. I. Chapalo, A. Petrov, D. Bozhko, M. Bisyarin, O. Kotov, Methods of Signal Averaging for a Multimode Fiber Interferometer: An Experimental Study, 11 April 2019 (2019). https://doi. org/10.1117/12.2522398 26. O. Kotov, I. Chapalo, Signal-to-noise ratio for mode-mode fiber interferometer, in Optical Measurement Systems for Industrial Inspection X (SPIE, 2017), p. 1032945. https://doi.org/ 10.1117/12.2270272 27. J. Burdíková, F. Mravec, J. Wasserbauer, M. Pekař, A practical comparison of photon correlation and cross-correlation spectroscopy in nanoparticle and microparticle size evaluation. Colloid. Polym. Sci. 295, 67–74 (2017)

Chapter 33

Experimental Study of Frequency Modulation in Single-Frequency Lasers Philipp V. Skliarov , Konstantin V. Muravyov , and Aleksei O. Kostromitin

Abstract The paper presents an experimental investigation of frequency modulation of different types of single-frequency lasers. Dependences of frequency modulation coefficients on modulating frequency were obtained experimentally and parasitic intensity modulation was estimated. The work is of an applied nature, and can be useful for interferometric fiber optic sensors engineering.







Keywords Fiber laser Semiconductor laser Frequency modulation of the laser Fiber sensor Auxiliary phase modulation signal




33.1




Introduction

At present, fiber and semiconductor single-frequency lasers are widely used to create measurement systems based on interferometric fiber-optical sensors [1–5]. Frequency modulation of these lasers allows the formation of an auxiliary phase modulation signal in fiber-optic sensors based on an unbalanced interferometer. For example, this is used in push-pull fiber-optic sensors [6, 7]. A piezoelectric actuator (PZT) embedded in the optical circuit is used to modulate the frequency in a fiber laser. In a fiber laser, an active fiber with a laser resonator is attached to the PZT [8] or a Fabry-Perot interferometer with a PZT is used connected to the laser resonator [9]. This type of modulator has mechanical resonances, which are important to consider when choosing the frequency of the modulating action. In addition, when the modulation signal is applied to the PZT, the laser radiation intensity is modulated [10]. In a semiconductor laser is used modulation the injecting current to modulate the frequency [11, 12]. In this case, modulation of frequency occurs simultaneously with intensity modulation, which leads to distortion of the interferential signal of the fiber-optic sensor [13]. In addition, the efficiency of laser frequency modulation P. V. Skliarov
K. V. Muravyov
A. O. Kostromitin (&) Concern CSRI Elektropribor, St. Petersburg, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_33

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depends on the frequency of injecting current modulation [14]. Therefore, to be able to compensate for the parasitic amplitude modulus and implement correct demodulation of the fiber-optic sensor signal, it is necessary to know dependences of the frequency modulation and amplification of laser radiation on the parameters of the modulation signal.

33.2

Experimental Setup

The experimental setup diagram is shown in Fig. 33.1. The signal of frequency modulation is sent from the generator (G1) to the laser modulation input (L). Optical radiation enters from the laser on the input of the Mach-Zener interferometer. The interferometer consists of two polarization-preserving splitters (SP), a phase modulator based on lithium niobate (PhM), and a delay line (D). The interferometer has an unbalance of DL = 3.067 m. The signal of sawtooth auxiliary phase modulation entered phase modulator by the generator (G2). The interference signal is attenuated by the attenuator from the interferometer output and direct to the photodetector (Phr). The interference signal was then recorded on an oscilloscope (Osc) and then processed. The sample rate of the ADC oscilloscope Fs = 100 MHz, time samples of the interference signal were stored at an interval of 50 ms (5 million points). The interference signal at the output of the photodetector can be written as   uðtÞ ¼ 1 þ Kp
Umod ðtÞ
fU0 þ Um cos½uðtÞ þ wðtÞg;

ð33:1Þ

where Kp is intensity modulation coefficient [1/V], Umod(t) is modulation signal applied to the laser, U0 is constant component, Um is interference signal amplitude, uðtÞ is phase modulation caused by the laser frequency modulation, wðtÞ is auxiliary phase modulation signal. The amplitude uðtÞ is determined by the ratio uðtÞ ¼ 2p
DL
n
Km
Umod ðtÞ=c;

SP1

L

D

SP2

Phm

fm G1

Fig. 33.1 Experimental setup

ð33:2Þ

Phr

Fm G2

synchronization

Osc

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293

where n is the refractive index, Km is frequency modulation coefficient [MHz/V], and c is the speed of light in vacuum. The signal (33.1) was processed using the demodulation algorithm described in the paper [15]. The amplitude um was determined of the signal uðtÞ as a result of demodulation of the signal (33.1). Then the coefficient Km was calculated by the ratio (33.2). Coefficients Km and Kp were determined for several frequencies of the fm signal of modulation. In the case of fm = 500 Hz and 5 kHz, the signal wðtÞ was formed with the frequency Fm = 100 kHz, and for fm  20 kHz, Fm = 2.5 MHz. The optical radiation of the laser output was direct to the photodetector through an attenuator in scheme (Fig. 33.1) without using an interferometer to determine the value of the Kp coefficient. The oscilloscope recorded the level of the constant component Uconst in DC mode and the amplitude of the intensity fluctuations Uac in AC mode. The Kp coefficient was calculated using the formula Kp ¼ ðUac =Uconst Þ
100%=Umod :

33.3

ð33:3Þ

Experimental Results

33.3.1 Semiconductor Laser

Fig. 33.2 The Km(fm) dependence for the RIO laser (the blue curve is experimental dependence, the orange curve is an approximation)

Kν(fm), MHz/V

Two semiconductor lasers were used in the study: RIO OrionTM Grade 1 and Thorlabs SFL-1550P with the Thorlabs ITC-5022 driver. The experimental dependences of Km(fm) and approximation results are shown in Figs. 33.2 and 33.3.

fm, Hz

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The approximation of the experimental dependence Km(fm) of the laser RIO has the form Km ðfm Þ ¼ 35:77
lgðfm Þ þ 192:55:

ð33:4Þ

The approximation of the experimental dependence Km(fm) of the laser Thorlabs has the form Km ð f Þ ¼ 26:47
ðfm Þ0:1 þ 111:10

ð33:5Þ

Fig. 33.3 The Km(fm) dependence for the Thorlabs laser (the blue curve is experimental dependence, the orange curve is an approximation)

Kν(fm), GHz/V

The results of the study of the dependence of the Kp(fm) coefficient are shown in Table 33.1 for both semiconductor lasers.

fm, kHz

Table 33.1 Coefficients Kp, for semiconductor lasers

fm, kHz

RIO Kp, %/V

Thorlabs Kp, %/V

0.5 1 2 5 10 20 50 100 150

1.18 1.09 0.99 0.93 0.89 0.74 0.49 0.21 0.09

11.52 11.56 11.58 11.59 11.60 11.58 11.41 11.05 10.53

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295

33.3.2 Fiber Laser The study used an NP Photonics Rock fiber laser. The dependence of the amplitude uðtÞ on the amplitude of the Umod(t) shows on Fig. 33.4 modulation signal for several fm values. For fm = 10 kHz, the dependence uðUmod Þ is linear over the entire range of amplitudes Umod(t). However, for higher frequencies (Fig. 33.5), nonlinear dependence uðUmod Þ is observed. Moreover, for frequencies fm = 30 kHz and 40 kHz, this dependence does not have linear sections. Therefore, measurements Km(fm) were performed only for frequencies not exceeding 20 kHz (Fig. 33.5). The approximation of the experimental dependence Km(fm) of the fiber laser has the form Km ðfm Þ ¼ 0:0074  ðfm Þ3 þ 0:3517  ðfm Þ2 5:7278  ðfm Þ þ 34:8696: ð33:6Þ

Fig. 33.4 Dependence of the amplitude uðtÞ on the amplitude of the Umod(t)

fm = 20 kHz

φ, rad

fm = 30 kHz

fm = 40 kHz

Fig. 33.5 The Km(fm) dependence for fiber laser (the blue curve is an experimental dependence, the orange curve is an approximation)

Kν(fm), MHz/V

Umod, V

fm, kHz

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Table 33.2 Coefficients Kp, for fiber lasers

fm, kHz

Kp, %/V

0.5 1 2 5 10

0.25 0.42 0.59 0.67 0.52

Uac, V

Fig. 33.6 Signal of parasitic amplitude modulation fm = 500 Hz, Umod = 13 V

t, ms

The results of the study of the dependence of the Kp (fm) coefficient are shown in Table 33.2. During the study of parasitic modulation of the intensity of a fiber laser, it was observed that fluctuations in the intensity of laser radiation contain noticeable distortions (Fig. 33.6).

33.4

Conclusion

Frequency modulation and spurious amplitude modulation for fiber and two semiconductor lasers were investigated. The largest frequency modulation coefficient has the semiconductor laser Thorlabs. Spurious intensity modulation when a frequency modulation signal is applied has the smallest value for a fiber laser. However, the fiber laser has limitations on frequency and amplitude of the modulation signal, which does not allow it to be used for generating an auxiliary phase modulation signal with a frequency of more than 40 kHz. The research results can be used in the development of measurement systems with fiber-optic sensors based on unbalanced interferometers. In particular, the obtained values of the frequency modulation coefficient allow choosing the optimal value of the interferometer unbalance, and the value of the intensity modulation coefficient allows estimating the distortion of the interference signal.

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References 1. E. Udd, Design and Application of Fiber Optic Sensors (2017) 2. N.A. Ushakov, A.A. Markvart, L.B. Liokumovich, Pulse wave velocity measurement with multiplexed fiber optic fabry-perot interferometric sensors. IEEE Sens. J. (2020) 3. L. Liokumovich, A. Markvart, N. Ushakov, Utilization of extrinsic fabry-perot interferometers with spectral interferometric interrogation for microdisplacement measurement. J. Electron. Sci. Technol. 100030 (2020) 4. V. Petrov, A. Medvedev, L. Liokumovich, Fiber-optic polarization interferometric sensor for precise electric field measurements. Int. J. Mod. Phys. A 31, 1641032 (2016) 5. J. Chen et al., A fiber-optic interferometric tri-component geophone for ocean floor seismic monitoring. Sensors 17(1), 47 (2017) 6. P.V. Skliarov, et al., Phase difference measurement of the target signals of sensing elements in the push-pull fiber optic interferometric acoustic and vibration sensors, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, 2019) 7. X. Qiu, et al., A new fiber optic accelerometer with push-pull structure using 3  3 coupler, in 2017 25th Optical Fiber Sensors Conference (OFS) (IEEE, 2017) 8. G.A. Cranch, Frequency modulation properties of erbium doped DFB fiber lasers using cavity strain, in 2002 15th Optical Fiber Sensors Conference Technical Digest. OFS 2002 (Cat. No. 02EX533) (IEEE, 2002) 9. C. Li et al., High-speed frequency modulated low-noise single-frequency fiber laser. IEEE Photon. Technol. Lett. 28(15), 1692–1695 (2016) 10. Z. Yang, et al., Fundamental principle and enabling technologies of single-frequency fiber lasers, in Single-Frequency Fiber Lasers (Springer, Singapore, 2019), pp. 11–53 11. S. Ogita et al., Direct frequency modulation of semiconductor laser. Electron. Commun. Jpn. (Part II: Electronics) 74(2), 39–49 (1991) 12. K.G. Libbrecht, J.L. Hall, A low-noise high-speed diode laser current controller. Rev. Sci. Instrum. 64(8), 2133–2135 (1993) 13. P.V. Skliarov, A.O. Kostromitin, K.V. Muravyov, Distortion analysis of interferometric signal with auxiliary emission modulation in semiconductor laser, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, 2019) 14. A.O. Kostromitin, A.V. Kudryashov, L.B. Liokumovich, Measurement and analysis of modulation and noise in the output frequency of single-frequency semiconductor diode lasers. J. Appl. Spectrosc. 82(4), 659–664 (2015) 15. L. Liokumovich et al., Signal detection algorithms for interferometric sensors with harmonic phase modulation: miscalibration of modulation parameters. Appl. Opt. 57(25), 7127–7134 (2018)

Chapter 34

Temperature Dependence of Acousto-Optic Polarization Mode Conversion Peak Frequency and Efficiency Andrey V. Varlamov , Petr M. Agrusov, Igor V. Il’ichev, Vladimir V. Lebedev, Aleksandr V. Shamrai , and Serguei I. Stepanov

Abstract Acousto-optic frequency shift modulators are widely used for well-known heterodyne signal processing algorithms in interferometric sensors. Output signal stability is very important for such modulators applications. For this reason temperature dependences of the polarization mode conversion peak frequency and efficiency of those modulators was tested in a climate chamber. It was shown that the shift of the polarization mode conversion peak frequency was about 0.1 MHz per 1 °C temperature change. Such behavior was theoretically explained by the temperature dependence of the difference between the effective refractive indices for polarization modes and the temperature dependence of the surface acoustic wave velocity on the X-cut lithium niobate substrate. Also it was shown that the change in the polarization mode conversion efficiency with temperature was due to a change in the surface acoustic wave excitation efficiency with the frequency, characterized by the frequency dependence of the real part of the interdigital transducer admittance. Keywords Integrated acousto-optic modulator frequency shift


Lithium niobate
Optic

A. V. Varlamov (&)
P. M. Agrusov
I. V. Il’ichev
V. V. Lebedev
A. V. Shamrai Laboratory of Quantum Electronics, Ioffe Institute, St. Petersburg, Russia e-mail: [email protected] S. I. Stepanov Ensenada Center for Scientific Research and Higher Education, Ensenada, Mexico © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_34

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Introduction

There are many different types of acousto-optic modulators (AOM) [1–4]. Acousto-optic frequency shift modulators (AOFSM) have applications in heterodyne signal processing algorithms [5–7] in interferometric sensors. It provides insensitivity of measurement systems to amplitude fluctuations. However, the invariability of the frequency shifted optical signal is very important for the accuracy of these systems. In this work, we focused on studying of the temperature dependences of acousto-optic polarization mode conversion peak frequency and efficiency. For this reason temperature dependences of the polarization mode conversion peak frequency and efficiency of those modulators was tested in a climate chamber.

34.2

Experimental Samples and Measuring Techniques

34.2.1 Experimental Sample of the AOFSM Chip Experimental sample of the AOFSM chip is shown on the Fig. 34.1. It is a single crystal X-cut congruent lithium niobate (LN) substrate (5  50  1mm) on which a titanium in-diffused straight channel optical waveguide [8, 9] was created. Optical waveguide was directed along the Y axis. Also there was created collinear straight channel acoustic waveguide which was described in our previous work [10]. Both waveguides had single mode waveguide propagation. Interdigital transducers (IDTs) were formed from the thin film (200 nm) magnetron sputtered aluminum. The IDTs had distances between centers of two adjacent fingers d = 10 µm which corresponded resonance SAW excitation at the frequency f0  179.5 MHz. An angle between the fingers and the Z axis was 5° for the compensation of a divergence between the direction of the acoustic power flow and the normal to SAW wave front [11]. IDTs had number of fingers N = 40 and overlap length D = 0.1 mm.

Fig. 34.1 Experimental sample of the AOFSM chip (D = 100 lm—length of the electrode overlap, d = 10 lm—distance between centers of adjacent electrodes, N = 40—number of electrodes, b = 5˚—the compensation angle between the electrodes and the Z axis [11])

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34.2.2 Experimental Methods A vector network analyzer was used to measure network parameters of IDT the reflection coefficients (S11) and the phase shift between initial signal and reflected one. It was used to calculate the real part of admittance. Observation of the TE-TM mode conversion was used to obtain information about the polarization mode conversion peak frequency and the efficiency. A tunable laser with wavelengths 1505, 1520 and 1550 nm and power 1 mW produced the laser radiation with TE polarization. The RF signal with power 15 dBm in the range from 170 to 190 MHz was applied to the IDT at three temperatures of 5, 25, and 40 °C. The light radiation passed through the AOFSM chip to the polarization splitter. The TE polarization from the polarization splitter passed to the photodetector. The output light intensity was measured using a photodetector. Diffraction efficiency was determined from the decrease in the intensity of the undiffracted TE mode. Also the change in the difference between the ordinary and extraordinary congruently grown LN refractive indices from temperature was measured by a polarization fiber optic interferometer [12, 13].

34.3

Results and Discussion

34.3.1 Temperature Dependences of Maximum Mode Conversion Frequency and Efficiency It was observed that the frequency of the polarization mode conversion peak depends on the input light wavelength and on the temperature. The frequency increased with the decrease in the wavelength and the decrease in temperature. At the same time, significant changes in the acousto-optic conversion efficiency were observed (see Fig. 34.2).

Fig. 34.2 Temperature dependences for efficiency (a) and frequency (theoretical and experimental) (b) of the maximum conversion of polarization modes in the AOFSM sample

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It can be seen that the dependence of the maximum mode conversion frequency on temperature is almost linear in the studied temperature range. The shift of the maximum mode conversion frequency when the temperature changes by 1 °C is about 0.1 MHz, which can be expressed by the empirical formula (34.1): DF½MHz ¼ 0; 1
DT½ C:

ð34:1Þ

Such temperature dependence of the frequency was caused by both the temperature dependence of the difference of the ordinary and extraordinary LN refractive indices and the temperature dependence of the SAW velocity.

34.3.2 Temperature Dependence of the Difference of the Ordinary and Extraordinary LN Refractive Indices The temperature dependence of the difference of the effective refractive indices for polarization modes (TE and TM) in the Ti:LiNbO3 waveguide at 1550 nm was obtained (Fig. 34.3). Sellmeier equation for calculating the ordinary and extraordinary refractive indices of the congruent X-cut lithium niobate substrate is [14, 15]. sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a2i þ b1i FðtÞ ni ¼ a1i þ 2 þ b3i FðtÞ  a4i k20 ; 2 k0  ða3i þ b2i FðtÞÞ

ð34:2Þ

where k0 is the wavelength of light in vacuum (in micrometers); F(t) = (t − 24.5) (t + 24.5 + 2∙273.15)—function of temperature in degrees Celsius; a1i, a2i, a3i, a4i, b1i, b2i, b3i—coefficients for calculating the ordinary and extraordinary refractive indices presented in the Table 34.1.

Fig. 34.3 Temperature dependence of the difference of the effective refractive indices for polarization modes and theoretical temperature dependence of the difference of the ordinary and extraordinary LN refractive indices

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Table 34.1 Coefficients for Sellmeier equation for LN Refr. index

a1

a2

a3

a4

b1

b2

b3

no ne

4,9048 4,5820

0,11775 0,09921

0,21802 0,21090

0,027153 0,021940

2,2314∙10−8 5,2716∙10−8

−2,9671∙10−8 −4,9143∙10−8

2,1429∙10−8 2,2971∙10−7

Fig. 34.4 Frequency dependences of the maximum conversion efficiency of polarization modes and the real part of the IDT admittance

It can be seen on the Fig. 34.3 that the experimental temperature dependence of the difference of the effective refractive indices for polarization modes in the Ti: LiNbO3 waveguide and the theoretical temperature dependence of the difference of the ordinary and extraordinary refractive indices of congruent LN coincided with good accuracy. This is because the Ti:LiNbO3 waveguide is weak guiding one. So the effective waveguide refractive indices for the TM and TE modes slightly different from the ordinary and extraordinary refractive indices of pure LN [16, 17]. A slight discrepancy between theoretical calculations and experimental results at temperatures above 50 °C is due to the appearance of a high temperature gradient in the experiment and less accurate temperature determination.

34.3.3 Temperature Dependence of the SAW Velocity However, a comparison of the temperature dependence of the difference of the effective refractive indices for polarization modes and the experimentally obtained temperature dependence of the maximum conversion frequency showed that it was necessary to take into account the change in the SAW velocity with temperature. The formula for the dependence of the SAW velocity on temperature has the form [18, 19]. VSAW ¼ VSAW0 ð1 þ a
DtÞ;

ð34:3Þ

where VSAW0 is the SAW velocity at the standard reference temperature (25 °C), Dt is the difference t − t0 of the current temperature and the standard reference one, a is the temperature coefficient for the SAW velocity on the X-cut LN (approximately equal to −0.0001 1/°C [18, 19]). The exact value of VSAW0 was determined

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by trial method and was 3660 m/s. The theoretical and experimental dependences are presented in the Fig. 34.2b. It can be seen that the experimental data are in good agreement with the theoretical dependencies. The dependence of the maximum conversion efficiency of polarization modes on the modulation frequency was obtained by combining the experiments data presented in the Fig. 34.2. This dependence is shown in the Fig. 34.4 together with the frequency dependence of the real part of the IDT admittance. It can be seen on the Fig. 34.4 that the change in the maximum conversion efficiency of polarization modes exactly matches to the change in the SAW excitation efficiency, characterized by the frequency dependence of the IDT admittance real part.

34.4

Conclusion

It was shown that the shift of the polarization mode conversion resonant frequency was about 0.1 MHz per 1 °C temperature change. Such behavior was explained by the contributions totality of the temperature dependence of the difference between the effective refractive indices for polarization modes and the temperature dependence of the surface acoustic wave velocity on the X-cut lithium niobate substrate. In addition, it was shown that the change in the polarization mode conversion efficiency with the temperature was due to a change in the surface acoustic wave excitation efficiency with the frequency, characterized by the frequency dependence of the real part of the IDT admittance.

References 1. S. Kakio, Acousto-optic modulator driven by surface acoustic waves. Acta Phys. Pol., A 127 (1), 15–19 (2015) 2. N. Courjal, S. Benchabane, J. Dahdah, G. Ulliac, Y. Gruson, V. Laude, Acousto-optically tunable lithium niobate photonic crystal. Appl. Phys. Lett. 96(13), 131103 (2010) 3. V.V. Kondalkar, Y. Lee, S.S. Yang, K. Lee, Highly diffractive, reversibly fast responsive gratings formulated through focused surface acoustic wave for holographic display. J. Mater. Sci.: Mater. Electron. 28(7), 5366–5374 (2017) 4. V.V. Kondalkar, G. Ryu, Y. Lee, K. Lee, Development of acousto-optic spatial light modulator unit for effective control of light beam intensity and diffraction angle in 3D holographic display applications. J. Micromech. Microeng. 28(7), 074001 (2018) 5. L. Liokumovich, A. Medvedev, K. Muravyov, P. Skliarov, N. Ushakov, Signal detection algorithms for interferometric sensors with harmonic phase modulation: distortion analysis and suppression. Appl. Opt. 56(28), 7960–7968 (2017) 6. L. Liokumovich, K. Muravyov, P. Skliarov, N. Ushakov, Signal detection algorithms for interferometric sensors with harmonic phase modulation: miscalibration of modulation parameters. Appl. Opt. 57(25), 7127–7134 (2018) 7. Y. Park, K. Cho, Heterodyne interferometer scheme using a double pass in an acousto-optic modulator. Opt. Lett. 36(3), 331–333 (2011)

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8. P.M. Karavaev, I.V. Il’ichev, P.M. Agruzov, A.V. Tronev, A.V. Shamray, Polarization separation in titanium-diffused waveguides on lithium niobate substrates. Tech. Phys. Lett. 42 (5), 513–516 (2016) 9. M. Bazzan, C. Sada, Optical waveguides in lithium niobate: Recent developments and applications. Appl. Phys. Rev. 2(4), 040603 (2015) 10. A.V. Varlamov, V.V. Lebedev, P.M. Agruzov, I.V. Ilichev, L.V. Shamrai, A.V. Shamrai, Acousto-optic frequency shift modulators with acoustic and optic waveguides on X-cut lithium niobate substrates. J. Phys: Conf. Ser. 1326(1), 012011 (2019) 11. J. Yang, H. Xu, C. Wen, C. Sun, Optimal design of integrated acousto-optic tunable filters based on investigation of SAW in acoustic waveguide, in Photorefractive Fiber and Crystal Devices: Materials, Optical Properties, and Applications XII, vol. 6314, (2006), p. 63140U 12. V. Petrov, A. Medvedev, L. Liokumovich, A. Miazin, Fiber-optic polarization interferometric sensor for precise electric field measurements. Int. J. Mod. Phys. A 31(02n03), 1641032 (2016) 13. L.B. Liokumovich, A.V. Medvedev, V.M. Petrov, Fiber-optic polarization interferometer with an additional phase modulation for electric field measurements. Opt. Memory Neural Netw. 22(1), 21–27 (2013) 14. G.J. Edwards, M. Lawrence, A temperature-dependent dispersion equation for congruently grown lithium niobate. Opt. Quantum Electron. 16(4), 373–375 (1984) 15. S. Fieberg, L. Streit, J. Kiessling, P. Becker, L. Bohaty, F. Kühnemann, K. Buse, Lithium niobate: wavelength and temperature dependence of the thermo-optic coefficient in the visible and near infrared, in Nonlinear Frequency Generation and Conversion: Materials, Devices, and Applications XIV, vol. 9347, (2015), p. 93471C 16. D.L. Zhang, C.X. Qiu, W.H. Wong, E.Y.B. Pun, Relationship between refractive index increase and Ti4 + concentration in Ti: LiNbO3 waveguide fabricated by Ti4 + diffusion in near-stoichiometric LiNbO3 substrate. Mater. Res. Bull. 60, 771–777 (2014) 17. P. Ganguly, J.C. Biswas, S.K. Lahiri, Analysis of titanium concentration and refractive index profiles of Ti: LiNbO 3 channel waveguide. J. Opt. 39(4), 175–180 (2010) 18. C. Zhou, Y. Yang, H. Cai, T.L. Ren, M. Chan, C.Y. Yang, Temperature-compensated high-frequency surface acoustic wave device. IEEE Electron Device Lett. 34(12), 1572–1574 (2013) 19. I.E. Kuznetsova, B.D. Zaitsev, S.G. Joshi, Temperature characteristics of acoustic waves propagating in thin piezoelectric plates, in 2001 IEEE Ultrasonics Symposium. Proceedings. An International Symposium, vol. 01CH37263, no. 1 (2001), pp. 157–160

Chapter 35

Intermodal Fiber Interferometer with Scanning Laser and Correlation Signal Processing: An Experimental Study Alexandr Petrov , Ivan Chapalo , and Oleg Kotov Abstract An intermodal fiber interferometer with wavelength scanning laser is proposed and experimentally investigated. The correlation processing of the interferometric signal formed by laser wavelength scans following each other is utilized to provide real-time stable response to external fiber perturbations. The ability to obtain linear transfer function is demonstrated by introducing the wavelength shift to the autocorrelation function calculation. The interferometer setup utilized in experiments is based on an SMS structure and a commercial FBG interrogator with a wavelength scanning laser. Keywords Fiber optic sensors Specklegram sensors

35.1


Multimode fiber
Intermodal interference


Introduction

Intermodal fiber interferometer (IFI) sensors have been intensively developing in recent years and are proposed to be applied in distributed security systems [1], vibration [2], temperature [3, 4], and pressure [5] sensing, biomedical [6, 7], chemical [8, 9] and other applications [10–16]. The operation principle of IFI-based sensors is to analyze a speckle pattern formed at a multimode optical fiber (MMF) end-face as a result of propagating modes’ interference. The speckle pattern is sensitive to external fiber perturbations that enables various physical quantities sensing. A. Petrov (&)
O. Kotov Institute of Physics, Nanotechnology and Telecommunication, Peter the Great St. Petersburg Polytechnic University, 29, Polytechnicheskaya Ul., St. Petersburg 195251, Russia e-mail: [email protected] I. Chapalo Electromagnetism and Telecom Department, University of Mons, 31 Boulevard Dolez, 7000 Mons, Belgium © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_35

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The main drawbacks of fiber specklegram sensors are statistical nature of an interferometric signal and strong nonlinearity of an IFI transfer function. The presence of varying ambient conditions, such as temperature and non-signal (parasitic) mechanical perturbations, results in signal fading. These factors make accurate sensing impossible without additional methods of IFI signal processing. In this work, we experimentally demonstrate the IFI with wavelength scanning light source providing stable response to external fiber perturbations operating in real time. For the IFI signal processing, we apply correlation approach ensuring signal fading reduction. In particular, we introduce so-called shifted correlation function providing linear response of the sensor. The IFI is realized as a single-mode-multi-mode-single-mode (SMS) structure interrogated by a commercial FBG interrogator with scanning laser.

35.2

Principle of Operation

A laser excites a certain number of modes in an MMF. As a result of their interference, the speckle pattern is formed at the fiber end-face. The speckle pattern is sensitive to external fiber perturbations and laser’s optical frequency variations that reveals as the speckle pattern transformations. The interferometric signal can be acquired by spatial filtering of the speckle pattern that in practice can be realized by recording an optical signal by a photodetector in far-field, projecting near-field intensity distribution to a camera matrix (every pixel of which acts as an individual spatial filter and a photodetector), or passing light through a single-mode fiber connected or spliced to an MMF output facet. The IFI is a statistical sensor, which main issues are signal fading and random non-linear transfer function. To overcome these limitations, we propose correlation processing of the IFI signal generated by continuously repeating laser’s wavelength scanning. Considering the IFI signals generated by individual wavelength scans as a single measurement points following each other, one can be calculated the temporal autocorrelation function (ACF) between the reference and the signal scans. The IFI response to the same wavelength scan remains unchanged in the absence of any external fiber perturbations keeping the ACF to be equal to unity. However when external fiber perturbations occur (in this work we consider fiber elongation dL as an EFP), the IFI response to single wavelength scan transforms causing decrease of the ACF value (Fig. 35.1, solid curve). In contrast to processing of the signal recorded by a photodetector at certain wavelength, this scanning technique eliminates signal fading, providing averaging over laser’s optical frequency.

35

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309

Fig. 35.1 The normalized autocorrelation function in two operating modes: the ACF itself (solid line) and the SCF (dashed line). The bold dots highlight the operating points

Fig. 35.2 Schematic illustration of the SCF calculation introducing the wavelength shift between the reference and the signal wavelength scans. The dashed line indicates the wavelength domain on which integration is performed when calculating the SCF. (In the figure, for a greater clarity, the value of f is significantly higher than used in the experiment)

However, the application of the described method in current form appends non-linearity to the transfer function taking to account the shape of the ACF. This drawback can be overcome by introducing a wavelength shift f between reference and signal wavelength scans (Fig. 35.2), shifting the operating point to the linear part of the ACF (Fig. 35.1, dashed curve). We refer to the new function as a shifted correlation function (SCF). Thus, the proposed technique provides two advantages: signal stability (fading elimination) and linearity of the transfer function.

35.3

Experimental Setup

A schematic of the experimental setup is shown in Fig. 35.3. We utilized four-channel FBG interrogator NI PXIe 4844 as a scanning light source and a photodetector [17, 18]. The parameters of the interrogator were as follows: wavelength scanning range 80 nm (1510–1590 nm), wavelength scanning step dk = 4 pm, scanning frequency 10 scans per second, optical power 0.06 mW, SMF-28

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Fig. 35.3 Schematic of the experimental setup

single-mode optical fiber (SMF). The first channel of the interrogator was used as a light source, and the second channel—as a photodetector. Light was introduced to the MMF (graded-index Corning 50/125 MMF with NA = 0.2 and length 50 m) by FC/PC fiber connection after passing through the mode scrambler “ModCon” (Arden Photonics). The last was utilized for approximately equal and stable modes excitation in experiments. After passing the MMF, light was spatially filtered by FC/PC connection of the MMF and the SMF, forming an SMS structure. The second end of the SMF was connected to the interrogator’s second channel operating as a photodetector. The circulator was placed in the output single-mode section in order to avoid parasitic effects related with light counter propagation in the scheme. A major part of the MMF was coiled on a piezoceramic modulator of cylindrical shape with diameter of 5 cm. The MMF length modulation generated by the modulator (applying alternating voltage of sinusoidal shape with frequency X = 0.5 Hz and amplitudes up to 40 V) was considered as an external fiber perturbation. The chosen frequency X was conditioned by respectively low sampling rate of the interrogator (10 wavelength scans per second) and provided 20 measured points per one period of the perturbation. Additionally, one meter of the remaining MMF was placed on the temperature controller based on the Peltier element. Slowly changing temperature of the controller (linear change from 20 °C to 40 °C during 300 s) we simulated slowly varying environmental conditions as a source of signal fading. This enabled us to evaluate the stability of the IFI signal response to fiber length modulation generated by the piezoceramic modulator. Signal processing was implemented in the LabVIEW software.

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311

Experimental Results and Discussion

The experiment was conducted as follows. Both piezoceramic modulation and slowly changing temperature were simultaneously applied to the MMF. The IFI signal was recorded as a sequence of signals generated by repeating wavelength scans. The IFI signal corresponding to one scan of the wavelength is shown in Fig. 35.4 (a). It is seen that the signal demonstrates typical interferometric behavior inherent in IFIs. Large number of maxima and minima indicates that utilized fiber length and the scanning band provided by the interrogator are sufficient for effective averaging. Figure 35.4 (b) demonstrates shift and slight transformation of the scan under weak external perturbation. Such transformations represent the target signal. The ACF calculation was implemented as follows: ACFðtÞ ¼

X 1 k¼1590 Iðt ¼ 0; kÞ
Iðt; kÞ; N k¼1510

ð1Þ

where I(t = 0,k) is the reference wavelength scan, I(t,k) is the wavelength scan obtained at time t, N is the number of discrete wavelength points provided by the interrogator (N = 20,000 in a range Dk = 1510 − 1590 nm). Utilized I(t,k) was obtained by subtracting the mean value from initial wavelength scans in order to eliminate the constant component. The IFI response R(t) was calculated by the ACF normalization by its maximum value: RðtÞ ¼

ACFðtÞ : ACFmax ðtÞ

ð2Þ

The SCF was calculated introducing the integration shift f to Eq. (1): SCFðtÞ ¼

X 1 k¼1590 Iðt ¼ 0; k þ fÞ
Iðt; kÞ; N k¼1510

ð3Þ

Fig. 35.4 The IFI signal generated by one wavelength scan a; an example of the wavelength scan transformation under external perturbation (the fragment of the entire wavelength scan) b

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Fig. 35.5 The IFI response to the MMF sinusoidal length modulation: the IFI signal recorded at certain wavelength and subsequently high-pass filtered a, the IFI response calculated by the ACF b, and the IFI response calculated by the SCF c. Voltage amplitudes U applied to the piezoceramic modulator are shown in the figures

and the IFI response Rs utilizing the SCF was calculated as Rs ðtÞ ¼ 1 

SCFðtÞ : SCFmax ðtÞ

ð4Þ

The substitution from unity was introduced to provide a positive slope of the transfer function (see Fig. 35.1, dashed curve, where the slope is negative). It should be noted that under changing environment conditions (provided by slowly changing temperature in our experiments), the reference scan must be periodically updated in order to provide the operating point stability. In experiments, we updated the reference scan every four seconds. Figure 35.5 demonstrates the IFI response to sinusoidal fiber length modulation provided by the piezoceramic modulator according to applied voltage u(t) = (U/2) ∙sin(0.5∙t) when three different approaches were utilized for signal processing: the signal recorded at certain wavelength and subsequently high-pass filtered in order to remove constant and slowly changing components (a), the IFI response calculated by the ACF (b), and the IFI response calculated by the SCF (c) (Fig. 35.6). It is seen that in the first case, the signal is significantly distorted. Moreover, under changing ambient conditions, the amplitude and the shape of the signal are varied demonstrating fading and nonlinearity of the IFI response (it is demonstrated in the following results (Fig. 35.7 (a))). In the second case, the amplitude of the IFI response is stable and proportional to the amplitude of the modulating signal (alternating voltage applied to the modulator). However, the transfer function is not linear as the shape of the signal represents the squared modulating signal (according to Fig. 35.1, solid curve), and the signal is single-signed. In contrast, the third case demonstrates complete modulating signal reconstruction (in accordance with Fig. 35.1, dashed curve) with stable amplitude proportional to the amplitude of modulating signal. The transfer function calculated as a dependency of the SCF amplitude on the amplitude of voltage applied to piezoceramic modulator is

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Fig. 35.6 The IFI transfer function measured with the SCF signal processing

Fig. 35.7 The primal IFI signal a and corresponding IFI response calculated by the SCF b measured during 300 s when both piezoceramic modulation and slowly changing temperature were applied to the MMF. The bold curves represent the envelopes of the signal in order to demonstrate the amplitude variation. Typical shape of the signal is presented by the insets

presented in Fig. 35.6. Thus, it can be concluded that the SCF signal processing provides both stability and linearity of the IFI response. It should be mentioned that the wavelength shift f must be properly chosen in order to keep the operating point close to the middle of the linear part of the correlation function. In experiments, we utilized f = 1.2 nm that qualitatively match with a half of typical distance between IFI signal maxima at the wavelength scan. The IFI response mean value obtained in experiments is approximately equal to 0.4 (Fig. 35.5 (c)) that demonstrated not optimal f, however it can be easily corrected. Strictly speaking, the wavelength shift f depends on the IFI parameters (number of excited modes, fiber length, refractive index profile etc.) and can be analyzed theoretically. In order to investigate stability of the IFI response calculated by the SCF in strong fading conditions, we recorded the IFI signal during 300 s, when both sinusoidal fiber length modulation (applied voltage amplitude 10 V) and slowly changing temperature (from 20 to 40 °C) were applied to the MMF as described in Sect. 3. Figure 35.7 demonstrates the comparison of the primal IFI signal (i.e. signal recorded at certain wavelength without additional processing) (a) and the IFI response calculated by the SCF (b). Significantly higher stability is clearly observed: amplitude deviation limit drops form 66% of amplitude’s mean value for the primal

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signal to 5% of amplitude’s mean value for the SCF-processed signal. Furthermore, the improvement of the signal shape is clearly observed (insets in Fig. 35.7). It should be noted that the correlation approach considered in present work is valid (has proper linearity) for limited range of external perturbation amplitudes. However, this is a common property of IFIs that reveals as a saturation part in the averaged amplitude characteristics [19]. The proposed in this work optical frequency scanning method has two potential advantages over the other averaging methods of the IFI signal. Firstly, it can be operated in real-time along with averaging over a fiber cross-section (spatial averaging), while other averaging techniques (ensemble averaging and averaging over “long realization”) are not capable for a real-time operation [20]. However, unlike spatial averaging, it does not require multichannel signal recording that simplifies an optical scheme. Secondly, unlike the mentioned above averaging methods mainly aimed at the signal fading elimination, the use of shifted correlation function enables a linear sensor response to external fiber perturbations in addition to its fading elimination function.

35.5

Conclusion

In this work, we considered an intermodal fiber interferometer with wavelength scanning light source. We demonstrated the potential to obtain stable and linear IFI response to external fiber perturbations, applying the correlation processing with a wavelength shift between the reference and the signal laser wavelength scans. The effectiveness of the proposed method was experimentally confirmed for the case of sine-shaped fiber length modulation. Our experimental setup was based on the SMS structure and the FBG interrogator with scanning laser, however it does not limit the use of other possible schemes providing frequency modulation of the light source. The results of the work can be useful for fiber specklegram sensors development since the proposed methodology enables a real-time sensor operation. Acknowledgements The reported study was funded by RFBR, project number 19-32-90238.

References 1. M. Zyczkowski, M. Karol, P. Markowski, M.S. Napierala, Simple fiber optic sensor for applications in security systems, in Proceedings of SPIE, Amsterdam ed. by E.M. Carapezza, C. Tsamis, P.G. Datskos, vol. 9248 (2014), p. 92480B. https://doi.org/10.1117/12.2066816 2. P.J. Pinzon, D.S. Montero, A. Tapetado, C. Vazquez, Dual-wavelength speckle-based SI-POF sensor for cost-effective detection of microvibrations. IEEE J. Sel. Top. Quantum Electron. 23(2), 7572087 (2017). https://doi.org/10.1109/JSTQE.2016.2611596

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3. J.J. Wang, S.C. Yan, F. Xu, Speckle-based fiber sensor for temperature measurement, in International Conference on Optical Communications and Networks (ICOCN) (IEEE, Wuzhen, 2017), pp. 1–3. https://doi.org/10.1109/ICOCN.2017.8121187 4. F. Musin, P. Mégret, M. Wuilpart, Fiber-optic surface temperature sensor based on modal interference. Sensors (Switzerland) 16(8), 1189 (2016). https://doi.org/10.3390/s16081189 5. V.M. Sperandio, M.J. Pontes, M.J. Neto, L. Goncalves, A new optical pressure sensor interrogated by speckles pattern for oil industry, in Proceedings of SPIE, Pestana Hotel and Convention Centre Curitiba, ed. by H.J. Kalinowski, J.L. Fabris, W.J. Bock, vol. 9634 (2015), p. 96347W. https://doi.org/10.1117/12.2185464 6. A. Rodríguez-Cuevas, E.R. Peña, L. Rodríguez-Cobo, M. Lomer, J.M. López-Higuera, Low-cost fiber specklegram sensor for noncontact continuous patient monitoring. J. Biomed. Opt. 22, 037001 (2017). https://doi.org/10.1117/1.JBO.22.3.037001 7. F. Feng, W. Chen, D. Chen, W. Lin, S.-C. Chen, In-situ ultrasensitive label-free DNA hybridization detection using optical fiber specklegram. Sens. Actuat. B-Chem. 272(11), 160– 165 (2018). https://doi.org/10.1016/j.snb.2018.05.099 8. E. Fujiwara, L.E. Silva, H.E. Freitas, Y.T. Wu, C.M.B. Cordeiro, Optical fiber chemical sensor based on the analysis of fiber specklegrams characteristics, in Proceedings of 2018 SBFoton International Optics and Photonics Conference, Campinas, (2018), pp. 1–5. https:// doi.org/10.1109/SBFoton-IOPC.2018.8610933 9. B. Wang, C. Huang, R. Guo, T. Francis, A novel fiber chemical sensor using inner-product multimode fiber speckle fields, in Proceedings of SPIE, San Diego, ed. by F.T.S. Yu, R. Guo, S. Yin, vol. 5206, (2003), pp. 299–304 10. M.J. Murray, A. Davis, C. Kirkendall, B. Redding, Speckle-based strain sensing in multimode fiber. Opt. Express 27(20), 28494–28506 (2019). https://doi.org/10.1364/OE.27.028494 11. F.M. Reis, P.F. Costa Antunes, N.M.M. Maia, A.R. Carvalho, P.S.B. André, Structural health monitoring suitable for airborne components using the speckle pattern in plastic optical fibers. IEEE Sens. J. 17(15), 4791–4796 (2017) 12. W. Chen, F. Feng, D. Chen, W. Lin, S.-C. Chen, Precision non-contact displacement sensor based on the near-field characteristics of fiber specklegrams. Sens. Actuators A Phys. 296(9), 1–6 (2019). https://doi.org/10.1016/j.sna.2019.06.010 13. G. Mu, Y. Liu, Q. Qin, Z. Tan, G. Li, M. Wang, F. Yan, Refractive index sensing based on the analysis of d-shaped multimode fiber specklegrams. IEEE Photon. Technol. Lett. 32(8), 485–488 (2020). https://doi.org/10.1109/LPT.2020.2980574 14. V. Varyshchuk, Y. Bobitski, H. Poisel, Deformation sensing with a multimode POF using speckle correlation processing method. Opto-Electron. Rev. 25(1), 19–23 (2017). https://doi. org/10.1016/j.opelre.2017.02.002 15. S. Razmyar, M.T. Mostafavi, A novel multi-mode fiber optic accelerometer: an intelligent sensor, in Proceedings of SPIE, Orlando, ed. by C.S. Baldwin, H.H. Du, A. Mendez, vol. 10654, p. 106540I (2018). https://doi.org/10.1117/12.2305164 16. Y. Liu, Q. Qin, H.-H. Liu, Z.-W. Tan, M.-G. Wang, Investigation of an image processing method of step-index multimode fiber specklegram and its application on lateral displacement sensing. Opt. Fiber Technol. 16(8), 48–53 (2018) 17. I. Chapalo, N. Ushakov, O. Kotov, Utilization of FBG interrogator for the analysis of multimode fiber interferometer signals induced by laser frequency modulation, in Proceedings of IEEE International Conference on Electrical Engineering and Photonics, EExPolytech ed. by E. Velichko (2019), pp. 325–327. https://doi.org/10.1109/EExPolytech.2019.8906788 18. I. Chapalo, A. Petrov, A. Theodosiou, K. Kalli, O. Kotov, Multimode CYTOP fiber interferometric response to laser wavelength scanning, in Proceedings of SPIE, ed. by K. Kalli, P. Peterka, C.-A. Bunge, vol. 11355, (2020), p. 11550X. https://doi.org/10.1117/12. 2559459

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19. O.I. Kotov, M.A. Bisyarin, I.E. Chapalo, A.V. Petrov, Simulation of a multimode fiber interferometer using averaged characteristics approach. J. Opt. Soc. Am. B: Opt. Phys. 35(8), 1990–1999 (2018). https://doi.org/10.1364/JOSAB.35.001990 20. I. Chapalo, A. Petrov, D. Bozhko, M.A. Bisyarin, O.I. Kotov, Averaging methods for a multimode fiber interferometer: experimental and interpretation. J. Lightwave Technol. (early access). https://doi.org/10.1109/JLT.2020.3002617

Chapter 36

Development of a Monitoring System the Flow of Charged Particles for Analysis of the Nanosatellite Flight Path Dennis Malygin and Jean R. Stepanov

Abstract The article presents a set of instrumentation for identification and classification according to the energy spectrum of charged particles that affect the nanosatellite of the CubeSat form factor. The described kit will make it possible to better understand the effect of radiation on the available industrial electronic-component base of on-board electronics. The architecture and features of CubeSat are based on the «Synergy» multipurpose block-modular platform. Keywords Nanosatellite

36.1


Platform “synergy”
Radiation detector
Photodiode

Introduction

Initially, the space industry produced large and complex spacecraft, hand-made by large teams of engineers with a huge budget, which is achievable by only a few large government agencies. However, over the past two decades, the space industry has shown increased interest in a small class of CubeSat spacecraft (ultra-small spacecraft, the so-called nanosatellites), moreover, advances in the miniaturization of electronic components have stimulated the development of the electronic component base of a new generations. Initially, the nanosatellites were presented as educational tools or low-cost demonstration platforms for technologies that could be developed and launched within two years. Recently, however, more sophisticated nanosats missions have been formulated and implemented, indicating that the transition has begun from exclusively educational and technological demonstration platforms to offers for low-cost real scientific missions with potentially high value in terms of generating income from commercial activities. Despite the significant progress achieved in the development of the small satellites over the past decade,

D. Malygin (&)
J. R. Stepanov Astronomikon Laboratory, 192286 St. Petersburg Alpiyskiy Alley, 29, of. 202, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_36

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some fundamental questions still arise about the possibilities, limitations and, ultimately, their scientific and commercial value (Fig. 36.1). The article is one of a series of works on the description of the concept of the basic set of instrumentation of the «Synergy» platform for measuring the situation of the Nanosatellites flight path. It is important to note that the class of devices under consideration imposes strict mass-dimensional restrictions on the elemental component base (ECB) of the instrumentation, and there are similar projects: a multi-wire detector for nanosatellites [2] or Radiation Monitor RADMON [3–6] or A Miniaturized Detector for a CubeSat Mission to Measure Relativistic Particles in Near-Earth Space [7] (Fig. 36.2). The instrumentation kit is a solid-state semiconductor device in the form of a portable silicon photodiode radiation detector in the 5 keV–10 MeV range, capable of detecting low-energy gamma radiation. The device consists of a matrix of silicon PIN diodes of a large area (100 mm2) with reverse bias (the choice of the detector candidate is presented in Table 36.1). Silicon diodes are an acceptable choice for a large number of ECBs, especially when it comes to heavily charged particles. Let us explain this point in more detail. Consider the types of ionizing radiation detectors: • Gas discharge (alpha, beta, gamma, neutrons, photons, x-rays, ionization chambers, detection units based on gas discharge counters). • Scintillation detectors and counters. • Semiconductor (silicon, germanium, other materials). • Detectors based on diamond. • Photodiode-based X-ray receivers.

Fig. 36.1 Nanosatellites based on the «Synergy» platform

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The advantages of semiconductor detectors in comparison with other types of detecting devices are noted in detail in handbook [8]. It is also possible to register and more high-energy particles [9]. As applied to nanosatellites, the following advantages of silicon semiconductor detectors can be distinguished: • Light weight and compactness (up to 10 mm2) - the ability to place a sensitive element on the solar panel without the threat of shading. • High manufacturability of detectors and related electronics. • Low cost. • High resistance to external influences and stability of parameters. • The possibility of registering particles in a wide energy range at the same time (unlike, for example, gas-filled ones, in which, due to the low density of the gas, high-energy and weakly ionizing particles are inefficiently recorded).

Fig. 36.2 Appearance and location of instrumentation

Table 36.1 PIN photodiode candidates Name

Manufacturer

Value (active area, mm2)

AXUV100 AXUV100GX PS100-2-THD PS100-5-THD PS100-6-THD PS100-7-CER-PIN SMP2000G-MV S3590-08 S3590-18 S6775

IRD IRD First Sensor First Sensor First Sensor First Sensor SemeLab Hamamatsu Photonics Hamamatsu Photonics Hamamatsu Photonics

100 100 100 100 100 100 100 100 100 26.4

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• High energy resolution due to lower semiconductor ionization energy. • High temporal resolution due to significant charge carrier mobility. It is worth noting that the analysis of the nanosatellite flight path is also important for large spacecraft and various electron optical systems based on CCD and CMOS matrices [10–12]; as well as devices with AI and adaptive technologies [13, 14].

36.2

Materials and Methods

Satellite platform “Synergy” (SP) [1] is designed for the assembly of nanosatellites, crucial educational, technological and scientific problems in Earth orbits. The platform is designed according to the specification of CubeSat form factor [15], consists of housing and supporting systems to which the target system is integrated, which is determined by the purpose of a particular satellite. The concept of spacecraft (SC) of this class is based on the principle of using a simple elemental base (COTS), which is not specifically intended for use in outer space. Therefore, the problem arises of analyzing the effect of particles on satellite components during flight. Since the platform is multi-purpose, i.e. designed to perform a variety of tasks, its operation is assumed to be in arbitrary low Earth orbits and in arbitrary orientation modes in an indefinite period of time. In addition, there are various design options. Figure 36.3a presents a model of the studied platform. The case (cube with edge length of 10 cm) consists of a frame and two covers. On the outer faces are installed PCBs with photocells of a rectangular shape, and on the side surfaces – trapezoidal. In the grooves on the inner surface of frame 4 PCBs are fixed with the components supporting systems. Structural elements, unnecessarily complicates the calculations are not included in model. Lids can be made in 4 versions, and the frame – in 2 (Fig. 36.3b). Of the 8 different variants of the

Fig. 36.3 Variants of design the platform

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Fig. 36.4 Variants of design the cases: lattice (a Skelet_20, b Skelet_40), c panel-rod (Mono), d rod (Rails)

combinations investigated cases platform in two versions: with a minimal surface area and the maximum (Fig. 36.4a, b). In addition, the most common cases of hull designs (Fig. 36.4c, d) of the CubeSat form factor spacecraft are investigated. Motion model In the framework of study, SP motion is modeled on one round orbit without taking into account the evolution of its parameters. The center of mass motion (orbital) and movement around the center of mass SP (angular-howling) considered independently. Earth orbit parameters, which are launched on the spacecraft form factor CubeSat (July, 2020), shown in Fig. 36.5. It is assumed that the platform under study operates in orbits with parameters whose values do not go beyond the presented ranges. Based on these data, the height H of most orbits (* 98.5%) does not exceed the value of 1000 km, and most of the orbits (* 97%) are near-circular (with eccentricity e < 0.02, the ellipticity of such orbits in the framework of the problem to be solved can be neglected and considered circular). Based on this, the flight path is studied only in low circular orbits, i.e. for orbits with parameters e = 0, H = 200… 2000 km. The parameters of position orbital plane are not limited (an explanation will follow). In modeling, the following coordinate systems are used: geocentric equatorial inertial (CK-1) and mobile (CK-2), barycentric coupled (CK-3) and basic (CK-4) (Fig. 36.6). The orbital motion is considered unperturbed and is determined by the elements of orbit: the longitude of ascending node X, the inclination i, the radius of orbit r, and the argument of latitude u. For each position u = 0… 2p, the coordinates of center of mass SP in CK-1 (X, Y, Z) are determined. The angular motion SP is assumed to be free spherical around the center of mass and is described by the Euler kinematic equations in CK-4: for each position in orbit, the coordinates of six normals of the faces cube are determined. Input parameters are angular velocities around the axes xx4 ; xy4 ; xz4 and the initial value of Euler angles u0 ; h0 ; w0 .

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Fig. 36.5 Distribution CubeSat satellites orbital parameters in the first days after launch [3], [4]

0

0

Fig. 36.6 Coordinate systems CК-1: OXYZ, CК-2: Oxyz, CК-3: O X3 Y3 Z3 , CК-4: O X4 Y4 Z4

In this project, the PIN-diode of the First Sensor company is used [16, 17]. Alpha, beta, gamma, and X-ray radiation can be detected with silicon PIN photodiodes either directly via the absorption in the crystal lattice or indirectly via the measurement of the luminescence radiation of a scintillation crystal. The Series X from First Sensor features optimized silicon PIN photodiodes, which form wide, fully depleted space-charge regions even at low reverse voltages in order to guarantee the maximum absorption of radiation. For high-energy radiation we offer detectors with a CsI:TI scintillation crystal. Special Features: • Very low dark current signals; • Very low capacitance levels; • Minimal series resistance at full depletion;

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Interactions of particles with matter are distinguished by three effects: • The photo-electric effect; • Compton scattering; • Pair-production. The final device will be a gamma radiation detector. When gamma radiation (wavelength less than 10 microns.) falls into the depletion region of the PIN photodiode, it produces a small charge (the Compton effect is incoherent scattering of photons by free electrons, incoherence means that photons do not interfere before and after scattering; the effect is accompanied by a change in the frequency of photons, part of the energy of which, after scattering, is transferred to electrons). The formation of an electron pair requires at least 1.022 MeV, for Si the value is 3.65 eV. When a Si atom receives 3.65 eV, then Si releases an electron. To obtain maximum sensitivity, it is necessary to use additional rating chains (Fig. 36.7). In the circuit, the PIN diode operates with reverse bias, and the presence of radiation is determined by ultra-small changes in the reverse current. Unfortunately, in addition to the useful signal, the diode has a dark current, which must be compensated. In addition, the magnitude of the detected change in current is very small, and the diode resistance is quite large (tens of megohms). As a result, to obtain high-quality instrumentation, it is necessary to apply a two-stage circuit with the following strict restrictions:

Fig. 36.7 Schematic diagram of the prototype instrumentation (detected pulse 100–500 mV, noise 80 mVp, DC bias 1.93 V)

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• to choose amplifiers with high input impedance, minimal bias current, low temperature drift, low noise; • to ensure maximum matching of input resistances in the first link of the amplifier in order to minimize the temperature error when changing the bias current of the amplifier; • to ensure constructive minimization of leakage currents due to the correct wiring and use of capacitors and resistors with low leakages, etc. Thus, we carry out the initial calculation. Consider a small current and voltage: For example, Cs-137(662 keV) produces 662 keV/3.65 eV = 181 K electrons in ideal case not real case. 1 electron has 1.6E-19 coulombs. Total charge is 181Kx1.6E-19 = 28.96E-15 coulombs. Charge amplifier formula Vout ¼ Q C1 , Vout = 28.96E-15/2E-12 = 14.48 mV where C1 = 2pF (step C1 between 1pF to 5pF is available, while smaller C1 comes to a high pulse and bigger C1 reduces noise). Note that Differentiator amplifier changes voltage to a pulse and the parameters are determined by formulas 36.1 and 36.2: Vout ¼ R2 C Vout ¼

d 1 Vi ; f  : dt 2pR1 C

R2 1 ; s ¼ R1 C: Vi ; f  2pR1 C R1

ð36:1Þ ð36:2Þ

Further, the parameters of the Integrator amplifier are determined by formulas 3 and 4: Z 1 1 Vi ; f  : ð36:3Þ Vout ¼ R1 C 2pR2 C Vout ¼

36.3

R2 1 ; s ¼ R2 C: Vi ; f  2pR2 C R1

ð36:4Þ

Results

The final, transformable scheme is presented in Fig. 36.8. Charge amplifier converts a small charge to DC voltage. Diffrentiator amplifier converts DC voltage to a pulse with Pole Zero Cancellation. Integrator amplifier converts a pulse to Gauss pulse instead of cusp pulse and separates it from noise. Cusp pulse is ideal for Integrator amplifier. When processing a technical solution, it is important to note several fundamental points for the amplifier (Fig. 36.9):

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Fig. 36.8 Advanced prototype circuit diagram

Fig. 36.9 Test work check

36.4

Conclusion

A main advantage of semiconductor detectors is the small ionization energy, independent of both the energy and type of incident radiation. However, it is necessary to consider several nuances: 1) Leak current • Guard ring is required in amplifier input for leak current. • PCB laminate with glass epoxy FR-4 is required. 2) Sensors • Large possible detection range, 1 keV–1 MeV. • Low capacitance to minimize noise, 20pF–50pF. • Negligible leakage current under reverse bias.

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References 1. D.V. Malygin, The multi-purpose block-modular platform “Synergy” for the assembly of nanosatellites. J. Instrument Eng. 8, 179–184 (2018) 2. O.V. Philonin, К.S. Nasonov, Miniature multi-wire detector for nanosatellites. J. Instrument Eng. 62(5), 492–498 (2019) 3. J. Gieseler, P. Oleynik, H. Hietala, R. Vainio, H.P. Hedman, J. Peltonen, A. Punkkinen, R. Punkkinen, T. Santti, E. Hæggstrom, J Praks, P. Niemela, B. Riwanto, N. Jovanovic, M.R. Mughal, Radiation monitor RADMON aboard Aalto-1 CubeSat: first results. Adv. Space Res. 66, 52–65 (2019) 4. P. Oleynik, R. Vainio, A. Punkkinen, et al., Calibration of RADMON radiation monitor onboard Aalto-1 CubeSat. Adv. Space Res. (2020). https://doi.org/10.1016/j.asr.2019.11.020 5. J. Peltonen, H.P. Hedman, A. Ilmanen, M. Lindroos, M. Maattanen, J. Pesonen, R. Punkkinen, A. Punkkinen, R. Vainio, E. Valtonen, T. Santti, J. Pentikainen, E. Hæggstrom, Electronics for the RADMON instrument on the Aalto-1 student satellite. in 10th European Work. Microelectron. Education EWME 2014 (2014), pp. 161–166 6. A. Kestila, T. Tikka, P. Peitso, J. Rantanen, A. Nasila, K. Nordling, H. Saari, R. Vainio, P. Janhunen, J. Praks, M. Hallikainen, Aalto-1 nanosatellite – technical description and mission objectives. Geosci. Instrum. Methods Data Syst. 2, 121–130 (2013) 7. G.S. Quintin, M. Abhishek, L. Xinlin, REPTile: a miniaturized detector for a cubesat mission to measure relativistic particles in near-earth space. in 4th Annual AIAA/USU Conference on Small Satellites, SSC10-VIII-1, Schiller & Mahendrakumar 8. M.L. Baranochnikov, Radiation receivers and detectors. Directory. Moscow. p. 1041 (2017) 9. A.M. Bykov, M.E. Kalyashova, D.C. Ellison, S.M. Osipov, High-energy cosmic rays from compact galactic star clusters: particle fluxes and anisotropy. Adv. Space Res. 64, 2439–2444 (2019) 10. D. Dyubov, O.Y. Tsybin Particles-on-surface sensor with potential barriers embedded in a semiconductor target. J. Phys.: Conf. Ser. 1326, 12 (2019) 11. A.V. Devyatkin, A.K. Tsytsulin, A.I. Bobrovsky, A.V. Morozov, D.L. Gorshanov, V.A. Pavlov, Adaptation of frame frequency to observation stages at control of spacecraft convergence. J. Phys: Conf. Ser. 1236, 012069 (2019) 12. V. Dyumin, K. Smirnov, V. Davydov, N. Myazin, Charge-coupled device with integrated electron multiplication for low light level imaging. in Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics, EExPolytech 2019 (2019), pp 308–310 13. A.K. Tsytsulin, A.I. Bobrovskiǐ, A.V. Morozov, V.A. Pavlov, M.A. Galeeva, Using convolutional neural networks to automatically select small artificial space objects on optical images of a starry sky. J. Opt. Technol. (A Transl. Opt. Zhurnal) 86, 627 (2019) 14. A.I. Bobrovsky, M.A. Galeeva, A.V. Morozov, V.A. Pavlov, A.K. Tsytsulin, Automatic detection of objects on star sky images by using the convolutional neural network. J. Phys: Conf. Ser. 1236, 012066 (2019) 15. California Polytechnic State University, «CubeSat Design Specification REV 13», p. 42 16. Silicon Gamma Radiation Detector [Electronic resource]. http://einstlab.web.fc2.com/ Xdetector/detector.html (date of the application: 01.04.2020) 17. Gamma spectroscopy [Electronic resource]. http://einstlab.web.fc2.com/Gamma/ spectroscopy.html (date of the application: 01.04.2020)

Chapter 37

The UV-Vis Transmission Spectra of Ferromagnetic Fluids Arseniy Alekseev, Elina Nepomnyashchaya , Elena Velichko , and E. Shan

Abstract In previous studies, it was found that a loss of magnetic fluids aggregation stability is possible upon dilution. It leads to the formation of large aggregates and their subsequent precipitation. As a result, optical properties of magnetic fluids, in particular absorption and scattering, change. In this work, we used simple and effective scheme for recording the absorption spectra of magnetic fluids. As a result of our study, we measured UV-Vis transmission spectra of ferromagnetic fluids of different concentrations and with different types of stabilization. The correlation between the solution concentration and its absorption spectrum was established. Based on the data obtained, we can say that the light transmission decreases with increasing concentration. We also observed a decrease in transmission for liquids more prone to aggregation. Our data allow us to expand our understanding of the optical properties of magnetic fluids and can contribute to their more effective use in magnetic field-controlled information transmission systems, as well as other fields of application of the optical properties of magnetic fluids. Keywords Ferromagnetic fluids Absorption Spectroscopy





Transmission spectra
UV-Vis spectra


A. Alekseev
E. Nepomnyashchaya (&)
E. Velichko Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., St. Petersburg 195251, Russia e-mail: [email protected] A. Alekseev e-mail: [email protected] E. Velichko e-mail: [email protected] E. Shan Jiangsu Normal University, 101 Shanghai Road, Tongshan District, Xuzhou, Jiangsu, China e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_37

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Introduction

It is known that due to the large number of useful properties magnetic fluids are applied in almost every field of human activity [1]. For example, in electronics, they are used as thermal storage or magnetic media in electrical devices [2], in engineering, for sealing rotating shafts [3], in ecology for collecting of oil products [4]. In medicine they are used for the treatment of oncological diseases by hyperthermia [5, 6], as well as in the role of contrast agents in tomography. Such a wide range of applications of magnetic fluids makes the issue of detailed study of their properties very relevant [7]. Of particular interest are the optical properties of magnetic fluids, as their use in data recording and transmission systems controlled by a magnetic field has a perspective. [8]. Operation of such devices is based on the fact that under the influence of a magnetic field birefringence, dichroism, light scattering and absorption occur in magnetic fluids [7, 9]. Therefore, the evaluation of optical properties of the magnetic fluids is very important. Absorption spectra of magnetic fluids based on Fe3O4 was studied in [10–12]. However, the effects of surfactants and concentrations of magnetic nanoparticles on the spectral characteristics of the absorption was not considered. In addition, the absorption of magnetic fluids is mainly investigated in the infrared region of the spectrum [12–14]. Since the magnetic fluid is highly dispersed colloidal solution consisting of magnetic particles of size 50–200 Å suspended in water or organic liquids, the stability of the system and its aggregation stability is highly important [15]. Violation of the aggregation stability can be provoked by changing the concentration of the fluids at their dilution that in turn can involve the change of the absorption spectra of the investigated liquids [14, 16]. In this work we studied the UV-Vis absorption spectra [17] of magnetic fluids (Fe3O4) stabilized with surfactant or with electrical double layer around the magnetic particles.

37.2

Materials and Methods

37.2.1 Evaluation of the Absorption Spectra The light detected after its interaction with substance can be calculated according to the Beer–Lambert law [18] I ðlÞ ¼ I0 ekk l

ð1Þ

where I ðlÞ is the intensity of light passing through a substance of thickness l, kk is the absorption coefficient, I0 is the illuminating light intensity, k is the wavelength of light. The absorption index depends on the wavelength of light is equal to

37

The UV-Vis Transmission Spectra of Ferromagnetic Fluids

kk ¼

4pk k

329

ð2Þ

k is absorption factor. Thus, with increasing wavelength, the intensity of the light exiting the sample increases, i.e., absorption decreases. The light attenuation cross section is an individual constant characteristic of a particle, showing the light attenuation introduced by one particle. It consists of the scattering cross section rsca and absorption cross section rabs r ¼ rsca þ rabs

ð3Þ

For particles small compared to the wavelength of light, the Rayleigh approximation is applicable [19–21] and the cross sections can be described by following equations rsca ¼


  2 ~  1Þ 8 2pa 4 ðm  ðm ~ 2 þ 2Þ  3 k

rabc ¼


2  ~  1Þ 8pa ðm Im ~ 2 þ 2Þ k ðm

ð4Þ

~ is complex refractive index of particles with respect to a where a is particle size, m liquid base ~ ¼ m þ ik m k¼

e00 2m

e ¼ e0 þ ie00 where e – effective dielectric constant. Equation (4) shows that the absorption is directly proportional to the size of the particles in the solution: the larger the size, the greater the absorption. From this we can conclude that solutions of magnetic fluids in which clusters are formed will absorb less light than solutions with no aggregates ang higher aggregation stability. Note that the scattering of light by a particle also increases with increasing size of this particle.

37.2.2 Absorption Spectra Detection The experimental setup consists of following main parts: radiation source, sample cell, spectrum analyzer, computer, optical fibers (Fig. 37.1).

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Fig. 37.1 Scheme for absorption spectra detection

The L10290 (Hamamatsu) light source, which consist of deuterium and halogen lamps, was used as a radiation source in the scheme. The emission spectrum of this source lies in the range 200–1100 nm (see Fig. 37.2), which makes it possible to detect absorption in a wide range of wavelengths [22]. From the light source, light via optical fiber is directed to the sample cell. It makes the radiation more directed and delivers it to the cell with less power losses. The 1  3 cm quartz cell with the fluid was in a darkened case to avoid reflection and external illumination. In this research we were interested in detection of UV-Vis spectra, so we used C10083MD (Hamamatsu) spectrum analyzer. This analyzer allowed us to provide spectroscopic measurements with high accuracy in 200–850 nm spectral region. To process the spectra, a computer with special software was used. The experiment was conducted in a darkened room to avoid additional exposure. With the described scheme we obtained the value of the radiation intensity passing through the sample cell. As a reference we used distilled water. Fig. 37.2 Emission spectrum of a light source (deuterium and halogen lamps)

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The UV-Vis Transmission Spectra of Ferromagnetic Fluids

331

37.2.3 Test Samples In this work, we studied solutions of magnetic fluids stabilized with surfactant or ions that were diluted with distilled water. The average particle size in both fluids was 10 nm. Ion-stabilized magnetic fluid is stabilized by adding HCl, as a result, a positive charge is formed on the surface of the oxide, pH = 5 [23]. The concentrations used in the experiment were 0.0078 vol.%; 0.0039 vol.%; 0.002 vol.% The surfactant stabilized magnetic fluid was studied in concentrations 0.0029 vol.%, 0.0012 vol.%, 0.00012 vol.%.

37.3

Results and Discussion

60000 50000 40000 30000 20000 10000 0

I (rel.un.)

I (rel.un.)

Based on experimental data, we obtained absorption spectra of magnetic fluids in different concentrations. The results of absorption spectra for ion stabilized magnetic fluid and surfactant stabilized magnetic fluid are presented in Figs. 37.3 and 37.4 respectively.

200

400

600

800

48000 40000 32000 24000 16000 8000 0 200

400

600

λ (nm)

λ (nm)

(a)

(b)

800

I (rel.un.)

40000 32000 24000 16000 8000 0 200

400

600

800

λ (nm)

(c) Fig. 37.3 Light absorption spectrum for ion stabilized magnetic fluids for the concentration of (a) 0.0078 vol.%, (b) 0.0039 vol.%, and (c) 0.002 vol.%

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As one can see from the figures, magnetic fluid absorbs light almost in the entire UV-Vis spectral region, except for infrared. With a decrease in concentration of magnetic fluid while its dilution, the absorption in the range *400–650 nm decreases noticeably. While the absorption in UV spectral diapason is approximately constant for first two concentrations 0.0078 vol.% (0.0029 vol.%) and 0.0039 vol.% (0.0012 vol.%) concentration and decreases for 0.002 vol.% (0.00012 vol.%.). This can be explained by scattering increase at some point of dilution. As it was noticed in Sect. 2.2, the scattering intensity increases with the size of particles growth, so detected reduction of the UV light intensity (along with the visible light intensity reduction) can be explained by aggregation. We suppose, that aggregation is provoked by violation of sedimentation stability by dilution. Violation of aggregation stability is observed for both investigated fluids. The observed differences in the absorption spectra of magnetic fluids with different stabilizers (ions and surfactants) consist in the difference of absorption in the near UV region. For ion-stabilized magnetic fluids, a slightly larger absorption peak is observed in the region of 300 nm, which may be due to the influence of the stabilizer on the absorption spectrum.

50000

I (rel.un.)

I (rel.un.)

60000 40000 20000

40000 30000 20000 10000

0

0 200

400

600

800

200

400

600

λ (nm)

λ (nm)

(a)

(b)

800

I (rel.un.)

40000 30000 20000 10000 0 200

400

600

800

λ (nm)

(c) Fig. 37.4 Light absorption spectrum for ion stabilized magnetic fluids for the concentration of (a) 0.0029 vol.%, (b) 0.0012 vol.%, and (c) 0.00012 vol.%

37

The UV-Vis Transmission Spectra of Ferromagnetic Fluids

37.4

333

Conclusion

In the paper we suggested simple and effective experimental setup for UV-Vis absorption spectra investigation. Studies on the detection of absorption spectra of magnetic fluids at various concentrations and with different stabilizers have revealed the loss of their aggregation stability due to dilution. In the future, we plane to compare these results with the results obtained by other methods and confirm the theory advanced [23]. The influence of magnetic field on the absorbance spectra of magnetic fluids also should be investigated, because some papers say about aggregation in magnetic fields [24]. The detection of concentrations at which there is a loss of aggregation stability for various magnetic fluids is an important task in modern science and technology. Acknowledgements This research work was supported by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020”. The authors are grateful to E.E. Bibik and I.V. Pleshakov for providing ferrofluid samples and fruitful discussions.

References 1. S.E. Logunov, V.V. Davydov, M.G. Vysoczky, O.A. Titova, Peculiarities of registration of magnetic field variations by a quantum sensor based on a ferrofluid cell. J. Phys: Conf. Ser. 1135, 12069 (2018). https://doi.org/10.1088/1742-6596/1135/1/012069 2. L. Pislaru-Danescu, A.M. Morega, G. Telipan, M. Morega, J.B. Dumitru, V. Marinescu, Magnetic nanofluid applications in electrical engineering. IEEE Trans. Magn. 49, 5489–5497 (2013). https://doi.org/10.1109/TMAG.2013.2271607 3. L. Matuszewski, Z. Szydlo, The application of magnetic fluids in sealing nodes designed for operation in difficult conditions and in machines used in sea environment. Polish Marit. Res. 15, 49–58 (2008) 4. A. Shishkin, V. Mironov, V. Lapkovskis, J. Treijs, A. Korjakins, Ferromagnetic sorbents for collection and utilization of oil products. Key Eng. Mater. 604, 122–125 (2014). https://doi. org/10.4028/www.scientific.net/KEM.604.122 5. A. Miaskowski, A. Krawczyk, Magnetic fluid hyperthermia for cancer therapy. Electr. Rev. 87, 125–127 (2011) 6. E.K. Nepomnyashchaya, E.N. Velichko, I.V. Pleshakov, E.T. Aksenov, E.A. Savchenko, Investigation of ferrofluid nanostructure by laser light scattering: Medical applications. In Journal of Physics: Conference Series (2017). https://doi.org/10.1088/1742-6596/841/1/ 012020 7. S.E. Logunov, V.V. Davydov, M.G. Vysoczky, M.S. Mazing, New method of researches of the magnetic fields force lines structure. J. Phys. Conf. Ser. 1038, 012093 (2018). https://doi. org/10.1088/1742-6596/1038/1/012093 8. H.E. Horng, J.J. Chieh, Y.H. Chao, S.Y. Yang, C.-Y. Hong, H.C. Yang, Designing optical-fiber modulators by using magnetic fluids. Opt. Lett. 30, 543 (2005). https://doi.org/ 10.1364/ol.30.000543 9. A.V. Prokof’ev, I.V. Pleshakov, M. Shlyagin, P.M. Agruzov, E.E. Bibik, Y.I. Kuz’min, Noise characteristics of the optical response of ferrofluids to a magnetic field. Tech. Phys. Lett. 45, 743–745 (2019). https://doi.org/10.1134/S1063785019080121

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10. B.K. Pandey, A.K. Shahi, J. Shah, R.K. Kotnala, R. Gopal, Optical and magnetic properties of Fe2O3 nanoparticles synthesized by laser ablation/fragmentation technique in different liquid media. Appl. Surf. Sci. 289, 462–471 (2014). https://doi.org/10.1016/j.apsusc.2013.11.009 11. R.A. Ismail, G.M. Sulaiman, S.A. Abdulrahman, T.R. Marzoog, Antibacterial activity of magnetic iron oxide nanoparticles synthesized by laser ablation in liquid. Mater. Sci. Eng. C 53, 286–297 (2015). https://doi.org/10.1016/j.msec.2015.04.047 12. N. Inaba, H. Miyajima, H. Takahashi, S. Taketomi, S. Chikazumi, Magneto-optical absorption in infrared region for magnetic fluid thin film. IEEE Trans. Magn. 25, 3866– 3868 (1989). https://doi.org/10.1109/20.42459 13. K.J. Smirnov, V.V, Davydov, S.F. Glagolev, N.S. Rodygina, N.V. Ivanova, Photocathodes for near infrared range devices based on InP/InGaAs heterostructures. J. Phys. Conf. Ser. 1038, 012102 (2018). https://doi.org/10.1088/1742-6596/1038/1/012102 14. R.S. Smerdov, T.V. Bocharova, V.S. Levitskii, K.G. Gareev, V.A. Moshnikov, E.I. Terukov, Spectroscopic properties of c-irradiated FemOn–SiO2 composite nanoparticles. Phys. Solid State 58, 919–923 (2016). https://doi.org/10.1134/S1063783416050243 15. L. Vékás, D. Bica, O. Marinica, Magnetic nanofluids stabilized with various chain length surfactants (2006) 16. O.S. Vezo, K.G. Gareev, D.V. Korolev, I.A. Kuryshev, S.V. Lebedev, V.A. Moshnikov, E.S. Sergienko, P.V. Kharitonskii, Aggregate stability and magnetic characteristics of colloidal FemOn–SiO2 particles obtained by sol–gel method. Phys. Solid State 59, 1008–1013 (2017). https://doi.org/10.1134/S1063783417050304 17. Y.V. Bogachev, J.S. Chernenco, K.G. Gareev, I.E. Kononova, L.B. Matyushkin, V.A. Moshnikov, S.S. Nalimova, The study of aggregation processes in colloidal solutions of magnetite-silica nanoparticles by NMR Relaxometry, AFM, and UV-Vis-Spectroscopy. Appl. Magn. Reson. 45, 329–337 (2014). https://doi.org/10.1007/s00723-014-0525-7 18. V.E. Privalov, V.G. Shemanin, Optimization of a differential absorption and scattering lidar for sensing molecular hydrogen in the atmosphere. Tech. Phys. 44, 928–931 (1999). https:// doi.org/10.1134/1.1259407 19. V.E. Privalov, A.V. Rybalko, P.V. Charty, V.G. Shemanin, Effect of noise and vibration on the performance of a particle concentration laser meter and optimization of its parameters. Tech. Phys. 52, 352–355 (2007). https://doi.org/10.1134/S1063784207030115 20. M.A. Bisyarin, O.I. Kotov, A.H. Hartog, L.B. Liokumovich, N.A. Ushakov, Rayleigh backscattering from the fundamental mode in multimode optical fibers. Appl. Opt. 55, 5041 (2016). https://doi.org/10.1364/ao.55.005041 21. M.A. Bisyarin, O.I. Kotov, A.H. Hartog, L.B. Liokumovich, N.A. Ushakov, Influence of a variable Rayleigh scattering-loss coefficient on the light backscattering in multimode optical fibers. Appl. Opt. 56, 4629 (2017). https://doi.org/10.1364/ao.56.004629 22. V. Privalov, V.G. Shemanin, On the determination of the minimum pulse energy in laser probing using harmonics of an Nd: YAG laser. Opt. Spectrosc. 82, 809–811 (1997) 23. E. Velichko, E. Nepomnyashchaya, A. Dudina, I. Pleshakov, E. Aksenov: Investigation of the interaction of ferromagnetic fluids with proteins by dynamic light scattering. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (2018). https://doi.org/10.1117/12. 2314615 24. E.K.Nepomnyashchaya, A.V. Prokofiev, E.N. Velichko, I.V. Pleshakov, Y.I. Kuzmin, Investigation of magneto-optical properties of ferrofluids by laser light scattering techniques. J. Magnet. Magnetic Mat. 431 (2017). https://doi.org/10.1016/j.jmmm.2016.10.002

Chapter 38

Calculation of Parameters of Positive Column in Laser Tubes of Variable Diameter Vadim Kozhevnikov , Vadim Privalov, Alexander Fotiadi, and Valery Shemanin Abstract Processes in the positive column of a direct current discharge in a gas in laser tubes of variable diameter have been considered. A system of equations is obtained that relates internal characteristics of the positive column (concentration of charged particles, electron temperature, and longitudinal electric field strength) to the external parameters of the column (varying radius of the discharge channel, gas inlet pressure and discharge current). These equations are derived from fairly general assumptions about gas in a laser. The charged particles motion equations, the charged particle balance equations and the electrons energy balance equation were used to obtain this system. This system of equations solves the problem. Numerical calculations were carried out and the internal characteristics of the positive column were calculated as a function of the axial distance along the tube at typical values of the external parameters of the column. Keywords Positive column plasma characteristics tubes Active element geometry





Variable diameter laser

V. Kozhevnikov (&)
V. Privalov
A. Fotiadi Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia e-mail: [email protected] V. Privalov e-mail: [email protected] A. Fotiadi e-mail: [email protected] V. Shemanin Novorossiysk Polytechnic Institute of Kuban State Technological University, Novorossiysk 353900, Russia e-mail: [email protected] V. Shemanin Branch of the V.G. Shukhov Belgorod State Technological University in Novorossiysk, Novorossiysk 353900, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_38

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Introduction

Traditionally, the cylindrical discharge tubes were used in the gas discharge lasers. However, the question was from the beginning whether the cylindrical geometry was energetically optimal. The simple models were proposed in which a larger laser gain was obtained in forms other than cylindrical ones [1, 2]. In particular, the conical shape of the discharge tube was considered as promising. However, the models considered were simple and did not take into account the dependence of the active medium characteristics on the tube longitudinal coordinate. Since as a rule in gas discharge tubes the active medium is the positive DC glow discharge column, it is necessary to study the effect of the discharge tube variable radius on the positive column parameters. Before the paper [3] similar studies during smooth change of the discharge channel radius were not performed, only the intermittent changing was studied [4]. This study is a logical continuation of the study in [3].

38.2

Formulation of the Problem and Its Solution

38.2.1 Formulation of the Problem We consider the positive column (PC) of the direct current discharge with the length of l in the monoatomic gas in the digit conditions, typical for the active medium of the gas-discharge lasers: the gas blousing pressure pH  10 mmHg, discharge current 10  Ip  100 mA, the discharge channel radius 1 < R < 5 mm. We assume that under such a discharge conditions the studied PC is: a) Three-component plasma consisting of the neutral atoms of one type, single-charge positive ions and electrons. Concentrations of these particles are – na, ni, ne, masses are – ma, mi, me, respectively. The ion mass is equal to the atom mass: mi = ma. b) The atoms concentration in the column is determined by equality: na = pH/kT, where pH is the gas pressure, Ta - its temperature in the discharge channel. c) The ions free-run length of ki is much lesser than the tube radius of: ki < fR(0) = 1. In particular, if the discharge channel geometry coincides with the caustic shape of the semiconfocal resonator main mode, the function fR(z) will be written in view: fR(z) = {1 + (z/l)2}1/2.

38

Calculation of Parameters of Positive Column …

337

Let us also consider, as in the classic description, that in these discharge conditions the following is fulfilled: 1) The column plasma is the quasi-neutral: ne  ni = n. 2) The column plasma is the poorly ionized that it means a little of frequencies of the charged particles collisions with each other (mee, mei, mii) in comparison with the frequencies of the electron - atomic (mea) and ion - atomic collisions (mia): mee, mei, mii 0, u  0, 0  m < 2p), it is possible to receive the exact solution (1)–(2): d N ¼ d N0 Ce0 ðu; k2 ða2  b2 Þ=4Þ
ce0 ðm; k2 ða2  b2 Þ=4Þ= ðCe0 ð0; k2 ða2  b2 Þ=4Þ
ce0 ðp=2; k2 ða2  b2 Þ=4ÞÞ

39

Radiation Power of He–Ne Laser …

347

where a and b are the ellipse semi-axes (b < a), Ce0(u, q) and ce0(m, q) are Mathieu functions (here and then we adhere to the symbols from [9, 10], in the different authors they differ), and from the boundary condition (2) it follows that k must be such a that:   1 a þ b k2 ða2  b2 Þ ; Ce0 ln ¼0 2 ab 4 From here one can obtain such an equation to determine the NMV boundary: 

2ða2  b2 Þ 2 2Re k2 ðch ðuÞ cos2 ðmÞ þ sh2 ðuÞ sin2 ðmÞÞ þ lnð 2 Þ þ 2  ln½jCe0 ð0; ða2  b2 Þj þ 2 w kw 4 2 2 k2 2 p k k þ ln½jCe0 ðu; ða  b2 Þj  ln½jce0 ð ; ða2  b2 Þj þ ln½jce0 ðm; ða2  b2 Þj ¼ 0 2 4 4 4

And such an expression for estimating of the elliptic laser power: ZZZ P¼c

dzd m duða2  b2 Þ NMV

sh2 ðuÞ þ sin2 ðmÞ k2 k2
Ce0 ðu; ða2  b2 ÞÞ
ce0 ðm; ða2  b2 ÞÞ
2 w 4 4   2ða2  b2 Þ 2 2 ðsh ðuÞ þ cos ðmÞÞ
exp w2

ð7Þ where c ¼

4E02 d N0 e

pffiffiffiffiffiffiffiffiffiffiffi dðRdÞ

. ce0 ðp2;k42 ða2 b2 ÞÞ It has to note the cumbersome calculations of Mathieu functions. The methods of Mathieu functions calculating are still the subject of studies (see, for example, [11, 12]). We used both interpolation of the tabulated values of Mathieu function decomposition coefficients from [9, 10, 13] and algorithm of these coefficients finding, based on normalization of Mathieu functions and the recurrent ratios for the decomposition coefficients. The calculations results were provided as an example with parameters similar to the above described rectangular laser: d = 2,2 m, R = 10 m, b = 2,5 mm, a – 2,75 mm 15 mm, l = 1,2 m, the tube is located in the center of the resonator. The surface limiting NMV at these laser parameters is also an ellipse with very small eccentricity, with the dependencies similar to the rectangular case. The eccentricity at the fixed z grows with the ratio a/b growth and the ellipse dimensions grow with growth z at the fixed ratio value of a/b. We also compared the laser powers with an elliptical and circular cross section of the same square (and at the same tube length or the same volume). We took the circular section radius of r0 = (ab)1/2 at the same a and b and the same d, R and l (with the tube in the middle of the resonator also). The results of the comparison are shown in Fig. 39.2. k
Ce

k2 2 2 0 ð0; 4 ða b ÞÞ


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28,5

1 28,0

P/α (M2)

Fig. 39.2 A plot of laser reduced power P/a (a = eE20dN0/k) with circle (1) and elliptical (2) cross section vs ratio of the ellipse half-axes a/b

27,5

2 27,0

26,5

26,0 1

2

3

4

5

6

a/b

39.4

Discussion

The results obtained by us about the power of the rectangular laser are well consistent both with our work on the calculation of the laser amplification and with the experiments. It has been obtained in [7] that the average cross-sectional gain of the rectangular laser is, firstly, independent on the ratio of the rectangle sides, and secondly, about 6% lesser than the gain of the cylindrical laser. It is visible from Fig. 39.1 that the power increased approximately in 6.5% at all at the ratio a/b increasing in 30 times, and since a/b  6 value it is possible to consider that power does not change. Figure 39.1 also shows that the power of the rectangular laser is about 4–6% lesser than the power of the cylindrical laser (with the cross section of the same square). The last fact corresponds to the experimental data in [14]. Therefore, we can assume that the method of the laser power calculating proposed by us is quite correct. Similarly, the results of the laser power with the elliptical cross section calculations are consistent with our laser gain calculations. The elliptical laser gain average in cross section for the half-axis ratio (a/b) was obtained in [7] and the elliptical laser gain is slightly lesser than the cylindrical one gain and their difference grows with the ratio a/b growth (recall that in [7] the approximate solution found by our method was used). Despite the fact that the elliptical beams (Mathieu beams or Mathieu-Gauss beams), being one of four (along with ordinary Gaussian, Bessel-Gaussian, and Parabolic Gauss beams) fundamental families of the nondiffracting solutions of wave equation, recently [15–17] studied actively after their experimental detection and theoretical consideration [18, 19], and they are considered as promising for the laser processing of materials [20], we could not find the description of the experiments with the detection of the gas-discharge lasers power with the elliptical cross-section of the active element.

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Conclusions

We have been proposed a method of estimating the He–Ne laser radiation power with tube cross section arbitrary geometry. The algorithm proposed in [7] with the cross section constant along the laser length for the homogeneous Helmholtz equation solving can be used. We calculated the circular, rectangular and elliptical lasers. The calculations results are well consistent with the earlier laser gain calculations and experiments. In the future, it is planned to consider other cross sections in search of the optimal power. We hope it will be useful at the problem considered in [21–25] solving.

References 1. V.A. Kozhevnikov, V.E. Privalov, V.G. Shemanin: Effective mode volume evolution in the he-ne laser. in Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), Editor by E. Velichko (IEEE, Saint Petersburg, 2019), pp. 272–274 2. T. Tako, Self-absorption of spectral line. J. Phys. Soc. Jpn. 15(10), 2016–2032 (1961) 3. W.R. Bennett Jr., Excitation and inversion mechanisms in gas lasers. Ann. N. Y. Acad. Sci. 122(2), 579–595 (1965) 4. V.E. Privalov, S.A. Fridrihov, Dependence of the He-Ne laser radiation power on the geometry of the discharge gap cross section. Tech. Phys. 38(12), 2080–2084 (1968). (in Russian) 5. G. Herziger, W. Holzapfel, W. Seelig, Verstärkung einer He-Ne-Gasentladung für die Laserwellenlänge k = 6328 AE. Zeitschrift für Physik 189, 385–400 (1966) 6. D.C. Sinclair, Choice of mirror curvatures for gas laser cavities. Appl. Opt. 3(9), 1067–1072 (1964) 7. V.A. Kozhevnikov, V.E. Privalov, The geometrical effect of an active element cross-section on the laser gain. St. Petersburg Polytech. State Univ. J. Phys. Math. 11(2), 77–87 (2018) 8. N.J. Higham, Accuracy and Stability of Numerical Algorithms, 2nd edn. (Society for Industrial and Applied Mathematics, Philadelphia, 2002) 9. Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables. in 10th edn. Edited by M. Abramowitz, I.A. Stegun (National Bureau of Standards 1972) 10. N.W. McLachlan, Theory and Application of Mathieu Functions (Clarendon Press, Oxford, 1951) 11. D. Frenkel, R. Portugal, Algebraic methods to compute Mathieu functions. J. Phys. A: Math. Gen. 34, 3541–3551 (2001) 12. M.M. Bibby, A.F. Peterson, Accurate Computation of Mathieu Functions. Synthesis Lectures on Computational Electromagnetics #32”. Morgan & Claypool Publishers (2014) 13. Tables relating to Mathieu functions. Characteristic values, coefficients, and joining factors. National Bureau of Standards (Columbia Univ. Pres., N.Y. 1951) 14. V.E. Privalov, V.A. Hodovoi, An experimental study of a He-Ne laser with a discharge gap of rectangular cross section. Opt. Spectrosc. 37, 797–799 (1974). (in Russian) 15. Z. Ren, H. Hu, B. Peng, Generation of Mathieu beams using the method of ‘combined axicon and amplitude modulation’. Opt. Commun. 426, 226–230 (2018) 16. Z. Ren, J. He, Y. Shi, Generation of Mathieu beams using angular pupil modulation. Chin. Phys. B 27(12), 124201 (2018)

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17. I. Julián-Macías, C. Rickenstorff-Parrao, O.J. Cabrera-Rosas, E. Espíndola-Ramos, S.A. Juárez-Reyes, P. Ortega-Vidals, G. Silva-Ortigoza, C.T. Sosa-Sánchez, Wavefronts and caustics associated with Mathieu beams. J. Opt. Soc. Am. A 35(2), 267–274 (2018) 18. J.C. Gutiérrez-Vega, M.D. Iturbe-Castillo, G.A. Ramírez, E. Tepichín, R.M. Rodrígues-Dagnino, S. Cháves-Cerda, G.H.C. New, Experimental demonstration of optical Mathieu beams. Opt. Commun. 195, 35–40 (2001) 19. J.C. Gutiérrez-Vega, M.A. Bandres, Helmholtz-Gauss waves. J. Opt. Soc. Am. A 22(2), 289– 298 (2005) 20. S. Orlov, V. Vosylius, P. Gotovski, A. Grabusovas, J. Baltrukonis, T. Gertus, Vector beams with parabolic and elliptic cross-sections for laser material processing applications. J. Laser Micro/Nanoeng. 13(3), 280–286 (2018) 21. Savchenko, E.A., Velichko, E.N., Aksenov E.T., Nepomnyashchaya, E.K.: Combined method for laser selection, positioning and analysis of micron and submicron cells and particles. in Proceedings - International Conference Laser Optics ICLO 2018, vol. 539 (2018) 22. V.A. Volkov, D.A. Gordeev, S.I. Ivanov, A.P. Lavrov, I.I. Saenko, Photonic beamformer model based on analog fiber-optic links’ components. J. Phys: Conf. Ser. 737(1), 012002 (2017) 23. E.K. Nepomnyashchaya, E.N. Velichko, I.V. Pleshakov, E.T. Aksenov, E.A. Savchenko, Investigation of ferrofluid nanostructure by laser light scattering: medical applications. J. Phys: Conf. Ser. 841, 012020 (2017) 24. S.I. Ivanov, A.P. Lavrov, S.A. Molodyakov, I.I. Saenko, Acousto-optical specrometers’ frequency performance stability. Proc. of SPIE 5381, 253–257 (2016) 25. M.V. Putintseva, E.T. Aksenov, C.C. Korikov, E.N. Velichko, Non-invasive research of biological objects by the method of laser polarimetry. J. Phys: Conf. Ser. 1124, 031021 (2018)

Chapter 40

Laser System for the Average Volume-Surface Diameter of Aerosol Particles Measuring Vadim E. Privalov , Vladimir V. Dyachenko , Alina A. Kovalyova , and Valery G. Shemanin Abstract The laser extinction signals in the cement aerosol at the 405, 532 and 650 nm wavelengths have been measured using the differential extinction lidar. The procedure for the average volume-surface diameter of aerosol particles calculating has been developed and implemented. Correlation dependences of the signals measured by this lidar on particle size distribution function for the studied aerosol were obtained. These dependencies can become the basis of a new method of the laser probing inverse problem solution for the aerosol in the atmospheric boundary layer. The developed differential extinction lidar makes it possible to measure in real time the laser radiation extinction signals and to calculate the reliable values of the particles average volume-surface diameter from the measured data according to the proposed procedure. The cement aerosol particles average volume-surface diameter relative error calculated from experimentally measured extinction signals is equal to 4.6% and this average diameter is equal to 0.61 ± 0.028 lm. Our results are consistent with the previous experimental results.







Keywords Laser radiation Wavelength Lidar system. differential extinction Cement aerosol particles Average volume-surface diameter







V. E. Privalov Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia e-mail: [email protected] V. V. Dyachenko
A. A. Kovalyova
V. G. Shemanin (&) Novorossiysk Polytechnic Institute of Kuban State Technological University, 353900 Novorossiysk, Russia e-mail: [email protected] V. V. Dyachenko e-mail: [email protected] A. A. Kovalyova e-mail: [email protected] V. G. Shemanin Branch of the V.G. Shukhov Belgorod State Technological University in Novorossiysk, 353919 Novorossiysk, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_40

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Introduction

For the solving of the laser probing inverse problem [1–3], the numerical solution of the integral Volterra equation of the second type is used and the core of which is the desired function of the aerosol particles size distribution. However, in the real conditions, it is often found that the aerosol in the atmospheric boundary layer is not uniform [4] and such an inverse problem solving is complicated by the effects of multiple scattering [3, 4] which magnitude is a priori undefined and can be estimated only from lidar signals. Therefore, the present work goal is the studies of the relationships of the aerosol flow differential extinction signals or optical density measured by the experimental multi-wave lidar system on the particle size distribution of the cement aerosol in the air flow. In continuation of [5] the experimental studies of this lidar optical density signals at the three wavelengths of the laser radiation in the visible spectrum from 405 to 650 nm will allow to create a new method of the laser probing inverse problem solving for the determination of the aerosol particles size distribution function in the atmospheric boundary layer.

40.2

Laser Differential Extinction Method

The laser differential extinction system which allows to measure extinction signals at three wavelengths of laser radiation has been developed to determine the aerosol flow parameters [6]. These extinction signals are the photodetector electrical signals directly proportional to the extincted radiation intensity. The differential extinction method itself is based on the inverse problem solution [1, 7], which can be represented as the equation: sk ¼

p
Cn
l
4

Z

1

x2
Qðx; k; mÞ
f ð xÞdx;

ð1Þ

0

where sk is the optical density; Cn is the particles counting concentration; k— the probing radiation wavelength; Q is the extinction efficiency factor for the single particles; l is optical ranging distance; x is the particles diameter; m is the particle material complex refractive index. And the average extinction efficiency factor is calculated by the formula: Qa ðx; k; mÞ ¼

R1

0

x2
Qðx; k; mÞ
f ð xÞdx R1 2 0 x
f ð xÞdx

ð2Þ

This method physical model is based on the monochromatic radiation with polydisperse medium interaction according to the Mie theory [8] and the extinction

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efficiency averaged factor invariance preservation relatively to the type of the particle size distribution function [7, 8]. The aerosol particles average volume-surface diameter d32 is determined by the studied aerosol optical density measuring at the several wavelengths or the averaged extinction efficiency factors calculating for the same wavelengths. The measured optical densities ratio at the two wavelengths is equal to the calculated extinction efficiency averaged factors ratio and expresses the function of the average particle size [5, 7]: ski Qa ðx; ki ; mÞ  ¼ Fij ðd32 Þ ¼  skj Qa x; kj ; m

ð3Þ

The developed laser system optical layout is described in detail in [5, 6]. Three semiconductor lasers with the laser radiation wavelengths of 405, 532 and 650 nm were selected for the studies. In order to create a pulsed aerosol flow in a closed gas pipe, it was used the injection of the aerosol material with mass of 10 g into the air flow by means of the pulsed particle generator [5] at the given time, which was zero time in all measurements.

40.3

Experimental Setup, Procedure and Discussion

In the lidar of differential extinction after passing laser radiation through the aerosol flow and its extinction occurs according to the Bougér- Lambert-Beer law [9]: I ¼ I0 expðsk Þ;

ð4Þ

where I is the intensity of the radiation transmitted through the aerosol flow; I0 is the intensity of the laser radiation entering to the aerosol flow; sk is the optical density. The sk optical density in expression (1) is defined from the expression (4). The average extinction efficiency factor Qa(d, k, m) can be calculated according to formula (2) knowing the time dependence of the aerosol particles concentration in the flow and the particle size distribution function of the studied aerosol at each time movement after the injection. The extinction signals measurement for three wavelengths of Uex ðki ; tÞ and reference signals Ur ðki ; tÞ were determined by the optical density of sk in experiments by means of the laser system. The Uex ðki ; tÞ extinction signal is calculated by formula Uex ðki ; tÞ ¼ Aex ðki ; tÞ
I ðki ; tÞ;

ð5Þ

where Aex ðki ; tÞ is the spectral function of the i-th extinction channel; I ðki ; tÞ is the intensity of the radiation transmitted through the aerosol flow at wavelength ki .

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The measured signals were used to normalize due to the values of the reference channels signals of Ur ðki ; tÞ at the same wavelengths. The expression (5) for the reference channel takes the form Ur ðki ; tÞ ¼ Ar ðki ; tÞ
I0 ðki Þ;

ð6Þ

where Ar ðki ; tÞ is the spectral function of the i-th reference channel; I0 ðki Þ is the intensity of radiation at the input of the aerosol flow. Taking into account (4) the expression (5) for the extinction signal Uex ðki ; tÞ takes the form UOCp ðki ; tÞ ¼ AOCp ðki ; tÞ
I0 ðki Þ
eski

ð7Þ

The signals normalization is performed by the determination of the ratio (7) to (6): Uex ðki ; tÞ Aex ðki ; tÞ
I0 ðki Þ
eski ¼ U r ð ki ; t Þ Ar ðki ; tÞ
I0 ðki Þ

ð8Þ

After the transformations, the expression (8) becomes Uex ðki ; tÞ ¼ U r ð ki ; t Þ

  Aex ðki ; tÞ
eski A r ð ki ; t Þ

ð9Þ

The values of Uex ðki ; tÞ и Ur ðki ; tÞ were measured in the experiments. The aerosol concentration is zero at the initial measurement time, so the extinction signals at the initial time, i.e. without aerosol material, are equal to (5) and (6) with index 0. Then the value of ski for the experimental dependences is defined by the formula  ski ¼ ln

Uex ðki ; tÞ Ur0 ðki ; tÞ
Uex0 ðki ; tÞ Ur ðki ; tÞ

 ð10Þ

These dependencies are most informative for individual parts of graphs corresponding to certain particle sizes (see Fig. 40.1). The effect at which the value ski at the different wavelengths decreases with the different speed is observed according to the plot in Fig. 40.1. The ski value at the 650 nm laser radiation wavelength decreases quicker, than the ski values at the 532 and 405 nm wavelengths. The average volume- surface diameter of d32 determination is based on it. The averaged efficiency factors ratio Qa(ki)/Qa(kj) at the different laser radiation wavelengths is some function F(d32) [7]. It has been shown in [5] how the particles size distribution function changes in time from the time after the aerosol material injection. The d32 value and Qa (d32, k, m) value were calculated for each time moment after the injection and for everyone the particles size distribution function.

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Fig. 40.1 A plot of the optical density skI, corresponding to fine aerosol at 405, 532 and 650 nm laser radiation

Fig. 40.2 A plot of Qa(ki)/Qa(kj) value vs d32 diameter at three wavelengths: Q532/Q405, Q650/ Q532, and Q650/Q405

We receive three Qa(d32, ki, m) values for each d32 value because Qa(d32, ki, m) value depends on the laser radiation wavelength. The dependence of F(d32) was constructed according to formula (3) based on the obtained average extinction efficiency factors. The plot of the Qa(ki)/Qa(kj) value dependence on d32 diameters is provided in Fig. 40.2. It is visible in plot in Fig. 40.2 that this dependence increases exponentially in the diameter range from 0.1 lm to 1 lm. The d32 value is 0.43 lm for the particles size distribution function at the filter output [11] therefore it is possible to apply the differential extinction method to the aerosol fine phase monitoring. The Qa(ki)/Qa(kj) relation was then compared to the experimentally measured ski/skj ratio. The plots of the calculated The Qa(d32, 650, m)/Qa(d32, 405, m) ratio are shown in Fig. 40.3 and the measured s 650/s 405 ratio as an example.

356 Table 40.1 The average volume-surface diameter error values

V. E. Privalov et al. Parameter

sk650 =sk405

sk1064 =sk405

sk1064 =sk650

d 32, lm Dd32, lm e, %

0.44

0.60

0.80

0.61

0.027 6.2

0.021 3.6

0.036 4.5

0.28 11.2

Fig. 40.3 A plot of the ratios Qa(d32, 650, m)/Qa(d32, 405, m) and s after the pulse aerosol material injection

650/s 405

Average value

time dependence

It is easy to see in the graphs in Fig. 40.3 that the calculated Qa(ki)/Qa(kj) ratio and the experimental s ki/s kj ratio coincide within the experimental error. Thus, we receive the relation of the Qa(ki)/Qa(kj) average extinction efficiency factors and the d32 value by the measured relation of s ki/s kj optical densities according to the plot in Fig. 40.3. Let’s calculate the relative errors for the particles average volume-surface diameter. The average volume-surface diameter values and its measured values relative error at each laser radiation wavelength for the different measurement series were calculated over the whole range of the measured data. The obtained errors calculation results of the average volume-surface diameters are presented in Table 40.1. It can be concluded that the instrumental errors, the applied differential extinction method errors and the material used in the experiment mass determining errors have no significant effect on the measured extinction signals. The relative errors of the average volume-surface diameter values and mass concentration of the cement aerosol particles calculated from experimentally measured extinction signals are permissible and are consistent with the data [5, 9, 10].

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Conclusion

Thus, the developed experimental differential extinction lidar makes it possible to measure the laser radiation extinction signals in real time and to calculate the reliable values of the particles average volume-surface diameter from the measured extinction signals according to the proposed procedure. Correlation relationships of the signals measured experimentally by the laboratory lidar on the probed aerosol particle size distribution function were obtained and their comparison confirms the results of [5, 7]. The laser application is expanding [12–16] and there is reason to believe that this paper results will be of interest for a wide range of specialists. Acknowledgements This work was partially supported by the Russian Fund of Basic Research grants, project No. 19-42-230004 and project No. 19-45-230009

References 1. V.E. Zuev, I.E. Naatz, Reverse tasks of atmospheric optics. L. Hydrometeo-isdat (1990) 2. Q. Yan, H. Di, J. Zhao, X. Wen, Y. Wang, Y. Song, D. Hua, Improved algorithm of aerosol particle size distribution based on remote sensing data. Appl. Opt. 58, 8075–8082 (2019) 3. R. Mejeris, Laser remote sensing. M. Mir (1987) 4. V.E. Zuev, B.V. Kaul, I.V. Samohvalov, K.I. Kirkov, V.I. Tsanev, Laser Sensing of Industrial Aerosols (Nauka, Novosibirsk, 1986) 5. S.V. Polovchenko, V.E. Privalov, P.V. Chartiy, V.G. Shemanin, Restoration of particle size distribution function on the basis of data of multi-wave laser sensing. J. Opt. Tech. 83, 43–49 (2016) 6. V.E. Privalov, V.V. Dyachenko, V.G. Shemanin, Laser ranging of the atmospheric aerosol and determination of its disperse composition. IEEE Xplore Digital Library (2019). https:// doi.org/10.1109/EExPolytech.2019.8906882 7. V.A. Arkhipov, I.R. Akhmadeev, S.S. Bondarchuk, B.I. Vorozhtzov, A.A. Pavlenko, M.G. Potapov, Modified spectral transparence method for the aerosols dispersion measurement. Atm. Ocean Opt. 20, 48–52 (2007) 8. K. Boren, D. Hafmen, Light absorption and scattering by small particles. M. Mir (1986) 9. V.A. Arkhipov, Laser Methods of Heterogeneous Flows Diagnostics (Tomsk Un. Publ, Tomsk, 1987) 10. V.E. Privalov, A.V. Rybalko, P.V. Charty, V.G. Shemanin, Effect of noise and vibration on the performance of a particle concentration laser meter and optimization of its parameters. Tech. Phys. 52(3), 352–355 (2007) 11. E.I. Vedenin, S.V. Polovchenko, P.V. Chartii, V.G. Shemanin, Change of particle size distribution function under different modes of dust cleaning equipment operation. Technosphere Safety 1(58), 41–47 (2016) 12. E.A. Savchenko, E.N. Velichko, E.T. Aksenov, E.K Nepomnyashchaya, Combined method for laser selection, positioning and analysis of micron and submicron cells and particles. in Proceedings of the International Conference on Laser Optics Iclo August 2018 (2018), p. 539 13. E.K. Nepomnyashchaya, E.N. Velichko, I.V. Pleshakov, E.T. Aksenov, E.A. Savchenko, Investigation of ferrofluid nanostructure by laser light scattering: medical applications. J. Phys. Conf. Ser. 8410, 012020 (2017)

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14. M.V. Putintseva, E.T. Aksenov, C.C. Korikov, E.N. Velichko, Non-invasive research of biological objects by the method of laser polarimetry. J Phys: Conf Series 1124, 031021 (2018). https://doi.org/10.1088/1742-6596/1124/3/03102017 15. S.I. Ivanov, A.P. Lavrov, S.A. Molodyakov, I.I. Saenko, Acousto-optical specrometers’ frequency performance stability. Proc. SPIE 5381, 253–257 (2016) 16. V.A. Volkov, D.A. Gordeev, S.I. Ivanov, A.P. Lavrov, I.I. Saenko, Photonic beamformer model based on analog fiber-optic links’ components. J Phys: Conf Series 737(1), 012002 (2017)

Chapter 41

The Compensation of Radiation-Induced Losses in the Fiber Optic Communication Line in Its Operation Mode Diana S. Dmitrieva, Valeria M. Pilipova, and Khuan Dominges

Abstract Nowadays high-speed large data exchange is impossible without using the fiber optic communication lines. The scientific and technical progress led to the appearance of large number of factors, which have negative influences on FOCLs. One more of these factors is c-radiation. It changes fiber properties and leads to the decrease of line efficiency. As the result of properties and structure changes in the optical fiber, color centers appear, i.e. microdefects with a natural frequency of light absorption, and also the radiation-induced losses arise, which are for several hundred times higher than the initial losses in the communication line. It is impossible to transmit the information in such conditions. The optical fiber recovery after c-radiation influence takes a lot of time, but this process can be significantly accelerated by the compensation of the radiation-induced losses. The article substantiates the necessity to develop a method of compensation of losses in fiber optic communication lines after irradiation, and also describes the method of increasing the rate of relaxation processes based on compensation of radiation-induced losses. The received experimental results are presented.




Keywords Optical fiber Gamma irradiation radiation Power Operation time








Radiation-induced losses
Laser

D. S. Dmitrieva (&)
V. M. Pilipova Bonch-Bruevich St. Petersburg State University of Telecommunications, Saint Petersburg 193232, Russia e-mail: [email protected] K. Dominges Salesiana Politecnica University, Quito 170215, Ecuador © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_41

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Introduction

Nowadays it is impossible to image the world without high-speed large data exchange [1–10]. Fiber optic communication lines (FOCL) and other systems based on it are used in different fields of science, technology and industry, and also, they are used for communication between people [3–7, 11–14]. FOCL are more stable to different influences than other communication lines [4, 5, 11, 12, 15–20]. Different methods of information processing [4, 16–26] used in them allow to solve various tasks quickly. The development of nuclear energy and industrial production with using radioactive elements led to the appearance of tone of the most unpleasant factors for FOCL—c-radiation [5, 26–31]. It can change the physical properties of the fiber. Under its influence fiber is getting dark, the power losses of the optical signal increase, which can be 400 dB/km and higher, if the influence will be prolonged [27–29]. The standard losses in the optical fiber at an operating wavelength k = 1550 nm and in the operating temperature range from 213 to 338 K are from 0.26 to 0.38 dB/km. Such sharp change in losses value leads to the suspension of the FOCL operation. Relaxation process takes a lot of time. In the case of high dose or prolong time of c-radiation the optical fiber can not to recover to the initial state. As the reason of long distance of trunk FOCL, it is impossible to exclude the possibility of the c-radiation influence (technological disasters and waste emissions are more and more). So, the task of development the method of compensation losses is the actual for info communication systems.

41.2

The Research of c-Radiation Influence on Losses in the Optical Fiber, Experiment Results and Discussion

The c-radiation influence leads to the appearance of two radiation factors in the optical fiber: displacement and ionization [27–31]. The displacement is due to the atoms shift from a stationary state in the crystal lattice to a more unstable, which leads to the appearance of microdefects that destruct the fiber crystal lattice. In a case of high radiation dose and prolong influence time the stable connections appear, which change the fiber structure and lead to its destruction. The ionization effect is based on the formation of electron-hole pairs. Electrons knocking the oxygen out of compounds take place in the crystal lattice. The increase in the amount of ionization atoms leads to change of the fiber refractive index. These defects can cause irreversible changes in the optical fiber structure and make the line unsuitable for further operation. As a result of structure change in the optical fiber, color centers appear. They are the point defects with natural frequency of light absorption. They are also the cause of occurrence of radiation-induced losses.

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The conducted earlier researches allow to establish, that the velocity of the relaxation processes depends on several factors. There are the most significant of them: the optical fiber’s temperature, the optical signal’s power and type of laser radiation (pulsed and continuous). It allows to develop the method of compensation of radiation-induced losses, which can be used to information transmit with full data exchange via FOCL. Proposed methodology is based on the input the additional laser radiation with other wavelength in the optical fiber together with the main radiation. The optical multiplexer is used for combining two laser radiation into a group channel and for dividing these radiations into two channels with different wavelengths k at the end. Using the multiplexer allows to use the proposed method without the problem of applying additional laser radiation on the main signal. After the multiplexer there are two photodetectors at the output: one of them is for receiving information, other of them is for controlling irradiation power. As an experimental example was used the single-mode fiber with a Si02 – GeO2 core (alloying 1.5%) with an irradiation dose of 100G. The additional laser radiation has a wavelength k = 1310 nm. The results of study of dependence in radiation-induced losses as with different powers of additional laser radiation are presented in Fig. 41.1. In the first experiment were used the low powers of pulse laser radiation: 0.1, 4.0 и 40 mW. The analysis of the results shows, that the compensation of radiation-induced losses accelerates, when the power of additional radiation increases. The compensation of these losses is slow. The optical fiber properties recover to the initial state after 1000 s. As the second experiment we decided to increase the power of additional laser radiation for following values: 0, 200 and 400 mW. Other parameters are similar to those in previous experiment. Its results are presented in Fig. 41.2.

Fig. 41.1 Time dependence of the change in loss as at a wavelength of k = 1550 nm for a single-mode fiber with Si0 2 – GeO 2 core (alloyage 1.5%) at T = 294.2 K. Charts 1, 2, and 3 correspond to different laser radiation powers of 0.1, 4.0, 40 mW respectively

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Fig. 41.2 Time dependence of the change in loss as at a wavelength of k = 1550 nm for a single-mode fiber with a Si0 2 – GeO 2 core (alloyage 1.5%) at T = 294.2 K. Charts 1, 2, and 3 correspond to different laser radiation powers of 0, 200, 400 mW respectively. Chart 4 is the constant level of the laser radiation with the power of 100 mW

Obtained results (Fig. 41.2) confirmed the efficiency of proposed method. The radiation-induced losses compensate of 200 and 400 mW for 100 and 10 s respectively. Without using obtained method, the optical fiber recovers to the initial term after 108 s. However, it should be noted, that increase of additional power radiation above 400 mW to accelerate the compensation of radiation-induced losses can lead to distortion of main signal, which transmits information.

41.3

Conclusion

The conducted experiments confirm that the proposed method is effective for compensation of radiation-induced losses. If we use the obtained dependence of the losses at laser radiation power and pulse duration, then we can establish the fact of c-radiation influence. It solves another problem in the operation of fiber optic communication line.

References 1. N.M. Grebenikova, A.V. Moroz, M.S. Bylina, V.V. Davydov, M.S. Kuzmin, Remote control of the quality and safety of the production of liquid products with using fiber-optic communication lines of the Internet. IOP Conf. Ser. Mat. Sci. Eng. 497, 012109 (2019) 2. M. Makolkina, V.D. Pham, R. Kirichek, A. Gogol, A. Koucheryavy, Interaction of AR and IoT applications on the basis of hierarchical cloud services, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11118 LNCS (2018), pp. 547–559

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Part III

Information Technologies and Signal Processing

Chapter 42

Object Classification Based on Channel State Information Using Machine Learning Maksim A. Lopatin, Stanislav A. Fyodorov, Sergey V. Zavjalov , and Dong Ge Abstract Wireless technologies have become more and more popular over the years. Today, Wi-Fi devices are used everywhere. This means that applied research on object recognition using Wi-Fi is relevant. This paper describes the classification of objects using channel state information (CSI). We use different machine learning methods, such as a decision tree, a support vector machine, k-nearest neighbors or a feed-forwarded neural net and others, which can be applied to the classification of physical objects. We explore the possibility of such recognition between Wi-Fi devices for various objects and study the effectiveness of various machine learning methods for recognizing objects using CSI. We also discuss the instability of CSI data due to drift over time which produces problems when using machine learning and consider the effectiveness of recognizing metal objects of various shapes at a short distance. In this paper all experiments are performed using 2 routers of the same model at a short distance between them. Keywords Wi-Fi

42.1


CSI
Neural network
Classification

Introduction

In today’s world, many people have wireless devices at home that use Wi-Fi technology to transmit data. Wi-Fi devices can not only exchange data, but also transmit information that describes the signal parameters. In this paper we use the channel state information (CSI) for classification which describes the signal propagation path.

M. A. Lopatin
S. A. Fyodorov
S. V. Zavjalov (&) Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya 29, St. Petersburg 195251, Russia e-mail: [email protected] D. Ge Tsinghua University, Haidian District, Beijing, People’s Republic of China © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_42

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There are two main tasks in which channel state information can be used: classification and indoor positioning [4]. The paper focuses on the classification of physical objects. The results of research in the field of object classification using Wi-Fi can be used to implement Wi-Fi devices that can be used for baggage screening, as an additional sensor that tracks the availability of free space, and so on. The use of CSI for recognition and localization has been discussed in various articles [1–7]. However, we are interested in recognizing small objects. And we use different machine learning methods for recognition. Machine learning methods find different application in various area of science and industry [8–13] and better results of performance in comparison with other methods. But choice of optimal machine learning method for specific conditions is very complex task. Our goal is to select the optimal machine learning method for object classification, taking into account the drift CSI over time.

42.2

CSI Structure

The CSI is a matrix that consists of complex values of the signal subcarriers for each pair of antennas (Fig. 42.1). A pair of antennas consists of a transmitting and receiving antenna. We use two routers of the same model (TL-WR842ND), each of which has two antennas. The bandwidth is set to 20 MHz, so CSI matrix are sent for 56 subcarriers. Consequently, for the equipment used the CSI matrix consists of 2  2  56 complex subcarriers.  Hi ¼

h11 h21

h12 h22

 ð1Þ

In the CSI matrix (1), H is the CSI matrix, i is the number of CSI measurements, h11…h22 is amplitudes for four pairs of antennas. Each CSI signal subcarrier is a complex number that contains the phase and amplitude. The amplitude value is expressed in milliwatts. For the classification problem, we use only the values of the amplitude of the subcarriers of signal, because using them results in higher accuracy in classification and amplitude of CSI does not need preparation, unlike the phase [1].

Fig. 42.1 Wi Fi signal transmission scheme

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Fig. 42.2 Amplitudes for four pairs of antennas without preparation. One line is a single data packet for one pair of antennas

Fig. 42.3 Change in the amplitude of CSI for h22 over time

However, the amplitudes can be prepared, for example, these values can be smoothed although this is not necessary. The amplitudes on the graph (Fig. 42.2) for four pairs of antennas were measured in a few seconds. Visually, they are clearly distinguishable. Sometimes outliers occur in the channel that adversely affect the classification accuracy.

42.3

Instability of CSI

Unfortunately, the amplitude value of the subcarriers may change over time [2]. It is a complex problem manifested in two ways: each subsequent package taken received in the same time period differs from the previous one (Fig. 42.3) and over time, the values of amplitudes generally change their curves. This fact makes classification very difficult.

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In particular, if the distance between routers increases to 1 m or more, the accuracy of object recognition in the Wi-Fi channel will not significantly decrease. However, if old training samples are used to classify new CSI data taken after a significant period of time, the accuracy could greatly reduce and the greater the distance between routers, the more negative the effect of CSI drift over time is.

42.4

Fixation Parameters

We selected the following parameters for the experiment: the distance between the routers is 50 cm, two objects for classification: a 0.5 L water bottle and air (no object in the Wi-Fi channel). The classification will be based on training data that was collected at different angles of the router antennas. There are about 50,000 data packets, which is enough for training. Test data will be recorded later, at least on the next day when the routers will be re-located at the place where the training data was taken. The different tilt of the antennas during training is necessary because of the high sensitivity of the CSI to the position of the antennas. Routers are set manually and the position of the antennas is difficult to set accurately. To compensate for this, we need to change the tilt of the antennas during training. This will make the results of classifying test data more stable. But on the other hand, such data is more difficult to train machine learning algorithms.

Fig. 42.4 A connected data frame consisting of amplitude values. This data is sent to the models for training

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One of the classification methods used is single-layer feedforward neural network (FFNN). We will try to find the optimal parameters for it, such as the activation function or the number of neurons in the layer. Convolutional neural networks (CNN) are also used for CSI data classification and localization [3]. However, CNN not discussed in this paper. Each CSI packet, as mentioned earlier, is a 2  2  56 matrix. As training data, CSI amplitudes will be used, which will be jointly transmitted to the model (Fig. 42.4).

42.5

Selection of FFNN Parameters

A series of accuracy measurements was performed to select the parameters of a single-layer FFNN. This neural network has 2 neurons at the output for two objects. The activation function for the output layer is softmax. The classification accuracy results for different parameter sets were unstable, so the number of layers and the activation function were selected as the maximum values of the averages. Each accuracy value in Table 42.1 is the average value after several training attempts. The accuracy was evaluated on the samples that were taken after the training samples. The number of epochs is 50. As you can see from the table (Table 42.1), a single-layer neural network copes with classification satisfactorily and on average classifies correctly in 76% of cases. As mentioned earlier, only two objects are classified, and such a low accuracy result is associated with the specifics of obtaining training samples.

Table 42.1 Accuracy results of FFNN parameter selection Activation function

10

60

110

160

210

260

310

360

Average

relu elu selu sigmoid tanh hard_sigmoid softplus softsign average

63,2 68,2 67,0 76,4 57,6 74,4 56,6 71,6 66,9

88,4 88,6 80,2 74,2 68,0 78,0 69,0 74,8 77,7

79,4 77,4 72,6 78,0 81,8 76,2 76,2 75,0 77,1

71,6 87,6 69,8 78,8 85,8 82,6 71,6 79,8 78,5

80,6 68,6 76,6 83,4 74,4 75,4 71,6 79,8 76,3

72,8 73,6 73,6 62,6 79,0 78,4 61,4 67,6 71,1

66,6 69,4 82,4 85,0 89,6 74,4 68,8 82,8 77,4

82,2 72,8 84,0 76,8 79,2 85,2 82,2 85,0 80,9

75,6 75,8 75,8 76,9 76,9 78,1 69,7 77,1 75,7

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Table 42.2 Classification results using various ML methods for air and water

42.6

ML method

Accuracy

K-nearest neighbors Logistic regression Gaussian naive bayers FFNN Support Vector Machine Random forest Decision tree Stochastic gradient descent Linear SVC Perceptron

99 96 89 85 85 85 80 73 72 57

Classification Results

Machine learning methods other than FFNN have been tested (Table 42.2). Some of them showed better results. Most likely this is due to the fact that we were not able to select the parameters of the neural network well enough or it needed more hidden layers. FFNN has higher potential compared to other machine learning methods and can be more flexibly configured. The principle of operation of a neural network allows it to successfully recognize more complex data (higher distance between routers, more objects, smaller size of objects). But a neural network usually requires subtle manual tuning, which is difficult to do. To create and train FFNN we use Keras with Tensorflow backend. For all other machine learning algorithms we use the Scikit-learn library for python with default settings of methods.

42.7

Classification of Metal Objects

For the classification, 4 metal objects were selected: a thermos, grater, plate and pan. Samples for training and testing were taken at the same time with a constant antenna angle. The classification accuracy was high (Table 42.3). There is a significant difference in accuracy between methods such as SVM, logistic regression and Random forest, FFNN. This means that in fairly simple cases (a small distance between routers), high recognition accuracy can be achieved using relatively simple machine learning methods. On the one hand, we can conclude that metal objects are easier to classify, because the signal is distorted more when passing through these objects than through water. But on the other hand, training and testing data for the classification of metal objects were obtained at a constant position of the antennas and this has a

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Table 42.3 Classification results using various ML methods for 4 metal objects

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ML method

Accuracy

Logistic regression Support Vector Machine Stochastic gradient descent K-nearest neighbors Linear SVC Decision tree Random forest FFNN Perceptron Gaussian naive bayers

100 99 97 96 92 89 87 86 84 79

positive effect on accuracy results. In any case, we have shown the possibility of successful multiple classification of objects.

42.8

Conclusion

In this article, we reviewed the classification of objects by CSI, using some examples for training and others for testing, which were obtained later under the same conditions. Classification was also performed using several machine learning methods, some of which showed higher results than single-layer FFNN. FFNN has higher potential, but it is more difficult to implement, because you need to select the appropriate parameters. The classification of several objects from the same material (metal), but of different shapes, was also considered. You can find the data used in the work and try to repeat the experiment in our repository [14]. Acknowledgements This research work was supported by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020”.

References 1. H. Chen, Y. Zhang, W. Li, X. Tao, P. Zhang, ConFi: convolutional neural networks based indoor Wi-Fi localization using channel state information. IEEE Access 5, 18066–18074 (2017). https://doi.org/10.1109/ACCESS.2017.2749516 2. S.A. Samadh, Q. Liu, X. Liu, N. Ghourchian, M. Allegue, Indoor localization based on channel state information, in 2019 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet) (IEEE, January 2019), pp. 1–4. https://doi.org/10.1109/wisnet.2019. 8711803 3. Y. Jing, J. Hao, P. Li, Learning spatiotemporal features of CSI for indoor localization with dual-stream 3D convolutional neural networks. IEEE Access 7, 147571–147585 (2019). https://doi.org/10.1109/ACCESS.2019.2946870

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4. E. Schmidt, D. Inupakutika, R. Mundlamuri, D. Akopian, SDR-Fi: deep-learning-based indoor positioning via software-defined radio. IEEE Access 7, 145784–145797 (2019). https://doi.org/10.1109/ACCESS.2019.2945929 5. A. Vora, P.X. Thomas, R. Chen, K.D. Kang, CSI classification for 5G via deep learning, in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall). (IEEE, September 2019), pp. 1–5. https://doi.org/10.1109/vtcfall.2019.8891133 6. H. Li, X. Zeng, Y. Li, S. Zhou, J. Wang, Convolutional neural networks based indoor Wi-Fi localization with a novel kind of CSI images. China Commun. 16(9), 250–260 (2019). https:// doi.org/10.23919/JCC.2019.09.019 7. J. Ding, Y. Wang, WiFi CSI-based human activity recognition using deep recurrent neural network. IEEE Access 7, 174257–174269 (2019). https://doi.org/10.1109/ACCESS.2019. 2956952 8. V.A. Pavlov, M.A. Galeeva, Detection and recognition of objects on aerial photographs using convolutional neural networks. J. Phys.: Conf. Ser. 1326(1), 012038 (2019). https://doi.org/ 10.1088/1742-6596/1326/1/012038 9. Y. Duan, S. He, D. Guo, F. Liu, General image segmentation by deeper residual U-Net, in Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence (April 2019), pp. 123–127. https://doi.org/10.1145/3325730.3325739 10. F. Shariaty, M. Baranov, E. Velichko, M. Galeeva, V. Pavlov, Radiomics: extracting more features using endoscopic imaging, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, October 2019), pp. 181–194. https://doi. org/10.1109/eexpolytech.2019.8906843 11. X. Meng, L. Yu, Z. Qin, Exploring the two-stage approach in neural network compression for object detection, in Eleventh International Conference on Machine Vision (ICMV 2018), vol. 11041 (International Society for Optics and Photonics, 2019), p. 110411W. https://doi.org/10. 1117/12.2522911 12. A.I. Bobrovsky, M.A. Galeeva, A.V. Morozov, V.A. Pavlov, A.K. Tsytsulin, Automatic detection of objects on star sky images by using the convolutional neural network. J. Phys.: Conf. Ser. 1236(1), 012066 (2019). https://doi.org/10.1088/1742-6596/1236/1/012066 13. E. Velichko, E. Nepomnyashchaya, M. Baranov, M.A. Galeeva, V.A. Pavlov, S.V. Zavjalov, E. Savchenko, T.M. Pervunina, I. Govorov, E. Komlichenko, A concept of smart medical autonomous distributed system for diagnostics based on machine learning technology, in Internet of Things, Smart Spaces, and Next Generation Networks and Systems (Springer, Cham, 2019), pp. 515–524. https://doi.org/10.1007/978-3-030-30859-9_44 14. Repository of Paper. https://github.com/maksimio/csi_classification. Accessed 26 May 2020

Chapter 43

Implementation of a Broadband Horn Antenna with High Level of Cross-polarization Discrimination in Microwave Inspection Systems Viktor V. Meshcheriakov , Semen N. Semenov , and Valentin I. Dudkin

Abstract The solution of radio polarimetry tasks in microwave screening systems (MSS) with aperture synthesis requires using antennas with a high level of cross-polarization discrimination (XPD) in a wide spatial angle. A spatial angle value can reach up to 30° during microwave image reconstruction in MSS which occurs on the commensurate distances with an aperture of the antenna structures. The antenna implementation with high value XPD which operates in upper X and lower Ku microwave subranges allows one to improve identification of the dangerous objects by microwave image of human body. In this article the main requirements were presented for receiver antennas implemented in MSS. The theoretical justifications were presented for choosing antenna construction based on simulation results in CST Studio. The simulation provided for pyramidal antenna and conical double-ridged antennas in circular and elliptical waveguides. The simulation results were compared with the experimental results of the antenna measurement carried out in anechoic chamber. Considering the value of XPD and complexity of production, the conical double-ridged antenna with elliptical waveguide was chosen as the most appropriate. The XPD of this antenna reached no less than 28 dB in space angle of 30°. The implementation of antenna in MSS improved the identification of dangerous objects. Keywords Microwave image antenna


Cross-polarization discrimination
Double-ridged

V. V. Meshcheriakov
S. N. Semenov (&) APSTEC Labs Ltd., 199–201 Nab. Obvodnogo kanala, Saint-Petersburg 190020, Russia e-mail: [email protected] V. V. Meshcheriakov e-mail: [email protected] V. I. Dudkin The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint-Petersburg, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_43

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Introduction

The modern microwave screening systems (MSSs) used for metal object detection on human’s body apply such methods as diffused cross-polarized radiation analysis [1] and radio polarimetry methods [2, 3]. In radio polarimetry methods the wide range double-ridge antennas are used for obtaining full information regarding microwave field diffused from human body [4, 5]. For acquisition of reliable data these antennas structures must have a high level of cross-polarization discrimination (XPD) [6, 7]. This is a challenging technical task if frequency range with an octave overlapping is applied [8, 9]. XPD (Cross Polar Discrimination—XPD [10]) refers to the minimum ratio of amplitudes of linear components of the electromagnetic field of main and cross-polarization defined in the Ludwig-3 coordinate system [11] for a given spatial angle. In the MSS developed by authors the aperture synthesis is applied for microwave image reconstruction. The low value of antenna XPD is essential limitation for microwave reconstruction in orthogonal polarization [1]. The objective of the current study is high XPD level receiving antenna development which operates in upper X and lower Ku microwave subranges and its implementation. The antenna implementation in MSS would increase efficiency of microwave reconstruction by depolarized radiation diffused by hidden dangerous objects in the human body. The required parameters should be reached and essential deficiencies in its construction should be resolved during antenna development.

43.2

Materials and Methods

The MSS used in experiments (Fig. 43.1) consisted of two antenna arrays (like in [12, 13] but operating in range of 8–18 GHz) located at an angle of 45° to the Xaxis. The inspective area included of transmitted radiation analysis area (Fig. 43.1, 2) and reflected radiation analyses area (Fig. 43.1, 3) [15]. According to reflected radiation analyses area’s size and location (Fig. 43.1, 3) the required space angle equals to 30° and specified XPD value should be maintained. Fig. 43.1 MSS location scheme, 1—direction of the main lobes of the transmitting antennas in the array, 2—zone of analysis of transmitted radiation, 3—zone of analysis of reflected radiation

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During the experiment described in [1] minimal XPD value was set for dangerous objects on human’s body classification: 5.0 in main polarization and 0.2 in cross-polarization, which is 28 dB. As result, the effectiveness of MSS operation [14] requires to satisfy the minimum XPD value while detecting in reflected radiation analyses area (Fig. 43.1, 3). The receiving Vivaldi antennas are applied in the experiment MSS studies [14]. However, during development of hidden objects detection system by cross-polarization scattering analysis methods it was discovered that these antennas XPD did not satisfy the required standards. Their XPD level within the angle of 30° was only 4 dB for linearly polarized radiation. At a space angle more than 30° and on frequency higher than 14 GHz cross-polarization value prevailed with XPD value decreased to −10 dB. Such results did not allow using these antennas for hidden dangerous objects detection described in [1]. Other Vivaldi antenna parameters in frequency range 8–18 GHz: • voltage standing wave ratio (VSWR) less than 1.8; • the average value of the directional factor is 9 dBi; • the width of the main lobe at the level of 3 dB in the dielectric substrate x0z (E-plane) of 70…60º, the width in the cross-polarized plane y0z (H-plane) of 85…30º are generally unsatisfactory for systems with aperture synthesis within the spatial angle of 30º. Apart from that, mechanical parameters of these receiving antennas had structural fragility and instability, as well as the insufficient repeatability of its electrodynamics parameters from sample to sample. The required electrodynamics and design parameters could also be obtained in a double-ridge antenna based on an elliptical section waveguide (Fig. 43.2) [15]. The elliptical shape of the waveguide eliminated the degeneration of the wave type H11 which was characteristic of a circular waveguide and provided fixation of the polarization plane [17].

Fig. 43.2 Frequency dependences of the parameters of the conic horn: a—CPR in a spatial angle of 30°, b—VSWR

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It was assumed in [16] that the location of the ridges along the small axis of the elliptical waveguide would improve the XPD value within the spatial angle of 30°. Figure 43.3 shows a 3D model of an elliptical double-ridge antenna developed in [16] (1–50-X coaxial cable with fluoroplastic insulator, terminated with SMA connector). Figures 43.4 and 43.5 show the frequency dependencies of VSWR, XPD within the spatial angle of 30°, the width of the directional pattern (DP) width in E- and H-plane, EBW and HBW and the directivity factor (DF). These results were obtained at the following elliptical antenna sizes: • transverse dimensions of an elliptical waveguide in the aperture plane 2 ael  bel = 11  7 mm2, where ael and bel are the major and minor semi-axes of an ellipse, respectively; • ridge thickness 3.4 mm; • slit width at the power source 1 mm; • antenna length 68 mm. The conical external shape of the antenna reduced the reception of foreign radiation by its aperture compared to a cylindrical antenna. The increase of the size on the right end of antenna added rigidity and stability to the entire structure. In the antenna aperture plane there was a small fluoroplastic ring which according to the results of the simulation significantly reduced the level of cross-polarized radiation in the lower part of the frequency range.

Fig. 43.3 Model of an elliptical double-ridged antenna: 1–50-X coaxial waveguide with fluoroplastic insulator

Fig. 43.4 Frequency dependences of the parameters of elliptical double-rib antenna: a—VSWR; b—CPR in spatial angle of 30°

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Fig. 43.5 Frequency dependences of the parameters of the an elliptical double-rib antenna: a— beam width in level—3 dB horizontal (blue line) and vertical (red line) planes; b—directional coefficient

Simulation of the elliptical double-ridge antenna gave the following parameters: • VSWR  2; • XPD in a spatial angle of 30° not less than 30 dB in the overlapping frequency range 2.2; • the width of the main lobe and DF meet the requirements to ensure the reception of the signal from the target in the analysis area of the reflected microwave radiation (see Figs. 43.1, 3).

43.3

Results and Discussion

In order to preserve the XPD level during the formation of an ensemble of receiving elliptical double-ridge antennas, it was necessary to use an absorbing material, for example, ECCOSORB VHP-2-NRL [17, 18]. The XPD value of the manufactured antenna at a frequency of 18 GHz within the spatial angle of 30° is better than 30 dB; with a decrease in the frequency, the level of discrimination increases. This is entirely consistent with the results of the simulation which allows using these antennas in MSSs. After that the antennas were installed in the MSS. HSR (Human Security Radar) [15] was used as MSS in the experiments with new designed antennas. In order to verify that parameters of elliptical double-ridged antennas are better than Vivaldi antennas, experiments were carried out. In these experiments four people with different BMI (Body mass index) were taking turns in zone of analysis of reflected radiation (Fig. 43.1, 3) at three positions with a stimulant of dangerous object and without one: 1. on the line located 1 m from the zone of analysis of transmitted radiation (Fig. 43.1, 2); 2. on the line located 2 m from the zone of analysis of transmitted radiation; 3. on the line located 3 m from the zone of analysis of transmitted radiation.

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Fig. 43.6 The relation of co-components (Co) to cross-components (Cr) are reflected from the same point from each person at three positions (Zone 1, Zone 2, Zone 3) in zone of analysis of reflected radiation. “Diamond” is a person with dangerous object stimulant, “cross” is a person without dangerous object stimulant

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In each position microwave image reconstruction was made. The results are presented in the Fig. 43.6. The experiment showed that using elliptical double-ridged antennas it was possible to distinguish the presence of the dangerous metal object on the human body by analyzing reflected microwave radiation. At the same time, Vivaldi antennas could not effectively operate far from receiver-transmitters elements.

43.4

Conclusion

A double-ridge elliptical antenna was developed and manufactured, with VSWR  2 and XPD in a spatial angle of 30° not less than 28 dB in the frequency range overlapping octave. Structurally, the double-ridge antenna was assembled in three parts—the upper and lower halves of the elliptical waveguide and the double-ridge plate, which are joined together in one piece with screws. Although this design meets the tight design tolerances set by the simulation, it requires high production standards. The antenna can be used in MSSs to detect the effect of microwave radiation depolarization by hidden dangerous objects on the human body. The implementation of these antennas in MSS (HSR) would increase the efficiency of dangerous objects detection on the human body.

References 1. A.D. Grigoriev, V.V. Mesheryakov, S.N. Semenov, Investigation of polarization changing effect by hidden objects placed on a human body. J. Russ. Univ.: Radioelectron. 6, 41–45 (2015). (In Russian) 2. W.L. Cameron, N.N. Youssef, L.K. Leung, Simulated polarimetric signatures of primitive geometrical shapes. IEEE Trans. Geosci. Remote Sens. 34(3), 793–803 (1996) 3. R. Touzi, F. Charbonneau, Characterization of target symmetric scattering using polarimetric SARs. IEEE Trans. Geosci. Remote Sens. 40(11), 2507–2516 (2002) 4. H. Fallahi, M. Kaboli, S.A. Mirtaheri, A. Mehrdadian, Design and implementation of 0.7 to 7 GHz broad-band double-ridged horn antenna, in 7’th International Symposium on Telecommunications (IST’2014) (IEEE, Tehran, 2014), pp. 250–255 5. C.I. Paez, E.H. Criollo, Improved broadband double ridged horn antenna without split radiation pattern. IEEE Latin Am. Trans. 14(3), 1156–1161 (2016) 6. M. Migliaccio, A. Gambardella, M. Tranfaglia, SAR polarimetry to observe oil spills. IEEE Trans. Geo-Sci. Remote Sens. 45(2), 506–511 (2007) 7. M. Martorella, F. Berizzi, S. Bruscoli, Use of genetic algorithms for contrast maximization and entropy minimization in ISAR autofocusing. EURASIP J. Appl. Sig. Process. 2006(1), 087298 (2006) 8. O.B. Jacobs, J.W. Odendaal, J. Joubert, Elliptically shaped quad-ridge horn antennas as feed for a reflector. IEEE Antennas Wirel. Propag. Lett. 10, 756–759 (2011) 9. Y. Ma, J. Hu, Y. Zhang, L. Li, L. Liu, Broadband dual-polarization microstrip antenna with high cross-polarization isolation for SAR, in 2018 China International SAR Symposium (CISS) (IEEE, Shanghai, 2018), pp. 1–3

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10. D.I. Stojce, Global Mobile Satellite Communications Theory, 2nd edn. (Springer International Publishing, Switzerland, 2017) 11. A. Ludwig, The definition of cross polarization. IEEE Trans. Antennas Propag. 21(1), 116– 119 (1973) 12. A.V. Moroz, V.V. Davydov, Fiber-optical system for transmitting heterodyne signals in active phased antenna arrays of radar stations. J. Phys: Conf. Ser. 1368(2), 022024 (2019) 13. A.V. Moroz, V.V. Davydov, Features of transmission bearing and heterodyne receivers for signals in fiber-optic communication line in active phased array antenna. J. Phys: Conf. Ser. 1410(1), 012212 (2019) 14. S.I. Vorobiev, V.P. Averyanov, M.Y. Osipov, S.N. Semenov, Multi-position system for constructing a microwave image in real time, in Collection of Articles of 13th International Science and Practical Conference “Fundamental and Applied Research, Development and Application of High Technologies in Industry and Economics”, St. Petersburg, Russia, vol. 1, ed. by A.P. Kudinov (2012), pp. 44–47. (In Russian) 15. V. Averianov, A. Evsenin, I. Gorshkov, P. Iurmanov, A. Kuznetsov, G. Labzovsky, V. Meshcheryakov, M. Mokhova, S. Semenov, D. Vakhtin, I. Vorobev, S. Vorobyev, D. Kellermann, Automatic standoff detection of threats in crowed areas, in 9th Future Security: Security Research Conference Processing, ed. by K. Thoma, I. Häring, T. Leismann (Fraunhofer Verlag, Berlin, 2014), pp. 322–329 16. V.V. Meshcheriakov, N.V. Markova, P.D. Iurmanov, Modeling and practical implementation of a broadband double-ridged horn antenna with an operating range more than an octave and a high level of cross-polarization discrimination. J. Russ. Univ.: Radioelectron. 22(5), 42–51 (2019) 17. G.D. Tsogkas, J.A. Roumeliotis, S.P. Savaidis, Cutoff wavelengths of elliptical metallic wave-guides. IEEE Trans. Microw. Theory Tech. 57(10), 2406–2415 (2009) 18. M. Nel, J. Joubert, J.W. Odendaal, The measurement of complex antenna transfer functions for ultra-wideband antennas in a compact range. IEEE Antennas Propag. Mag. 56(6), 163– 170 (2014)

Chapter 44

Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges Kseniya V. Trusova

Abstract Machine learning methods find wide application to build digital twins of avionics systems under the integrated system health management. This allows representing the system performance and managing all stages of the system life cycle that significantly saves resources of manufacturers. In this work, a possibility of the machine learning methods application was researched for avionics objects health analysis and faults localization challenges to solve classification problems under building digital twins in ISHM. Decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors methods were considered. The research results show that some methods are effective for the health analysis challenge, and some methods are effective for the faults localization challenge. The application of the results will allow getting a base to build digital twins of avionics systems.








Keywords Aircraft avionics Integrated system health management Health analysis Faults localization Machine learning Classification model Aggregated model Digital twin




44.1










Introduction

Avionics systems health analysis and faults localization challenges is appropriate to consider as a part of the integrated system health management (ISHM) because ISHM implies these problems. Base of this approach was established near fifty years ago [1], and ISHM is continually improving [2, 3]. ISHM operates with health of an object across its whole life cycle that implies design or conception, development, manufacturing and operation stages. There are now a lot of ISHM solutions which involve not only techniques, technologies and methods but also organization of ISHM in general, requirements to design and development, and maintenance approach. K. V. Trusova (&) JSC RDC St. Petersburg Branch, Saint-Petersburg 195009, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_44

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Machine learning methods are also used ISHM to solve complex problems [4]. For the health analysis and the faults localization in operation, the built-in test equipment (BITE) and continuous monitoring (CM) are designed with an avionics system and pass all stages of a life cycle implying design, development, manufacturing, testing and maintenance in operation. BITE and CM operation assessment through the whole life cycle is a complex problem dealing with a big data volume processing which should automate. The automation allows improving quality of a product reducing errors which occur through manual operations with data, and saving the time spent on debugging of BITE and CM at stages of a life cycle. The conception of digital twins [5] was introduced in ISHM to represent systems performance and to solve similar problems. Machine learning methods find the wide application to build digital twins. The paper describes the research results of a possibility applying machine learning methods to solve classification problems for the health analysis and faults localization challenges under building digital twins in ISHM. This work is continuation of the research [6] that describes an approach based on aggregated models. The objective of the research has been defined as to adapt an aggregated model obtained describing of an avionics object to a classification model uniting two approaches and to analyze a possibility applying machine learning methods by a classification model. The paper is organized the following. Section 44.2 describes an aggregated model, a classification model, the adaptation of the aggregated model to the classification model, the researched methods of machine learning and diagnosis programs synthesis correlation with a classification model. Section 44.3 describes the classification model of the recorder from Indication/Recording Systems, its research results and discussion of the results.

44.2

Models and Methods

44.2.1 Aggregated Models An aggregated model is considered in details in the previous work mentioned above. The main concept concludes the following. The aggregation in problems of model-based analysis means the building of models based on sets of aggregated states. The aggregated state is a combination of attributes that defines the states of common characteristics of the object which can be united in one class. An object health analysis challenge concludes the authentication of the health state observed with one of the previously determined aggregated states. The aggregated model for the health analysis is represented as ordered sets and depends on the characteristics of the object of analysis, an objective content, and analysis conditions as well as the mathematical model of the analysis process.

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Attributes used in the health analysis process can be discrete (binary or integer) or continuous (interval). Continuous attributes were not considered in this work. The aggregated model of the object of analysis for discrete attributes is MAO ¼ fS; P; R; P; Ug

ð1Þ

S is a set of health states for detecting with established analysis depth, P is a set of attributes by which all health states are pairwise distinguishable, R is a set of attributes reference values, P is a set of health states probabilities, U is an expression that each attribute value is predetermined in each health state. A process analysis aggregated model is  ^ R MPA ¼ Y; X; P; P;

ð2Þ

Y is a set of observed attributes values, X is a set of informational states for a ^ is a set of modeled analysis process, P is a probability measure on the X set, P attributes checks, R is a set of decisions about object health. ^ sets are defined from conditions and an analysis purpose (moniS and P (P) toring or diagnosis). The similar approach based on describing states and transitions of an avionics system is used to show for which type of systems prognostics is not meaningful, and decision making should be unified with system health management [7].

44.2.2 A Classification Model All classification methods operates model with one dependent variable y and several independent variables x1 ; x2 ; . . .; xn . The dependent variable y takes one of two values–0 (event has not happened) and 1 (event has happened). Independent variables x1 ; x2 ; . . .; xn is an attribute set whose values must be applied to compute of probability of acceptance of one or another value by dependent variable y. Therefore, input data of a classification model for learning is
Xtrain; ; Ytrain ¼ ðx1 ; x2 ; . . .; xn ; Y Þ

ð3Þ

44.2.3 Adaptation of the Aggregated Model to the Classification Model An aggregated model applied for description of an avionics object, is not difficult for adapting to a classification model which is used by machine learning to solve classification problems.

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For the aggregated model of the object of analysis, a set of health states S is equivalent to a vector of dependent variables Ytrain . A set of attributes P on which all health states are the pairwise distinguishable and set of attributes reference values R are equivalent to a set of independent variables Xtrain . P is a description of a variable, and R is a value of a variable. U is an expression of S  R and so Y  X. A set of health states probabilities P is not considered this research because it is a particular challenge for research, related to constructing of aggregated models themselves. For the adaptation of the aggregated model of the analysis process, is supposed that input test data for learnt classification model is a set of attributes Xtest ¼ x1 ; x2 ; . . .; xn

ð4Þ

which are ordered by random combinations. Then a set of attributes values ^ is a description of observed Y is equivalent to Xtest . A set of attributes checks P Xtest . A set of decisions about object health R is equivalent to a set of recognized health states Ypred that is obtained at output of the model. It is necessary to make an important note. For aggregated models, R includes the finite states S as well as transient informational states X. For the classification model, an accuracy of model description can be defined by researcher during the model construction. So a set of informational states for a modeled analysis process X and a probability measure on the X set P can be defined by researcher and built-into the model. These models can be used as a base to build digital twins.

44.2.4 The Researched Methods of Machine Learning The research of machine learning methods through the model considered above was implemented by using resources of Python programming language and Spyder framework using built-in libraries. The following machine learning methods were researched: decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors. The classification model described above underlies all methods. Methods differ by their classifiers based on different mathematic principles. Spyder library has built-in classifiers for all methods. Logistic Regression The logistic regression is based on the comparison probability of an event with a logistic curve.

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The following hypotheses estimate that events probabilities y = 1 and y = 0 equal Pfy ¼ 1jxg ¼ f ðzÞ; Pfy ¼ 0jxg ¼ 1  f ðzÞ;

ð5Þ

z ¼ bT x ¼ b0 þ b1 x1 þ . . .bn xn ;

ð6Þ

x and b are vectors of independent variables values x1 ; x2 ; . . .; xn and regression parameters b1 ; b2 ; . . .; bn and f(z) is a logistic function which is a classifier f ðzÞ ¼

1 1 þ ez

ð7Þ

If predicted probability Pfy ¼ 1jxg > 0,5 then y = 1, otherwise y = 0. The classification rules obtained are linear classifiers. Decision Tree Classification The decision tree classification uses the target value conception as classifier. A tree is built by splitting the input variables set to subsets using the target value obtained through the variables check results. It is an old known method. Random Forest Classification The random forest classification is based on the same principle as decision tree classification. A forest is constructed with multitude of decision trees. Support Vector Machine The support vector machine method uses maximum margin hyperplane concept as a classifier. The main idea is the search of dividing hyperplane between classes with maximum distance in this space. The points should be presented by two-dimensional space  !
!

x1 ; y1 ; x2 ; y2 ; . . .; ! xn ; yn

ð8Þ

yi accepts a value 1 or −1 depended on which class the point xi is belonged to. Dividing hyperplane looks as follows ~ w ~ xb¼0 Vector w is normal vector to the dividing hyperplane. from hyperplane to the origin of coordinates.

ð9Þ b k wk

equals the distance

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Two parallel hyperplanes are constructed on the both sides of dividing hyperplane through the points of different classes (these points are called support vectors): 

~ w ~ xb¼1 ~ w ~ x  b ¼ 1

ð10Þ

The search of dividing hyperplane implies the minimizing of kwk subject to
yi ~ w! xi  b  1 for i ¼ 1; . . .; n

ð11Þ

Kernel Support Vector Machine The kernel support vector machine method is based on a concept similar to the support vector machine using a nonlinear classifier. For nonlinear classifier construction, the conversion from two-dimensional to tree-dimensional space by integral transformation is applied. This integral transformation is called a kernel function and this action is also called a kernel trick. The kernel function depends on the kind of integral transformation. Naive Bayes Naive Bayes method is based on applying Bayes’ theorem with strong (naive) independence hypotheses of independent variables x1 ; x2 ; . . .; xn between each other. Naive Bayes classifier uses the conditional probability model PðYjx1 ; x2 ; . . .; xn Þ. K-Nearest Neighbors The k-nearest neighbors method implies the choice of a number of neighbors which classes are already known. Then a distance (usually Euclidian) between neighbors and the observed point is measured. The point belongs to that class which is the most ordinary among neighbors of this point.

44.2.5 Diagnosis Programs Synthesis Diagnosis programs synthesis was mentioned in previous work describing aggregated models. The dynamic programming and the branch and bound method were supposed for the synthesis of optimal and suboptimal programs. Therefore, optimization criteria are necessary to select. Certainty of diagnosis, a program realization cost, the integrity of diagnosis, the information utility were offered. Diagnosis programs synthesis allows optimizing health state recognition by checking the most significant attributes chosen by optimization criteria. It is an optimal or suboptimal program for the recognition. Nevertheless, it is not the recognition itself. Machine learning methods particularly solve the health state recognition problem. Therefore, the diagnosis program synthesis challenge is formulated before recognition problem by machine learning methods. The significance order of attributes can be a built-in classification model if it is necessary. However, this challenge requires specific research that should take into account the complexity of practical problems and objects.

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Research Results

44.3.1 Recorder Data Presentation for a Classification Model The recorder of tele video information at the Indication/Recording Systems was chosen as an object of research. Its functions, architecture and aggregated models were considered in previous work. This object is simple and obvious for the research. The obviousness is an important factor because if an object more complex a model contains more data. Observing correctness of recognition becomes more difficult in these conditions. There is a threat of incorrect results interpretation. Data for the classification model training and test data for its testing only are considered in this work. The classification model was adapted from aggregated models by paragraph 2.3. Similar approach to acquisition of data of an object is represented to solve a prognostic challenge for the radar [8].

44.3.2 Training Data A Health Analysis Challenge The model has three attributes as a set of independent variables Xtrain and three health states as a vector of dependent variables Ytrain (Table 44.1). Three attributes are obtained by checks implemented by recorder built-in test equipment. Each combination of attributes values corresponds to definite recorder health state value in general significance. Therefore, the recorder cannot operate if its solid-state drive was broken but this detail is a faults localization challenge yet. A Faults Localization Challenge The model has two attributes as a set of independent variables Xtrain and six different health states as a vector of dependent variables Ytrain (Table 44.2). Two attributes consist of an attribute check and an attribute value as a result of the check. Data has only three attribute checks and two attributes values for each check. It is necessary to present data using each attribute check twice because each attribute value corresponds different health states.

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Table 44.1 Training data for the health analysis challenge Xtrain A query response of the video board x1

Software checksum authentication x2

Read/write to allocated SSDa slot operations accomplishing x3

0 1

0 0

0 0

0

1

0

1

1

0

0

0

1

1

0

1

0

1

1

1

1

1

a.

Health Ytrain Up Partly Down Partly Down Partly Down Full Down Full Down Full Down Full Down

Solid-State Drive

Table 44.2 Training data for the faults localization challenge Xtrain Attribute check x1

Health Ytrain Attribute value x2

A query response of the video board A query response of the video board Software checksum authentication

0 1 0

Software checksum authentication

1

Read/write to allocated SSD slot operations accomplishing Read/write to allocated SSD slot operations accomplishing a. Serial Advanced Technology Attachment

0

Videoboard up Videoboard down SATAa controller software up SATA controller software down SSD up

1

SSD down

44.3.3 Test Data Input test data loaded for testing of machine learning methods for the trained classification model is represented in Tables 44.3 and 44.4 for the health analysis and faults localization challenges.

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Table 44.3 Test data for the health analysis challenge Xtest A query response of the video board x1

Software checksum authentication x2

Read/write to allocated SSDa slot operations accomplishing x3

1 0 0 1 0 0 1

0 1 0 1 0 0 0

0 1 0 0 1 0 0

Table 44.4 Test data for the faults localization challenge Xtest Attribute check x1

Attribute value x2

A query response of the video board Read/write to allocated SSD slot operations accomplishing Software checksum authentication Software checksum authentication A query response of the video board Software checksum authentication Read/write to allocated SSD slot operations accomplishing

1 0 1 0 0 1 0

44.3.4 Results As mentioned above the research of machine learning methods was implemented by classifiers that are built-into framework libraries. For the health analysis challenge, decision tree, random forest, kernel support vector machine, naive Bayes methods showed the best results. Recognized states Ypred coincided with health states from Ytrain for all combinations of attributes values from Xtest (Table 44.5). Logistic regression, support vector machine, k-nearest neighbors methods showed worse results. The classifiers of methods did not recognize the “Up” health state. Logistic regression and support vector machine recognized “Up” as “Partly Down”, k-nearest neighbors recognized “Up” as “Full Down” when the number of neighbors equaled five, and the same situation as logistic regression and support vector machine when the number of neighbors equaled two. Therefore, applying these methods will give false positive (false response) for the “Up” health state. Figure 44.1 shows whole results for the health analysis challenge.

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Table 44.5 Health states recognition results for the health analysis challenge Xtest A query response of the video board x1

Software checksum authentication x2

Read/write to allocated SSDa slot operations accomplishing x3

1

0

0

0

1

1

0 1

0 1

0 0

0

0

1

0 1

0 0

0 0

Health Ypred Partly Down Full Down Up Partly Down Full Down Up Partly Down

For the faults localization challenge, all methods besides k-nearest neighbors showed good results. Recognized states Ypred coincided with health states from Ytrain for all attributes values from Xtest (Table 44.6).

Fig. 44.1 Health states recognition results for the health analysis challenge

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Table 44.6 Health states recognition results for the faults localization challenge Xtest Attribute check x1

Health Ypred Attribute value x2

A query response of the video board Read/write to allocated SSD slot operations accomplishing Software checksum authentication

1 0

Videoboard down SSD up

1

Software checksum authentication

0

A query response of the video board Software checksum authentication

0 1

Read/write to allocated SSD slot operations accomplishing

0

SATA controller software down SATA controller software up Videoboard up SATA controller software down SSD up

Fig. 44.2 Health states recognition results for the faults localization challenge

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Quantity of health states, which was recognized correctly by k-nearest neighbors, equals five. Quantity of health states, which was recognized incorrectly, equals two. This result occurs when the number of neighbors equals two. If the number of neighbors more than two the result becomes worse. Figure 44.2 shows the whole results for the faults localization challenge.

44.3.5 Discussion Researched machine learning methods have advantages and disadvantages that influence choice one or another method for applying to practical problems. Logistic Regression and support vector machine methods are not appropriate for nonlinear problems. Kernel support machine vector and naive Bayes work on nonlinear problems. Decision tree and random forest classification work on linear as well as nonlinear problems. Support vector machine and kernel support vector machine are not sensitive to overfitting but it is not the best choice for a large number of features/ attributes. For the decision tree and random forest classification, overfitting can easily occur. There are also another advantages and disadvantages of methods. Incorrect results of the recorder health state recognition in this research can be related to the number of attributes/features for the health analysis challenge. As for k-nearest neighbors method, probably its mathematical principles do not appropriate for these practical problems. Classifiers also have parameters settings that depend on methods. These settings are adapted to the best classification results obtaining. For random forest classification, a number of threes should be chosen. It equals ten for the recorder classification model. For the kernel support vector machine, an integral transformation type should be chosen. The Gaussian integral transformation was chosen for the recorder classification model. K-nearest neighbors classifier needs a choice of neighbors number as mentioned above. Support vector machine method is also applied to solve regression problems for avionics systems. This method was mentioned to prognostics challenge [9, 10]. Comparison of linear support vector machine, k-nearest neighbors and decision tree classifiers was used for fault detection and fault isolation of the fuel system [11]. Described machine learning methods finds the wide application in other areas to solve classification problems [12–16]. There are a lot of examples applying other machine learning methods. Support vector regression, neural gas clustering, multiple-classes support vector machine and Bayesian fuzzy fault tree were considered to isolate and predict faults from a component to a system/subsystem [17]. A convolutional neural network was used for classification and clustering analysis of spacecraft electrical signal [18].

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There are also different ways to estimate an efficiency of methods. Classifiers have a number of events recognized correctly and a number of events recognized incorrectly. Through a number of events recognized incorrectly, a false positive and a false negative can occur. The false positive is an error that classifier has recognized as an event has happened when actually it has not happened. The false negative is an error that classifier has recognized as an event has not happened when actually it has happened. The false negative worse than the false positive because it brings about more dangerous wastage. All together described numbers consist a confusion matrix used for efficiency analysis of models. For the health analysis challenge, the recorder has three health states which are represented as classes for the recognition while typical data consists of only two classes. In this case, the false positive means that the “Partly Down” health state was recognized when actually recorder had “Up” health state. For the faults localization challenge, the recorder has six possible health states or three health states which are pairwise distinguishable by attributes check (a query response of the video board, software checksum authentication, read/write to allocated SSD slot operations accomplishing). In this case, the false positive is a situation which for example “Videoboard down” was recognized when actually recorder had “Videoboard up”. The false negative is the opposite situation when “Videoboard up” was recognized when actually recorder had “Videoboard down”. Therefore, a confusion matrix consideration depends on the classification model itself and data interpretation. A cumulative accuracy profile (CAP) and a receiver operating characteristic (ROC) are also applied for the classification models efficiency estimation. CAP allows visualizing a confusion matrix analysis and represents the total number of positive results versus the corresponding total number of a classifying parameter. ROC curve represents correlation between false positives and false negatives. ROC curves were analyzed for model optimization by Gauss-Newton method to solve anomaly diagnosis and fault classification problems with prognostics [19]. CAP and ROC were not considered for the intuitive recorder classification model. In this case, the confusion matrix estimation is enough.

44.4

Conclusion

The paper describes the adaptation of the aggregated models proposed for the description of avionics objects in previous research to classification models for the research of a probability applying machine learning methods for the health analysis and faults localization challenges. As an example, the recorder of tele video information at the Indication/Recording Systems was considered. The results were obtained and estimated for the following machine learning methods: decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors.

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For the health analysis challenge, decision tree, random forest, support vector machine, kernel support vector machine, naive Bayes, logistic regression methods showed the good results of the classification. For the faults localization challenge, all methods showed the good results of the classification besides k-nearest neighbors method. This method does not fit to solve described problems. Whereas the recorder is described by three classes instead two classes for the recognition, it was an important to research a possibility applying machine learning methods for the health state recognition by the recorder’s example because avionics objects often have more than two health states. Therefore, the research helped to get a view of a possibility applying machine learning methods for the avionics health analysis and faults localization challenges as a base to build digital twins in ISHM. An aggregated model allows describing avionics objects more completely than a classification model that is necessary for the automation traceability of avionics design and development processes. Nevertheless, a classification model allows applying machine learning methods for the automation of avionics life cycle processes such as a design modeling, a testing at the different stages, a faults localization implementing by manufacturer and others. Machine learning methods can also be used for BITE and CM algorithms for software optimizing. Correlation between classification models construction and optimal or suboptimal diagnosis programs synthesis mentioned in previous work was also considered in the paper. Results obtained by optimal or suboptimal diagnosis programs synthesis construction can be a built-in classification model if it is necessary to solve a problem. On the next step plans applying described approach to more complex avionics systems objects that have a lot of attributes, its combinations and health states corresponding to them. Acknowledgements The author would like to thank Peter the Great St. Petersburg Polytechnical University for providing the opportunity publishing this paper.

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6. K. Trusova, Health analysis and diagnostic program synthesis for avionics systems, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, Saint Petersburg, 2019), pp. 103–107 7. Cornell University Homepage, https://arxiv.org/pdf/1903.03948.pdf. Accessed 01 June 2020 8. Y. Lyu, Z. Pang, C. Zhou, P. Zhao, Prognostics and health management technology for radar system. MATEC Web Conf. 309(1), 04009 (2010) 9. W. Qiancheng, Z. Shunong, K. Rui, Research of small samples avionics prognostics based on support vector machine, in Prognostics and System Health Management Conference (PHM) (IEEE, 2011), pp. 1–3 10. H. Chen, W. Zhang, J. Shi, K. Jiang, Research on health assessment method for power supply element of avionics electronic, in IEEE Chinese Guidance, Navigation and Control Conference (CGNCC) (IEEE, Nanjing, 2016), pp. 807–813 11. M. Jung, O. Niculita, Z. Skaf, Comparison of different classification algorithms for fault detection and fault isolation in complex systems. Procedia Manuf. 19(2017), 111–118 (2018) 12. V.A. Pavlov, M.A. Galeeva, Detection and recognition of objects on aerial photographs using convolutional neural networks. J. Phys: Conf. Ser. 1326, 012038 (2019) 13. A.I. Bobrovsky, M.A. Galeeva, A.V. Morozov, V.A. Pavlov, A.K. Tsytsulin, Automatic detection of objects on star sky images by using the convolutional neural network. J. Phys: Conf. Ser. 1236, 012066 (2019) 14. A.K. Tsytsulin, A.I. Bobrovskiĭ, A.V. Morozov, V.A. Pavlov, M.A. Galeeva, Using convolutional neural networks to automatically select small artificial space objects on optical images of a starry sky. J. Opt. Technol. 86(10), 627–633 (2019) 15. A.V. Pavlov, J.A. Serdyuk, A.B. Ustinov, Machine learning and the Schrödinger equation. J. Phys: Conf. Ser. 1236, 012050 (2019) 16. E. Velichko, E. Nepomnyashchaya, M. Baranov, M.A. Galeeva, V.A. Pavlov, S.V. Zavjalov, E. Savchenko, T.M. Pervunina, I. Govorov, E. Komlichenko, A concept of smart medical autonomous distributed system for diagnostics based on machine learning technology, in 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Internet of Things, Smart Spaces, and Next Generation Networks and Systems, ed. by O. Galinina, S. Andreev, S. Balandin, Y. Koucheryavy. LNCS, vol. 11660 (Springer, St. Petersburg, 2019), pp. 515–524 17. W. Yin, G. Wang, W. Miao, M. Zhang, W. Zhang, Semi-supervised learning of decision making for parts faults to system-level failures diagnosis in avionics system, in 31th IEEE/ AIAA Digital Avionics Systems Conference (DASC) (IEEE, Williamsburg, 2012), pp. 7C4-1–7C4-14 18. Y. Liu, K. Li, Y. Zhang, S. Song, MRD-nets: multi-scale residual networks with dilated convolutions for classification and clustering analysis of spacecraft electrical signal. IEEE Access 7, 171584–171597 (2019) 19. B.U. Kim, C.C. Lynn, N. Kunst, S. Vohnout, K. Goebel, Fault classification with Gauss-Newton optimization and real-time simulation, in Aerospace Conference (IEEE, Big Sky, 2011), pp. 1–9

Chapter 45

Research on FBMC/OQAM Spectral and Energy Characteristics for Different Prototype Filters Lavrenyuk Ilya , Maksimova Elizaveta, and Sadovaya Yekaterina

Abstract In this paper we investigate the performance of filter bank multicarrier system with offset QAM modulation (FBMC/OQAM) and compare it with commonly used cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) multicarrier scheme. FBMC utilizes special prototype filtering, on which many system parameters depend, and therefore the accurate choice of prototype filter is crucial for the overall performance. We consider known filters such as PHYDYAS, Nyquist, and filters obtained during optimization procedure and compare the corresponding bandwidth efficiency, level of out-of-band emissions, and the power spectral density forms with OFDM system. In addition, we estimate BER performance in AWGN and fading pedestrian, vehicle and urban (EPA, EVA and ETU) channels and calculate complementary cumulative distribution function (CCDF) for estimation of peak-to-average power ratio (PAPR) to understand the overall energy efficiency of system with different prototype filters. The results demonstrate that the energy efficiency of FBMC/OQAM system for certain prototype filters is relatively close to performance of the OFDM system, while bandwidth utilization is significantly better. Keywords OFDM

45.1


FBMC
Prototype filters

Introduction

Multicarrier signals such as OFDM are the most common for high-speed wireless communications. Currently, one of the main development trends of such signals is to increase the bandwidth efficiency. Promising approach is to use so-called spectral L. Ilya (&)
M. Elizaveta Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya 29, St. Petersburg 195251, Russia e-mail: [email protected] S. Yekaterina Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_45

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and time compression (SEFDM and FTN) [1–5]. The main disadvantage of such method is extremely high receivers’ computational complexity, because of high intersymbol interference affection [6–9]. Another one of the potential candidates for future wireless communications is Filter-bank multicarrier (FBMC) technology, and such approach seem to be more practical from the computational complexity point of view [10–13]. The FBMC signals form with the applying of subcarrier filtration by special prototype filters (PF). Such approach significantly reduces the out of band emissions of the transmitted signal comparing to common OFDM signaling [14, 15], which is important for compact frequency localization and the overall bandwidth efficiency of a system. The accurate and weighted choice of shaping prototype filter significantly affects the spectral and temporal characteristics of the FBMC signals, and the complexity of receiver is also depending on the PF. Thus, the employment of different types of PFs and the analysis of signals in certain conditions and restrictions is the point of interest in FBMC technology. The main goal of this paper is to analyze and compare FBMC signals obtained using different prototype filter techniques in order to determine spectral and energy characteristics and overall bandwidth and energy efficiency of FBMC systems. There are basically three methods for generating the coefficients of prototype filters: the method of frequency sampling of the filter response, the window weighting method, and the method of direct optimization of filter coefficients [16– 18]. PHYDYAS and Dolph-Chebyshev filters resulting from the use of the first formation method, the RC and RRC filters resulting from the second method, while the filters developed in [17, 19], consider the formation using the coefficient optimization procedure. There are several methods of modulation within FBMC signal, and in this paper, we will investigate the characteristics of FBMC signal based on Offset Quadrature Amplitude Modulation (OQAM) scheme [20]. The FBMC/OQAM signals in-phase and quadrature components are transmitted with a time offset relative to each other. The combination of filter banks with OQAM modulation leads to a higher data transfer rate without the need for a time dispersion protection or cyclic prefix as in Universal Filtered Multi-Carrier and OFDM, and therefore it is more promising modulation compared to another FBMC approaches.

45.2

Analysis of Time-Frequency Characteristics of FBMC Signals Based on Different Prototype Filters

Let us define input complex symbol on k-th subcarrier as Ck: Ck ðnÞ ¼ Ck;I ðnÞ þ jCk;Q ðnÞ;

ð1Þ

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where Ck,I and Ck,Q are real and imaginary components of n-th complex symbol. Then, the corresponding baseband OQAM/FBMC signal will have the form: K 1 X X

T ðCk;I ðnÞhðt  nTÞ þ jCk;Q ðnÞhðt  nT  ÞÞ expðjkut Þ; ut 2 k¼0 n 2pt p þ ; ¼ T 2

sðtÞ ¼

ð2Þ

where h(t) is the impulse response of the PF. A prototype filter with a finite impulse response (FIR) h[n] can be obtained by sampling a prototype filter with continuous time h(t). Due to the narrow bandwidth of the filters used in the FBMC, a long tail in the time domain is inevitable, which creates additional computational complexity. In addition, due to the lack of orthogonality, the interference introduced between the symbols complicates the channel alignment algorithms on the receiving side. The basic parameters to analyze for prototype filters selection are mutual interference value, in-and out-of-band energy, level of sidelobes. Based on these criteria and main filters designing techniques let’s consider three commonly used impulses, namely the RRC impulse, the Martin impulse proposed in the research project PHYDYAS [21] for use with the FBMC and the Dolph–Chebyshev impulse (DC), along with filters based on optimization method proposed in [19].

45.2.1 RRC Filter RRC filters are widely used in telecommunication systems to minimize ISI at the receiver. The impulse response of the RRC pulse is: 8   p1ffiffiffi 1  a þ 4 a t¼0 > > p T >   p    p  < a 2 2 T pffiffiffi 1 þ t ¼  4a p sin 4a þ 1  p cos 4a ð3Þ hð t Þ ¼ 2 T t t t > > 1ffiffiffi sinðpT ð1aÞÞ þ 4aT cosðpT ð1 þ aÞÞ > p other : 2 T pTt 1ð4aTt Þ where T is the symbol interval, and a is the decay coefficient, which shows the excess bandwidth of the pulse in the frequency domain.

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45.2.2 PHYDYAS Filter Let M be the number of subcarriers. Then the impulse response: pð nÞ ¼ P 0 þ 2

XK1 k¼1

  2pk ð n þ 1Þ ð1Þ Pk cos KM k

ð4Þ

for n = 0, 1, …, KM − 2 and K = 4, where the coefficients Pk, k = 0, …, K − 1 were chosen using the frequency sampling method, and assume that the following values: P0 ¼ 1 P1 ¼ 0:97195983 pffiffiffi P2 ¼ 1= 2 P3 ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  P1

45.2.3 Dolph-Chebyshev (DC) Filter The DC filter minimizes the width of the main lobe for a given attenuation of the side lobes. Its impulse response in discrete time is: pð nÞ ¼

    

XðN1Þ=2 1 kp 2nkp A 1020 þ 2 T x cos cos N1 0 k¼1 N N N

ð5Þ

for n = 0, ±1, …, ±(N − 1)/2, where N is the number of coefficients, A is the attenuation of the side lobes in dB, 

  1 A 1 20 cosh 10 x0 ¼ cosh N1 and

T n ð xÞ ¼

cosðncos1 ð xÞÞ coshðncos1 ð xÞÞ

is a Chebyshev polynomial of the first kind.

j xj  1 j xj [ 1

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45.2.4 Optimal Filters In addition, an optimal approach to the formation of filters is possible, for example, in order to ensure minimum energy outside the passband of the filter under the condition of almost perfect reconstruction (PR), that is, the ISI/ICI resulting from the use of filters is maintained below a certain threshold value. The opposite approach is also possible: minimizing the ISI/ICI level at a given energy outside the filter passband. Unfortunately, both problems are not convex, since the restriction of the equality of energy is quadratic, and not affine. In [19], prototype filters were proposed based on non-convex optimization, aimed at providing the specified spectrum characteristics while maintaining high quality character recovery. Based on this method we have design two prototype filters, namely “optimal A”, which is more accurate in reconstruction procedure and “optimal B”, which has higher out-of-band emissions decay. The Fig. 45.1 demonstrates magnitude of frequency response of different PF, including the optimal A and B, which are the result of direct optimization [19]. Figure 45.2 demonstrates corresponding impulse responses.

Fig. 45.1 The magnitudes of amplitude-frequency responses of different PF

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Fig. 45.2 The impulse responses of different PF

Table 45.1 Comparison of the spectral characteristics of pulses corresponding to the studied prototype filters

Sideband level, dB Main lobe bandwidth 99% energy concentration bandwidth Out of band emission (0.105 bandwidth)

Rectangular

RRC

Optimal A

DC

PHYDYAS

Optimal B

−13.3

−24.0

−40

−150

−47.7

−58.8

0.0156

0.0724

0.0683

0.0901

0.0625

0.0781

0.1527

0.1104

0.0460

0.0372

0.0452

0.0494

−32.4

−43.2

−84.1

−150

−81.9

−128.5

Based on Table 45.1, it can be seen that DC has the lowest level of side lobes, but the level of out-of-band emissions is constant and does not change with increasing bandwidth. The optimal B filter has the highest decay rate, and the PHYDYAS and optimal A filters practically coincide in characteristics. The worst spectral characteristics have a rectangular window and a pulse corresponding to the RRC filter.

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405

Simulation Model

Let’s consider the simulation model for estimation of energy characteristics of FBMC signals based on different PF and comparison with those of OFDM signals. Initially, the model data is initialized, such as the number of subcarriers, the number of symbols, the number of signal constellation points, CP length, the channel type, and the filter type. As a channel we consider AWGN and different models of frequency-selective fading channels, such as EPA, EVA and ETU model with doppler frequency. After that, input bits and pilot associated bits are. Pilots are modulated by the same type of modulation as the information subcarriers and are filled in the time-frequency grid according to the DVB-T standard. The data is modulated by QAM modulation and populates the remaining cells in the time-frequency grid. The generated sequence of symbols goes to the input of the OQAM modulator. On the OQAM modulator, the real and imaginary parts of the symbol are separated. In an even symbol, a real signal is transmitted on odd subcarriers, and an imaginary signal is transmitted on even symbols. In an odd character, the process is inverted. Next, zeros are added between each symbol to filter the subcarriers. The number of zeros is determined by the overlap coefficient K. The filter coefficients are nonzero samples of the frequency response of the filter, the impulse response of which was selected based on the specified type of filter. The filtering of subcarriers in the frequency domain can be interpreted as filtering by a shaping filter. Next, the signal passes through the IFFT. The generated symbols overlap in the time domain in such a way that each FBMC symbol act on K − 1 neighboring symbols. After that, the signal passes through the channel similarly to OFDM. The received signal is fed to the input of the FFT. The signal from the FFT output is filtered by an analysis filter, consistent with the prototype filter on the transmitter. Samples from the analysis filter are taken every K samples. The samples from the equalizer are fed to the input of the OQAM demodulator. Next, ZF equalization is performed, similar to OFDM. After that, the signal is demodulated. The resulting sequence is compared with the transmitted one and error counting and calculation of the error probability value for a given Eb/N0 value are carried out (Fig. 45.3).

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Start Conditions initialization

Bit sequence generation

QAM mapper

Pilots generation

QAM mapper

Combining

Fading frequency selective channel Finish

AWGN

FFT

OQAM formation

APF matched filtration

Upsampling

Downsampling

PF Pulse shaping

Deformation OQAM

IFFT

ZF equalization

Overlapping

QAM demapper

BER calculation

Fig. 45.3 Simulation model flowchart of FBMC/OQAM signaling

45.4

Simulation Results

Periodograms of OFDM and FBMC signals for various prototype filters are shown in Fig. 45.4. The construction of the periodogram was carried out by weighing a rectangular window function. The value of the power spectral density was normalized to the maximum value. The number of information subcarriers is 600; the number of guard subcarriers is 424. As can be seen from Fig. 45.4 the level and decay rate of out-of-band emissions from FBMC is significantly lower than that of OFDM. The nature of the decay of the spectrum depends on the choice of the prototype filter and is consistent with the spectral characteristics of the pulses corresponding to the studied prototype filters. Optimal B filter has the best localization of the spectrum. The characteristics of the FBMC spectra with PHYDYAS filters and the optimal A filter almost coincide. For the FBMC spectrum, a DC filter with a given out-of-band emission level also has good localization, but there is no spectrum decline. The worst localization is observed in the RRC filter. The BER curves of OFDM and FBMC signals in the AWGN channel are presented in Fig. 45.5. The calculation was performed for 64 information subcarriers and 32 guard subcarriers. The cyclic prefix length was 1/14 of the symbol length for OFDM. For optimal filters, the same BER performance is observed as for the PHYDYAS filter. The loss relative to OFDM is 2.9 dB in terms of error probability 10−4. The loss of the DC filter is 4.3 dB, and for RRC filter is 4.8 dB loss at the level of 10−4.

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Fig. 45.4 Periodograms for OFDM and FBMC with different types of PF

Fig. 45.5 BER performance for FBMC and OFDM signals in AWGN channel

Figure 45.6, 45.7 and 45.8 demonstrate BER performance in different fading channels for FBMC and OFDM signals. In the EPA channel with zero Doppler frequency, in the case when 7 beams with various attenuations and time delays arrive at the receiver, the loss of FBMC with the optimal A filter relative to OFDM is about 17 dB in for bit error probability 10−4. When a Doppler shift is added to the channel, the situation changes, and OFDM starts to lose relative to the FBMC with the optimal A filter about 5 dB at a level of 10−4. With a further increase in the complication of the channel characteristics, the FBMC and OFDM practically coincide in BER performance. However, in the ETU channel with a Doppler

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(а) ETU with 70 Hz Doppler

(b) ETU with 300 Hz Doppler

Fig. 45.6 Comparison of BER performance for FBMC and OFDM signals in frequency-selective fading channels (ETU) with different doppler shifts

(а) EVA with 5 Hz Doppler

(b) EVA with 70 Hz Doppler

Fig. 45.7 Comparison of BER performance for FBMC and OFDM signals in frequency-selective fading channels (EVA) with different doppler shifts

frequency of 70 Hz and a Doppler frequency of 300 Hz, OFDM fails with the reception, and the curve goes into saturation of the error probability of 10−3 and 10−2, respectively. The optimal A filter has the best BER performance among FBMC signals, while the RRC filter also ceases to cope with the reception and goes into saturation of the 2 * 10−3 error probability in the ETU channel at a Doppler frequency of 70 Hz. The BER performance of FBMC with optimal B and PHYDYAS filters is almost the same, except for the EPA channel with a 5 Hz Doppler shift, where optimal B has a small energy gain of 2 dB relative to PHYDYAS.

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(а) EPA with 5 Hz Doppler

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(b) EPA with 0 Hz Doppler

Fig. 45.8 Comparison of BER performance for FBMC and OFDM signals in frequency-selective fading channels (EPA) with and without doppler frequency shift effect

Fig. 45.9 Comparison of CCDF PAPR estimates for OFDM and different PF FBMC signals

Analysis of Peak-to-Average Power Ration was done by calculation of Complementary Cumulative Distribution Functions, presented on the Fig. 45.9. Here the number of subcarriers in signal equal to 64. As we can see, the minimum possible PAPR for all signals is about 9 dB, and for the level of CCDF equal to 10−3 results differs slightly (no more than 0.2 dB). Therefore, the choice of PF does not affect significantly on the PAPR of the FBMC signal, while the difference between OFDM and FBMC signal is also minor.

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Conclusions

In the paper we have investigated and compared FBMC systems parameters based in different PF application with commonly used OFDM systems. The evaluation of energy and spectral characteristics of FBMC signals with various prototype filters was carried out under different transmission channel conditions. After the analysis of FBMC signals with PF we can see, that there is no universal form of prototype filter, but the approach of direct filter optimization with a focus on a specific problem seems to be more promising, since they could provide higher out-of-band decay rate, which is one of the most important characteristic for the effective frequency resource utilization, especially in fragmented spectrum scenarios. Acknowledgements This research was funded by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020” and used computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (http://www.scc. spbstu.ru).

References 1. E.N. Smirnova, A.S. Ovsyannikova, S.V. Zavjalov, G. Dong, On features of implementation of SEFDM-transmitter with optimal shape of envelope. J. Phys: Conf. Ser. 1236(1), 012067 (2019) 2. E.N. Smirnova, Experimental research of receiver based on SEFDM-signals with optimal envelopes. J. Phys: Conf. Ser. 1326(1), 012031 (2019). https://doi.org/10.1088/1742-6596/ 1326/1/012031 3. D.C. Nguyen, S.V. Zavjalov, A.S. Ovsyannikova, C.M. Nguyen, Improving the effectiveness of the multi-frequency signals application under conditions of amplitude limitation. J. Phys: Conf. Ser. 1326(1), 012028 (2019). https://doi.org/10.1088/1742-6596/1326/1/012028 4. D.C. Nguyen, S.V. Zavjalov, A.S. Ovsyannikova, C.M. Nguyen, The effectiveness of tone reservation method for peak-to-average power ratio reduction of SEFDM signals with optimal envelopes, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, 2019), pp. 165–168. https://doi.org/10.1109/eexpolytech.2019. 8906819 5. A. Gelgor, T. Gelgor, New pulse shapes for partial response signaling to outperform faster-than-Nyquist signaling, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, 2019), pp. 144–148 6. A. Rashich, S. Gorbunov, Computational complexity analysis of SEFDM time and frequency domain equalizers, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, October 2019), pp. 94–97. https://doi.org/10.1109/ eexpolytech.2019.8906828 7. A. Rashich, S. Gorbunov, ZF equalizer and trellis demodulator receiver for SEFDM in fading channels, in 2019 26th International Conference on Telecommunications (ICT) (IEEE, April 2019), pp. 300–303. https://doi.org/10.1109/ict.2019.8798843 8. I. Darwazeh, H. Ghannam, T. Xu, The first 15 years of SEFDM: a brief survey, in 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) (IEEE, July 2018), pp. 1–7. https://doi.org/10.1109/csndsp.2018.8471886 9. S. Sugiura, Frequency-domain equalization of faster-than-Nyquist signaling. IEEE Wirel. Commun. Lett. 2(5), 555–558 (2013). https://doi.org/10.1109/CSNDSP.2018.8471886

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10. R. Nissel, S. Schwarz, M. Rupp, Filter bank multicarrier modulation schemes for future mobile communications. IEEE J. Sel. Areas Commun. 35(8), 1768–1782 (2017). https://doi. org/10.1109/JSAC.2017.2710022 11. S. Jang, D. Na, K. Choi, Comprehensive performance comparison between OFDM-based and FBMC-based uplink systems, in 2020 International Conference on Information Networking (ICOIN) (IEEE, January 2020, pp. 288–292. https://doi.org/10.1109/icoin48656.2020. 9016425 12. R. Zakaria, D. Le Ruyet, A novel filter-bank multicarrier scheme to mitigate the intrinsic interference: application to MIMO systems. IEEE Trans. Wirel. Commun. 11(3), 1112–1123 (2012). https://doi.org/10.1109/TWC.2012.012412.110607 13. H. Saeedi-Sourck, Y. Wu, J.W. Bergmans, S. Sadri, B. Farhang-Boroujeny, Complexity and performance comparison of filter bank multicarrier and OFDM in uplink of multicarrier multiple access networks. IEEE Trans. Signal Process. 59(4), 1907–1912 (2011). https://doi. org/10.1109/WCL.2013.072313.130408 14. F. Schaich, T. Wild, Waveform contenders for 5G—OFDM vs. FBMC vs. UFMC, in 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP) (IEEE, May 2014), pp. 457–460. https://doi.org/10.1109/isccsp.2014.6877912 15. B. Farhang-Boroujeny, OFDM versus filter bank multicarrier. IEEE Signal Process. Mag. 28 (3), 92–112 (2011). https://doi.org/10.1109/MSP.2011.940267 16. S. Mirabbasi, K. Martin, Overlapped complex-modulated transmultiplexer filters with simplified design and superior stopbands. IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process. 50(8), 456–469 (2003). https://doi.org/10.1109/TCSII.2003.813592 17. D. Chen, D. Qu, T. Jiang, Y. He, Prototype filter optimization to minimize stopband energy with NPR constraint for filter bank multicarrier modulation systems. IEEE Trans. Signal Process. 61(1), 159–169 (2012). https://doi.org/10.1109/TSP.2012.2222397 18. A. Sahin, I. Guvenc, H. Arslan, A survey on multicarrier communications: prototype filters, lattice structures, and implementation aspects. IEEE Commun. Surv. Tutor. 16(3), 1312–1338 (2013). https://doi.org/10.1109/SURV.2013.121213.00263 19. R.T. Kobayashi, T. Abrão, FBMC prototype filter design via convex optimization. IEEE Trans. Veh. Technol. 68(1), 393–404 (2018) 20. P. Siohan, C. Siclet, N. Lacaille, Analysis and design of OFDM/OQAM systems based on filterbank theory. IEEE Trans. Signal Process. 50(5), 1170–1183 (2002). https://doi.org/10. 1109/78.995073 21. A. Viholainen, M. Bellanger, M. Huchard, Prototype filter and structure optimization, ICT-211887 Project PHYDYAS (Physical Layer for Dynamic Access and Cognitive Radio) Technical report D5. 1 (2009)

Chapter 46

Multiple Object Tracking Using Convolutional Neural Network on Aerial Imagery Sequences Sergey B. Makarov , Vitalii A. Pavlov , Andrei K. Bezborodov , Aleksey I. Bobrovskiy , and Dong Ge Abstract In this paper, an application of the neural network algorithm YOLO v2 for detecting and tracking a group of moving objects of complex shape on aerial imagery sequences is presented. The Kuhn – Munkres algorithm was used to set up a one-to-one correspondence between moving objects from frame to frame. The matrix elements in the algorithm are the Euclidean distances between the bounding boxes on two consecutive frames. It is proposed to use a comparison of color histograms of images of objects to handle situations with the disappearance and appearance of objects in the field of view of the camera. To compare the color histograms the Bhattacharya distance metric was selected. The proposed approach showed high results compared to the IOU Tracker algorithm.










Keywords Detection Identification Tracking Convolutional neural network Aerial photography

46.1




Introduction

Currently, computer vision systems are actively used in various fields: space [1, 2], medicine [3–5], industry [6], etc. One of the urgent tasks of computer vision is the development of algorithms for detecting and tracking one or more objects in the video sequence. The use of human resources for such tasks is extremely inefficient and expensive, therefore, various computer vision algorithms have found applications in such areas [7].

S. B. Makarov
V. A. Pavlov (&)
A. K. Bezborodov Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia e-mail: [email protected] A. I. Bobrovskiy JSC Television Research Institute, Saint Petersburg, Russia D. Ge Tsinghua University, Haidian District, Beijing, People’s Republic of China © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_46

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For most modern algorithms, the task of detecting, and tracking group objects of complex shape in image sequences can be divided into several subtasks that require step-by-step execution. Initially, we need to detect all moving objects on every frame of the input video sequence, and then it is necessary to compare the accompanied objects with those found in the current frame. The use of several independent algorithms [8–12] for multiple object tracking has several disadvantages. With an increase in the number of monitored objects, the computational load on the hardware platform increases, which significantly reduces the speed of work. Such approaches are not applicable for real-time operation in onboard television systems. Also, in single tracking algorithms, it is not possible to select an object for initialization [12]. For this, manual selection is used, which leads to significant time costs. The task of tracking multiple objects can be divided into two types [13]: detection free tracking and detection-based tracking. The first class of approaches assumes that nothing is known about the objects being followed, except for their position in the frame, which is determined by the bounding box. Detection-based tracking means that every object in a video is detected. The appearances of the same objects are interconnected. So, the trajectories of moving objects are formed. Such methods are the most popular in solving problems of tracking multiple objects, however, they strongly depend on the performance of the used object detection algorithm. Depending on the shooting scenario, the methods of automatic detection of objects can be divided into two categories: the camera and the background are motionless; the camera position changes with dynamic background change. In the first case, the task is reduced to the separation of the moving parts of the image from a fixed background. After the foreground selection procedure, the segmentation of moving areas at the same speed is performed. Segmentation results in bounding boxes that contain the selected objects. There are 3 groups of methods: background subtraction methods, probabilistic methods, optical flow methods. A common drawback of these algorithms is their correct operation under static shooting conditions, so the use of these approaches for aerial photography is impossible. The camera movement creates many difficulties. As was shown in [7], under the conditions of aerial photography, neural network approaches in the problems of detecting multiple objects demonstrate the best results for frame-by-frame processing of a video sequence [23]. For every frame processing of video sequences, 2 groups of methods can be distinguished: two-stage and one-stage. In two-step methods, the image is divided into areas of interest. A set of features is extracted from each area, which is then fed to the input of the classifier to identify objects in the feature space. In classical approaches, such as the Viola-Jones [14] and Dalal-Triggs [15] detector, the sliding window approach is used to form areas of interest.

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In R-CNN [16] and Fast R-CNN [17], selective search [18] is used instead of the sliding window approach, and a convolutional neural network extracts features. In Faster R-CNN [19], authors use a pre-trained neural network to form potential bounding boxes. In [20], a method for tracking several objects for unmanned vehicles was proposed, in which the approaches Faster R-CNN and GOTURN are used for detection and tracking. In the second [21–23] class of methods for detecting objects, the image is fed to the input of the convolutional neural network as a whole. The result of the work is the coordinates of the bounding boxes and the probability that the objects belong to certain classes. Thus, most of the currently existing algorithms show their effectiveness in the case of the stationary position of the camera and background, which cannot be ensured in aerial photography. Detection free tracking using single-stage methods demonstrates the best results in tasks of tracking many objects, but this type of method needs additional processing of various problem scenarios. This work aims to develop an algorithm for detecting, recognizing, and tracking one or more moving objects in aerial photography. As was shown, these conditions create additional difficulties due to the complex background-target situation, the frequent disappearances of objects, and blurry [24] caused by a sharp turn of the camera.

46.2

Description of the Algorithm

The block diagram of the proposed algorithm is presented in Fig. 46.1. To detect objects in this work, we use the YOLO v2 convolutional neural network [23], trained to detect and recognize 6 classes of objects: cars, bulky vehicles, buildings, planes, helicopters, and ships. The meanAP (mAP) value on the labeled test sample (1504 images (15800 objects)) was 76.14% [7]. A detailed description of the process and learning outcomes is given in [7]. Fig. 46.1 The block diagram of the algorithm

Frame

Detection and recognition No Initialization Yes

Memory unit

Matching Assigning / updating unique identifiers

Result

Trajectory correction

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Let us consider in detail the tracking algorithm shown in Fig. 46.1. If the current frame is the first one on which objects are present, then each of them is assigned its unique identifier. The algorithm skips the frame if YOLO v2 detected no objects. If this frame is not the first one and initialization was successful, it is necessary to set up a one-to-one correspondence between the detected and tracked objects using the Kuhn – Munkres optimization algorithm [25, 26] and, if necessary, calculating histograms of the found objects. The Kuhn – Munkres algorithm solves the assignment problem by reducing the matrix of penalty coefficients to the optimal form. The rows of the matrix show the objects being tracked; the columns display the detected objects. The matrix elements are the proximity values (Euclidean distance [28]) of the bounding box of the detected objects and known from the previous frame. The result of the Kuhn – Munkres algorithm is shown schematically in Fig. 46.2. If one of the objects leaves the frame and then reappears in it, the Kuhn – Munkres algorithm will assign a new unique number to such an object, which gives false information about the object. In this regard, two situations require more processing: 1. The number of objects in the current frame has decreased. 2. The number of objects in the current frame has increased. If the number of moving objects has become smaller, we should save a histogram of the object bounding box that leaves the camera field of view, as well as its unique number. When the number of objects in a new frame increases, provided that no object has previously left the frame, it is enough to assign a new unique number to the detected object. The most difficult situation is when it is necessary to decide whether a new object has already disappeared from the frame earlier. To determine this, it is necessary to compare the histograms of objects that disappeared earlier with the histogram of the detected object using the proper metrics.

Fig. 46.2 The result of matching objects using the Kuhn – Munkres algorithm

ID 1

ID 3

ID 2

Frame n Tracked objects Detected objects

ID 4

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To compare the color histograms of objects in the current frame with the histograms of objects that left the camera’s field of view, the Bhattacharya distance [28] metric was selected. This metric showed the best results when comparing histograms of bounding boxes in which objects are small and low in detail since shooting is carried out from a big height [27]. The histograms of the disappeared objects are being saved during the time the algorithm works. If the numerical result of comparing the histograms using this metric turned out to be below a certain threshold value, we assume that the object that left the frame earlier returned to the camera’s field of view.

46.3

Results

To evaluate the results of the experiments, we selected the following metrics that describe how correctly the unique object numbers are determined: Precision [28] and Recall [28]. At the stage of marking test video sequences, each object was assigned its unique identifier, regardless of class. The identifiers assignment was carried out in ascending coordinates order of the object center position in the frame. Moreover, each object of a certain class had a unique identifier. The tracking algorithm used the same principle of assigning unique identifiers during initialization and operation. To assess the accuracy of objects localization, we selected the following metrics: Jacquard index [28]; absolute localization error (Δ), which is defined as the Euclidean distance between the centers of the ground truth and predicted bounding boxes. To test the efficiency of the algorithm, we selected 10 video sequences [28], which characterize various problem scenarios (camera rotation, occlusion, the intersection of trajectories, etc.). The test results of the proposed tracking algorithm for a group of moving objects are shown in Table 46.1. The comparison was made with the IOU Tracker algorithm [29]. The Jacquard index is used in the IOU Tracker algorithm to establish a one-to-one correspondence between moving objects from frame to frame [29]. A convolutional neural network YOLO v2 from this work is used to detect objects. Figure 46.3 demonstrates frames from the video sequences 1, 6, 10, which show how the algorithm operates.

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Table 46.1 Results Video 1 2 3 4 5 6 7 8 9 10 Average

Precision OurT IOUT

Recall OurT

IOUT

Jacquard index OurT IOUT

Δ, pixel OurT IOUT

0.997 0.996 0.996 0.994 0.945 0.917 0.999 0.853 0.941 0.983 0.962

0.934 0.971 0.946 0.989 0.862 0.944 0.931 0.877 0.992 0.994 0.944

0.489 0.383 0.478 0.468 0.469 0.339 0.474 0.382 0.221 0.407 0.401

0.75 0.7 0.72 0.7 0.73 0.63 0.79 0.67 0.84 0.8 0.73

3.3 4.8 4.0 4.2 5.0 4.4 4.6 7.5 6.0 3.3 4.7

0.986 0.978 0.971 0.979 0.984 0.901 0.923 0.944 0.948 0.957 0.957

0.75 0.72 0.72 0.7 0.73 0.61 0.8 0.67 0.84 0.62 0.72

3.3 4.2 4.0 4.6 5.0 7.7 4.6 7.3 5.2 0.6 5.2

Fig. 46.3 Examples of frames from test video sequences demonstrating the algorithm operation: a, b - video 1, frames 130 (a) and 325 (b); c, d - video 6, frames 22 (c) and 215 (d)

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46.4

419

Conclusion

In this paper, we proposed an algorithm for the detection, recognition, and tracking of one or several objects on a sequence of aerial photographs in a complex background-target environment based on the convolutional neural network YOLO v2. The Kuhn – Munkres algorithm is used to set up a one-to-one correspondence between moving objects from frame to frame. As the matrix elements in this algorithm, the Euclidean distance between the centers of the detected objects on two consecutive frames was selected. It is proposed to compare objects color histograms in the current frame with objects histograms that disappeared from previous frames to deal with occlusions and to determine the identifiers of detected objects that previously left the camera’s field of view. The Bhattacharya distance was chosen as the color histogram comparison metric. This metric has shown the best results when comparing the color histograms of small objects. The proposed approach showed high results compared to the IOU Tracker algorithm, which does not cope with determining the identifiers of objects in such complex situations. Acknowledgments This research was funded by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020” and used computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (http://www.scc. spbstu.ru).

References 1. A.A. Andryakov, Image filtering for the nanosatellite vision system. J. Phys: Conf. Ser. 1326(1), 1–7 (2019) 2. I. Fomin, S. Orlova, D. Gromoshinskii, A. Bakhshiev, Object detection on docking images with deep convolutional network. Stud. Comput. Intell. 799, 136–143 (2018) 3. M. Kots, V. Chukanov, U-Net adaptation for multiple instance learning. J. Phys: Conf. Ser. 1236, 1–6 (2019) 4. M.A. Baranov, E.N. Velichko, A.A. Andryakov, Image processing for analysis of bio-liquid films. Optical Memory Neural Netw. 29(1), 1–6 (2016) 5. O.B. Kuznetsova, E.A. Savchenko, A.A. Andryakov, E.Y. Savchenko, Z.A. Musakulova, Image processing in total internal reflection fluorescence microscopy. J. Phys: Conf. Ser. 1236(1), 1–6 (2019) 6. N. Bakir, V. Pavlov, S. Zavjalov, S. Volvenko, A. Gumenyuk, M. Rethmeier, Development of a novel optical measurement technique to investigate the hot cracking susceptibility during laser beam welding. Welding World 63, 435–441 (2019) 7. V.A. Pavlov, M.A. Galeeva, Detection and recognition of objects on aerial photographs using convolutional neural networks. J. Phys: Conf. Ser. 1326, 1–6 (2019) 8. G. Nebehay, R. Pflugfelder, Consensus-based matching and tracking of keypoints for object tracking. in IEEE Winter Conference on Applications of Computer Vision, pp. 862–869. Steamboat Springs, CO (2014) 9. S. Hare et al., Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016) 10. J.F. Henriques, R. Caseiro, P. Martins, J. Batista, High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

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11. Z. Kalal, K. Mikolajczyk, J. Matas, Tracking-Learning-Detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012) 12. M. Kristan, et al. The seventh visual object tracking VOT2019 challenge results. in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 2206– 2241. Seoul, Korea (South) (2019) 13. W. Luo, J. Xing, X. Zhang, X. Zhao, T. Kim Multiple object tracking: A literature review. arXiv preprint arXiv:1409.7618, pp 1–18 (2014) 14. P. Viola, M.J. Jone Rapid object detection using a boosted cascade of simple features. in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), pp. 511–518. Kauai, HI, USA (2001) 15. N. Dalal, B. Triggs Histograms of oriented gradients for human detection. in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. San Diego, CA, USA (2005) 16. R. Girshick, J. Donahue, T. Darrell, J. Malik Rich feature hierarchies for accurate object detection and semantic segmentation. in 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. Columbus, OH (2014) 17. R. Girshick Fast R-CNN. in 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448. Santiago (2015) 18. J.R. Uijlings, V. Sande, T. Gevers, A.W. Smeulders, Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013) 19. S. Ren, K. He, R. Girshick, J. Sun, Faster R—CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017) 20. A. Agarwal, S. Suryavanshi Real-Time Multiple Object Tracking (MOT) for Autonomous Navigation. Technical Report, pp. 1–5 (2017) 21. J. Redmon, S. Divvala, R. Girshick, A. Farhadi You only look once: unified, real-time object detection. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788. Las Vegas, NV (2016) 22. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed SSD: single shot multibox detector. in European Conference on Computer Vision (ECCV) 2016: Computer Vision – ECCV, vol. 9905, pp 21–37. Amsterdam, The Netherlands, (2016) 23. J. Redmon, A. Farhadi YOLO9000: Better, Faster, Stronger. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525. Honolulu, HI (2017) 24. A. Korobeynikov, A. Grishentsev, E. Velichko, C. Korikov, S. Aleksanin, M. Fedosovskii, I. Bondarenko, Calculation of regularization parameter in the problem of blur removal in digital image optical memory and neural networks. Inf. Optics 3, 184–191 (2016) 25. H. Kuhn, The hungarian method for the assignment problem. Naval Res. Logist. Quarterly 2, 83–97 (1955) 26. J. Munkres, Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957) 27. R. Schowengerdt, Remote Sensing Models and Methods for Image Processing, 3rd edn. (Academic Press, USA, 2006) 28. R. Klette, Concise Computer Vision. An Introduction into Theory and Algorithms (Springer, London, 2014) 29. E. Bochinski, V. Eiselein, T. Sikora High-speed tracking-by-detection without using image information. in 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. Lecce, Italy (2017)

Chapter 47

Application of a Convolutional Neural Network for Detection of Ignition Sources and Smoke Ilya R. Aliev , Vitalii A. Pavlov , Sergey V. Zavjalov , and Yekaterina Sadovaya Abstract The article discusses various methods for detecting ignition sources on aerial photographs. An algorithm based on color filtering and biorthogonal wavelet transform and the Tiny-YOLOv3 convolutional neural network were chosen for research. For the study, training and test datasets were developed. According to the results of experiments, Tiny-YOLOv3 exceeded the algorithm based on color filtering and biorthogonal wavelet transform in detection accuracy. For image processing algorithm AP was 16%. For the Tiny-YOLOv3 with input layer size of 416  416, the detection accuracy (AP) of fire and smoke was 56.5% and 31.9%, respectively. Keywords Computer vision Tiny-YOLOv3

47.1


Detection
Convolutional neural networks


Introduction

Application of computer vision algorithms allows minimizing the disadvantages of using traditional fire detectors based on the use of various sensors. Algorithms for monitoring the area using aerial photographs can search for ignition source in a vast territory [1]. Thus, computer vision algorithms allow to place the camera which monitors the area, away from the subject. The algorithm must satisfy the requirements necessary for working in real-time on a mobile platform.

I. R. Aliev
V. A. Pavlov (&)
S. V. Zavjalov Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia e-mail: [email protected] Y. Sadovaya Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_47

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47.1.1 Image Processing Methods At first, we consider modern fire detection algorithms, based on image processing. For flame segmentation, region growing algorithm [2] can be used. It uses information about the color distribution of the flame, combining areas with the same color tone of the pixels. When monitoring large open areas, it is also necessary to observe the appearance of smoke. Smoke can be detected by the camera, even if no flame is visible. To extract the signs of the texture of the smoke, LBP (Local Binary Pattern) can be used [2]. This contributes to earlier detection of the fire before it spreads around. However, to perform fire detection using image processing algorithms, you must have knowledge about the features of smoke and fire.

47.1.2 Neural Networks Nowadays neural networks can perform tasks assigned to computer vision [4–7], including the detection of ignition sources and smoke [3]. During the learning process, a search of optimal weight coefficient values is applied, thus, the dependencies of the result on the set of input features are established. For example, authors in [1] propose the idea of applying two types of UAVs for fire detection at two altitude ranges. In article [8] a convolutional neural network was trained on a set of aerial photographs for 6 classes. A comparison of the efficiency of modern neural network algorithms for detecting flame and smoke is given in [9]. The operating speed of convolutional neural networks on a mobile platform depending on the resolution of the input image is presented in the study [10]. Currently, there are many implemented detectors which allow localizing and classification objects with high accuracy and close to real-time operation speed, such as: YOLOv3 [14], SSD [15] and others [16, 17].

47.2

Implementation

47.2.1 Image Processing The main existing fire detection methods include the following image processing algorithms: • filtering by brightness and color tone [2]; • wavelet transform [12, 13]; • estimation of pixel displacement between frames using an optical flow [11].

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Wavelet transform method allows to find the contours of objects which are defined as local extrema in the image with analyzing areas with changes in brightness. The described above methods for detecting fires using digital image processing were implemented in the Python programming language using the OpenCV library of computer vision algorithms. To perform biorthogonal wavelet transform, was used the function bior1.3 from the PyWavelets library [21].

47.2.2 Convolutional Neural Networks The focus of the study was chosen to detect fires using a convolutional neural network. A significant difference between the convolutional neural network YOLOv3 relative to earlier versions (YOLO [18] and YOLOv2 [19]) is the ability to make predictions on three scales. This allows detecting objects with different sizes with higher accuracy. Thus, using this network, a fire can be detected regardless of its size and the height from which the survey is made. Since the detector should be implemented on a mobile platform in real time, the speed of computing operations should be high. To achieve these goals, a convolutional neural network Tiny-YOLOv3 was used; it has a simplified network architecture compared to YOLOv3 and, as a result, a higher speed of operation. The training data set was formed based on 44 videos, of which 1650 frames were obtained with images of fires shot from the air under various conditions (5235 examples of fire, smoke - 2673). To implement detection on different sizes of feature maps, there are up-sampling layers, which increase tensor dimension and concatenation of feature maps. The network consists of 13 convolutional layers (4 of them have a size of 1  1, stride 1 and 9 with size 3  3, stride 1), also 6 layers of Max-pooling are applied. In the network there are 23 layers. Thus, the detection is carried out on the 16th and 23rd layers, this allows to detect objects of small sizes with higher accuracy. Network predicts 3 bounding boxes for each grid cell, so number of anchors is also 3. The tensor depth at each of the three scales is 21, because the number of classes is two.

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Detection and localization of ignition sources and smoke using a convolutional neural network Tiny-YOLOv3 was implemented using neural network framework Darknet [22] and CUDA. The use of this architecture allows us to gain high speed of work on a GPU.

47.3

Results

47.3.1 Wavelet-Transform We apply brightness and color tone filtering to the original image in the HSV color model, evaluate the movement of pixels between frames using the optical stream, and apply wavelet transform. Images obtained as a result of wavelet transform contain information on high-frequency vertical components of the original image (Fig. 47.1 b.). In Fig. 47.1, a it is shown the low-frequency component, the output of wavelet transform [13]. To highlight the result, we enclose the resulting regions in rectangular frames (Fig. 47.1 c.). Performance assessment of the described algorithm was made. The speed turned out to be low (2 frames per second when processing videos with a resolution of 1280  720), and plenty of false positives were also noticed. This is confirmed by the results of fire detection using the AP metric [20] in Table 47.1 for various IoU values [20].

Fig. 47.1 The result of wavelet analysis. a) filtered low-frequency image, b) high-frequency vertical components, c) the result of localization of the ignition source on the original image

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Table 47.1 Accuracy of the image processing algorithm

IoU

APfire, %

0.2 0.3 0.4 0.5

15.1 14.4 13.4 11.6

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47.3.2 Convolutional Neural Network Tiny-YOLOv3 As a result, network predicts 7 values for each anchor box in a grid cell, 4 of them correspond to the size and position of an object, 2 class probabilities and confidence score. Thus, areas that contain smoke or fire with some probabilities are enclosed in bounding rectangles corresponding to the predicted color class. Let us present the obtained results in Fig. 47.2a–Fig. 47.2d. Accuracy results were obtained using the AP metric, which describe the accuracy of the neural network performance in test images during detection with the sizes of the input layer of the network 608  608, 416  416 and 320  320. Estimation of the detector speed was made for the GPU – GeForce GTX 1050 Ti with 768 CUDA cores in Table 47.2.

Fig. 47.2 The results of convolutional neural network Tiny-YOLOv3 in test images

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Table 47.2 The accuracy and speed of Tiny-YOLOv3 for different sizes of the input layer (IoU = 0.4, confidence = 0.4)

47.4

Size of input layer

APfire, %

APsmoke, %

mAP, %

fps

320  320 416  416 608  608

37 56.5 61.5

18.5 31.9 40.2

27.8 44.2 50.8

151 111 62.5

Conclusion

Various methods for detecting and localizing fires on aerial photographs were considered. At first, an algorithm based on color filtering and the use of wavelet transform was chosen for implementation. Detection accuracy was 16%, which indicates the possibility of false positives in a complex background-target environment. Also, the performance of the convolutional neural network Tiny-YOLOv3 was evaluated. The network structure allows performing detection of small objects while maintaining low computational complexity. According to the results of experiments, Tiny-YOLOv3 exceeded the algorithm based on color filtering and biorthogonal wavelet transform in detection accuracy. That convolutional neural network demonstrated high accuracy for the fire class (56.5%). Low accuracy values for the smoke class (31.9%) may be due to the insufficient number of examples of this class in the training dataset. High average precision (more than 80%) can be reached with more than 5000 training examples of objects and when camera is stationary, which is shown in other researches [3, 9]. The resulting high speed of operation on a GPU with 768 CUDA cores (111 fps for input layer 416  416) indicates the possibility of using Tiny-YOLOv3 in real time on mobile platforms Jetson Xavier with 512 cores and Jetson TX2, which has 256 CUDA cores [23]. Acknowledgments This research was funded by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020” and used computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (http://www.scc. spbstu.ru).

References 1. G. Hristov, J. Raychev, D. Kinaneva, Emerging methods for early detection of forest fires using unmanned aerial vehicles and lorawan sensor networks. in 2018 28th EAEEIE Annual Conference (EAEEIE) (2018), pp. 1–9 2. L. Jie, X. Jiang, Forest fire detection based on video multi-feature fusion. in 2nd IEEE International Conference on Computer Science and Information Technology, Beijing (2009), pp. 19–22

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3. Y. Chen, Y. Zhang, X. Jing, G. Wang, L. Mu, Y. Yi, H. Liu, D. Liu, UAV image-based forest fire detection approach using convolutional neural network. in 2019 14th IEEE Conference on Industrial Electronics and Applications, ICIEA (2019), pp. 2118–2123 4. A. Korobeynikov, A. Grishentsev, E. Velichko, C. Korikov, S. Aleksanin, M. Fedosovskii, I. Bondarenko, Calculation of regularization parameter in the problem of blur removal in digital image. Optical Memory Neural Netw. 25, 184–191 (2016) 5. M.A. Baranov, E.N. Velichko, A.A. Andryakov, Image processing for analysis of bio-liquid films. Optical Memory Neural Netw. 29(1), 1–6 (2020) 6. A. Fomin, Object detection on docking images with deep convolutional network. in: Advances in Neural Computation, Machine Learning, and Cognitive Research II, pp. 136– 143. (2019) 7. O.B. Kuznetsova, E.A. Savchenko, A.A. Andryakov, E.Y. Savchenko, Z.A. Musakulova, Image processing in total internal reflection fluorescence microscopy. J. Phys: Conf. Ser. 1236 (1), 1–6 (2019) 8. V. Pavlov, M. Galeeva, Detection and recognition of objects on aerial photographs using convolutional neural networks. J. Phys. Conf. Ser. 1326, 62–85 (2019) 9. S. Wu, L. Zhang, Using popular object detection methods for real time forest fire detection. in 2018 11th International Symposium on Computational Intelligence and Design (ISCID), (Hangzhou, China 2018), pp. 280–284 10. Z. Jiao, A deep learning based forest fire detection approach using UAV and YOLOv3. in 2019 1st International Conference on Industrial Artificial Intelligence (IAI), (Shenyang, China 2019), pp. 1–5 11. J. Huang, W. Zou, Z. Zhu, Optical flow based realtime moving object detection in unconstrained scenes (2018) 12. B.U. Töreyin, Y. Dedeoğlu, A.E. Çetin, Wavelet based real-time smoke detection in video. in 2005 13th European Signal Processing Conference, Antalya (2005), pp. 1–4 13. A.A. Andryakov, Image filtering for the nanosatellite vision system. J. Phys: Conf. Ser. 1326 (1), 1–7 (2019) 14. J. Redmon, A. Farhadi Yolov3: An incremental improvement (2018) 15. L. Wei, SSD: Single Shot MultiBox Detector. Lecture Notes in Computer Science, pp. 21–37 (2016) 16. L. Utkin, An imprecise extension of SVM-based machine learning models. Neurocomputing 331, 18–32 (2019) 17. G. Lindsay Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020) 18. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Las Vegas, NV 2016), pp. 779–788 19. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Honolulu, HI 2017), pp. 6517–6525 20. M. Everingham, G.L. Van, C. Williams, J. Winn, A. Zisserman, The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 303–338 (2010) 21. PyWavelets. Wavelet transforms library for Python. http://wavelets.pybytes.com/wavelet/ bior1.3/ 22. Redmon, J. Darknet: Open source neural networks in c. http://pjreddie.com/darknet/ (2016) 23. NVIDIA Corporation. https://www.nvidia.com/ru-ru/autonomous-machines/jetson-store/

Chapter 48

ROM-Based Encoder with Bubble Error Correction Mikhail A. Bellavin

and Dmitry O. Budanov

Abstract Thermometer-to-Binary encoder is an essential part of flash ADC, which significantly defines its basic parameters, such as conversion rate and effective number of bits (ENOB). Losses in ENOB can arise due to many reasons. One of these reasons is the occurrence of bubble errors, which are conditioned by thermometer code non-monotonicity. Bubble errors may cause an incorrect result of analog-to-digital conversion, which may increase the total noise of ADC, and leads to ENOB reduction. In this paper a novel approach, which considers the ADC input signal edge direction for bubble error correction is proposed. Also a bubble error correction circuit based on this approach for modified ROM-based encoder is designed and simulated.




Keywords Encoder Thermometer code error Correction circuit




48.1


Binary code
ROM-encoder
Bubble

Introduction

Thermometer-to-binary encoder is an essential part of flash analog-to digital converter (ADC). This type of ADC is widely used in modern high-performance digital systems due to its highest conversion rate. Flash ADC are typically used in applications, which require a very high rate of analog-to digital conversion (more than 1GS/s) [1]. ADCs of this type include analog part (resistor ladder and comparator array) and digital part (thermometer-to-binary encoder). Analog part converts an input analog signal into a discrete code [2]. This code is called thermometer code, in which the current input voltage level corresponds to the placement of transition between logic ones and zeroes in this code. Typically, logic zeroes are placed above the 1-0 transition and logic ones are placed below it. When one or several zeroes take place below the 1-0 transition, it is considered, that the M. A. Bellavin (&)
D. O. Budanov Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_48

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thermometer code monotonicity is broken and the bubble has occurred [3]. The reasons of bubble error occurrence are the difference of the comparators response times and the clock jitter [3]. The digital part of flash ADC is represented by thermometer-to-binary encoder, which converts thermometer code into the binary one. The effective number of bits (ENOB) is an important characteristic of ADC quality. This characteristic means the resolution of the ideal ADC if its total noise would be equal to the total noise of the real ADC being considered [4]. As well as circuit mismatches, conversion of the combinations with bubble errors reduces ENOB of analog-to-digital converter, because these combinations may be converted incorrectly and the total noise of ADC may increase [1]. To decrease the comparator level deviation for increasing the ENOB of ADC, and, as following, for reducing the appearance of bubble errors on the comparator array outputs, on the stage of comparator array the following methods may be applied: • Increasing the size of transistors in the comparator circuit [5]. • Using interpolation architecture [6, 7]. • Using the redundant comparator array and the calibration circuit [8–10]. To avoid a conversion of code combination with bubble error, which result may be incorrect, on the stage of thermometer-to-binary encoder different correction circuits are applied in common with it. These circuits may be conditionally classified as follows: • • • • •

Based on n-input AND gate array [11]. Using bit-swap logic to provide the bubble error correction [12]. Based on standard logic gates (AND, OR) [13]. Using Gray code [13]. Using Differential Cascade Voltage Switch Logic (DCVSL) [14–16].

To evaluate the complexity of correction circuits, the number of its constituent transistors is presented in Table 48.1. These results are summarized for the 5 bit encoder case and an implementation of logic gates (AND, OR) in CMOS logic. Table 48.1 Bubble error correction circuits complexity Type of correction circuit

Number of constituent transistors

Based on n-input AND gate array (n ¼ 3Þ Using bit-swap logic Based on standard logic gates (AND, OR) (the first type error correction) The proposed correction circuit

352 360 186 416

48

ROM-Based Encoder with Bubble Error Correction

431

In devices using Gray code and DCVSL for providing bubble error correction, the stage of bubble error correction is not presented explicitly, so it is impossible to evaluate the number of its constituent transistors. Despite its lower complexity, the known circuits for bubble error correction have one significant disadvantage: they provide bubble error correction without taking into account the input signal edge direction, so the approach reported in Sect. 48.3.1 may not be realized. This work is organized as follows: part 2 will present the main facts about the encoder in question and the process of converting bubble errors; part 3 will describe the process of designing the correction circuit; part 4 will report the result of simulation of the correction scheme and draw conclusions in part 5.

48.2

Bubble Error Conversion Process

48.2.1 Encoder Architecture Encoder, which is presented in this paper, uses the input bit numbers and is based on a

b logic element. The least significant bit of the encoder is computed as a result of NAND logic operation on two bits (modified ROM-encoder, Fig. 48.1). The proposed encoder circuit is the modification of the basic ROM-encoder. It has the similar conversion algorithm, based on two stages. During the first stage the intermediate code is being generated (signals Zk on Fig. 48.1). This code contains some logic ones, which number is the same, that the number of logic ones in output code combination. During the second stage these logic signals open single transistors in the transistor array and the output binary combination is being generated [1]. Due to its architecture features, such as single transistor selection and interconnections between a

b logic gates, the modified ROM-encoder has enhanced performance and increased bubble-tolerance due to reduction the number of its constituent transistors and more perfect structure of the transistor array [1].

432

M. A. Bellavin and D. O. Budanov Vdd

X5 X4 X3

Z5

Z7

B1

Y1

Z5

B0-2 B0-1

Y0

B0-1

Z6

Y2

B0-2

X6

B2

B1

Z7

B2

X7

Z6 Z4 Z3

Z4 Z3

X2

Z2

Z2

Z1

X1

Z1

Fig. 48.1 Modified ROM-based 3 bit encoder [1]

Table 48.2 Bubble error type examples Error type

Correct code combination

Error combination examples

Designation

1

111111

2

111111

3

111111

101111 111011 100111 110101 100011 110001

BE(5,6,1) BE(5,6,3) BE(5,6,1,2) BE(5,6,2,4) BE(5,6,1,2,3) BE(5,6,2,3,4)

48.2.2 Bubble Error Classification For bubble error description in paper [1] was proposed the following designation: BE ðN; K; Pos1; . . .; PosN Þ;

ð48:1Þ

where BE is the abbreviation for Bubble Error, N—encoder resolution in bits, K— number of the most significant non-zero bit in the thermometer code, Pos1; . . .; PosN—positions of logic zeroes. These positions should be counted from the most significant thermometer code bit, which is equal to logic one (the number of this bit is considered as zero) [1].

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ROM-Based Encoder with Bubble Error Correction

433

The number of logic zeroes below the 1-0 transition defines a bubble error type. Examples of bubble-error types and its designations are presented in Table 48.2 [1]. It should be noted that bubble errors may manifest itself not only by logic zeroes in the set of logic ones, but also the other way around, by logic ones in the set of logic zeroes (see Sect. 48.2.3). The consideration of both categories is similar. The following assumptions are made for definiteness of consideration: • Bubble errors are represented by zeroes below transition between logic ones and zeroes. The number of zeroes defines the bubble error type. • The most significant thermometer bit which is placed with a logic one is always considered, as a correct.

48.2.3 Bubble Errors Occurrence and Conversion The Statistic of Bubble Error Occurrence. In paper [17] the statistic of the first type bubble error appearance in the first ðBE ðN; K; 1ÞÞ, the second ðBE ðN; K; 2ÞÞ and the third ðBE ðN; K; 3ÞÞ positions was discussed and there was shown, that the most probable are BE ðN; K; 1Þ because the probability of its appearance is at least two order magnitude higher, than the same probability of BE ðN; K; Pos  2Þ. The same may be asserted about the second type bubble errors. Thus, the most probable, and therefore most frequently occurring, are bubble errors in the first bit position (BE ðN; K; 1Þ). Hence, these errors should be corrected most notably [1]. Conversion of the First Type Bubble Errors in a Modified ROM-Encoder. In modified ROM-encoder due to its architecture peculiar properties [1, 2] every even code combination BE ðN; 2K; 1Þ is converted into the code combination, which appropriates the same code combination without error. Every odd code combination is converted into code combination, which appropriates the nearest less significant odd code combination without error [1, 2]. The Problem of Bubble Error Identification. As it was previously mentioned in Sect. 48.2.2, the bubble errors may be represented not only by logic zeroes below the transition between logic zeroes and logic ones, but also by logic ones above it. In the case of BE ðN; K; 1Þ the question appears, which bit is really correct: the logic one in zero position, or the logic zero in the first position. The proposed approach, which takes into account the input analog signal edge, makes this question clear: if the considered error input combination occurs during the rising signal edge, then the logic zero in the first position is considered as an error. Otherwise, in case of decreasing signal edge, the logic one in zero position is considered as an error [1].

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M. A. Bellavin and D. O. Budanov

Correction Circuit Design

48.3.1 Proposed Approach to the Bubble Error Correction According to the information presented in Sect. 48.2.3, it is possible to propose a special approach for the bubble errors correction in a modified ROM encoder to take the direction of input signal edge into account. This approach is as follows: • Only the first type bubble errors are considered. • Only even thermometer bits should be corrected, because due to encoder architecture only the most significant non-zero bit defines, is the input combination odd or even. According to the assumptions, which had been made in Sect. 48.2.2, it is never equal to zero. Other less significant odd bits do not take part into output code formation. • If the input combination is BE ðN; 2K; 1Þ and the input analog signal edge is positive, then logic one in the most significant non-zero bit should be transmitted to the encoder input and input combination should be converted without changes. In the case of a negative edge the logic one in the most significant non-zero bit should be replaced with zero and input combination should be transformed into adjacent less significant even combination, i.e. 2K  2. • If the input combination is BE ðN; 2K þ 1; 1Þ and the input analog signal edge is positive, then logic zero in a less significant even bit, adjacent to the most significant non-zero bit, should be replaced with logic one, and input combination should be converted without changes. In the case of a negative edge logic zero in a less significant even bit, adjacent to the most significant non-zero bit, should be transmitted to the encoder input and input combination should be transformed into adjacent less significant odd combination, i.e. 2K  1. • If the input combination is BE ðN; K; Pos  2Þ, then a zero value in the considered bit should be replaced with logic one, thus the input code combination transforms into correct one and then transfers to the encoder inputs.

48.3.2 Algorithm of Even Bit Correction To make a decision, which signal should be sent to the encoder input, the following steps should be completed: 1. The considered bit position in input combination should be determined. 2. The information about input analog signal edge should be received. 3. It is required to clear up, which signal (logic one or zero) is presented at the signal input, i.e. input X1 (see Fig. 48.2), at the moment. 4. The decision, which signal (logic one or zero) should be passed to the encoder input, should be made according to the received results on steps 1–3. The structure of both kinds of circuit is shown on Fig. 48.2.

48

ROM-Based Encoder with Bubble Error Correction

Lock Input 3

X5

Lock Input 2

X4

Lock Input 1

X3

Comp

X2

Signal Input

X1

CorrecƟon scheme Out

435

Lock Input 2

X4

Lock Input 1

X3 CorrecƟon scheme Out X2

Comp Signal Input

X1

(a)

(b)

Fig. 48.2 Structure of two kinds of the even bit correction circuits: (a)—for general case; (b)—for correction of the adjacent to MSB even bit

48.3.3 Concept of the Correction Circuit It supposed to design two kinds of even bit correction circuit: • Circuit for the even bit correction in general case (for any even bit of thermometer code except bit with number 2N  2, where N is encoder resolution). • Circuit for the correction of the adjacent to MSB even bit (bit with number 2N  2). It has the similar function, as the circuit for general case, but its design seems to be simpler due to less input number because of the certain position in a thermometer code combination. The following designations are used on Fig. 48.2(a): • X1—input for the thermometer code signal. • X2—input for signal from comparison circuit. • X3; X4; X5—inputs for definition of the bit position in the input code combination. If it is supposed, that the considered bit has number K, where K is any even number from 2 to 2N  4, N is the resolution of the encoder, then input X3 is connected with the bit with number K  1, input X4—with the bit number K þ 1, input X5—with the bit number K þ 3. Circuit on Fig. 48.2(b) has the similar assignment of inputs, but due to the certain position of the considered bit, the input X5 is absent, inasmuch it is unnecessary.

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48.3.4 Algorithm for the Bit Position Definition Since the decision on the value of the output signal of the correction circuit depends on the position of the corrected bit, an appropriate algorithm for determining the position should be presented. The algorithm steps are as follows: 1. When input signal X5 is equal to zero: • If the input signals X3 and X4 are both equal to logic zero, the considered bit is placed above 1-0 transition or it is the most significant non-zero bit of the thermometer code. Logic one on circuit output is possible only if input signals X1 and X2 are both equal to logic one. • If the input signal X4 is equal to logic one, but signals X3 and X1 are both equal to logic zero, the situation is undefined, because the type of bubble error is not first. • If the input signals X3 and X4 are equal to logic one and signal X1 is equal to logic zero, then the considered bit is the most significant non-zero bit in the even thermometer code combination. If the input signal X2 is equal to logic one, the output signal should be equal to logic one too. If the input signal X2 is equal to logic zero, the output signal should be equal to logic zero too. • If the input signals X1, X3 and X4 are equal to logic one, then the code combination part near considered bit does not contain bubble error and output signal should be equal to logic one without dependence on signal X2. 2. When input signal X5 is equal to logic one, it means, that the considered bubble error is BE ðN; K; Pos  2Þ. Such bubble errors should be suppressed and the output signal should be logic one in any case. All statements from the first part of the reported algorithm are absolutely true for correction circuit for the adjacent to MSB even bit.

48.3.5 Logic Functions of the Proposed Circuits Logic functions describing the correction circuits were obtained in a standard way. The truth tables of proposed circuits were compiled based on algorithms reported in Sect. 48.3.2 and 48.3.4. Obtained from truth tables logic functions were minimized with Karnaugh maps. The obtained logic functions are: • For the general case of the corrected circuit: Out ¼ ððX1
X3 Þ
ðX1
X2 Þ þ X5 Þ
ðX4
ðX1  X2 ÞÞ

ð48:2Þ

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ROM-Based Encoder with Bubble Error Correction

437

Fig. 48.3 The correction circuit for the general case X4 X2 X1

Out

X5

X3

X2 X1 Out

X3 X4

Fig. 48.4 The correction circuit of the adjacent to MSB less significant even bit

• For the correction circuit of the adjacent to MSB even bit: Out ¼ X1
ðX2
X3 Þ
X4
ðX2
X3 Þ

ð48:3Þ

48.3.6 Schematic of Correction Circuits Schematic for both kinds of the correction circuits are presented on Figs. 48.3 and 48.4.

48.4

Simulation of Bubble Errors Correction

The simulation of the bubble errors correction was performed in Cadence Virtuoso for a 5 bit modified ROM encoder. Two sets of simulation were provided: • Simulation of all possible bubble errors in first position in all input combinations (from BE ð5; 31; 1Þ to BE ð5; 2; 1Þ) for cases with and without correction. • Simulation of all possible bubble errors in all positions in a specific input code combination (for example, from BE ð5; 25; 1Þ to BE ð5; 25; 24Þ) for cases with and without correction. As an example, the input code combinations corresponding to 24 and 25 were simulated, since these combinations are the most illustrative. The simulation results are summarized in Tables 48.3 and 48.4.

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Table 48.3 Simulation results of all probable bubble errors in the first position Circuit type

Number of error input combinations

Number of correct output combinations

With correction Without correction

30

30 15

Table 48.4 Simulation results of bubble errors in all positions in a specific input code combination Circuit type

Thermometer code combination

Number of error input combinations

Number of correct output combinations

With correction

24 25 24 25

23 24 23 24

23 24 12 18

Without correction

48.5

Conclusions

A novel approach to the bubble error correction was proposed and special algorithms for its practical implementation were developed. The circuit for the bubble error correction to be applied with modified ROM-based encoder was designed based on the proposed approach. All probable first type bubble errors in the first positions of all thermometer code combinations were simulated. Also the simulation of bubble errors in all positions of certain thermometer code combination was provided for the cases of even and odd code combinations. The simulation results show that proposed circuit is able to successfully provide the bubble errors correction taking into account the direction of the input signal edge. As well it was shown that all bubble errors are successfully corrected and no incorrect output combinations are presented. So the designed correction circuit may significantly increase conversion accuracy and, therefore, the ENOB of flash ADC, based on a modified ROM-encoder.

References 1. M. Bellavin, D. Budanov, Study of bubble errors conversion in a modified ROM-encoder, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, St. Petersburg, 2019), pp. 58–61 2. D. Budanov, D. Morozov, M. Pilipko, An 8-bit analog-to-digital converter with a novel encoder using 90 nm CMOS, in IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, St. Petersburg, 2018), pp. 56–59

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3. M. Rahman, K. Baishnab, F. Talukdar, A novel ROM architecture for reducing bubble and metastability errors in high speed flash ADCs. IEEE J. Solid-State Circ. 43(9), 1982–1990 (2008) 4. I. Piatak, M. Pilipko, D. Morozov, Design considerations for pipelined ADCs, in IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW) (IEEE, St. Petersburg, 2016), pp. 646–648 5. Y. Lin, C. Lin, S. Chang, A 5-bit, 3.2- GS/s flash ADC with a digital offset calibration scheme, IEEE Trans. VLSI Syst. 18(3), 509–513 (2010) 6. A. Zjajo, J. Pineda de Gyves, Low-Power High-Resolution Analog to Digital Converter: Design, Test and Calibration (Springer, Netherlands, 2011) 7. H. Tang, H. Zhao, S. Fan, X. Wang, L. Lin, Q. Fang, J. Liu, A. Wang, B. Zhao, Design technique for interpolated flash ADC, in 10th IEEE International Conference on Solid-State and Integrated Circuit Technology (IEEE, Shanghai, 2010), pp. 180–183 8. H. Darwish, G. Leger, A. Rueda, A 0.2pJ/conversion-step 6-bit 200 MHz flash ADC with redundancy, in 27th Conference on Design of Circuits and Integrated Systems (DCIS 2012) (2012), pp. 1–6 9. A. Korotkov, Calibration and correction methods for analog-to-digital converters: State of the art, in 2013 International Symposium on Signals, Circuits and Systems (ISSCS) (IEEE, Iasi, 2013), pp. 1–8 10. A. Korotkov, Methods of calibration and correction of analog-to-digital converters (review). Russ. Microlectron. 43(3), 226–237 (2014) 11. S. Zhang, S. Wang, X. Lin, G. Ren, A 6-bit low power flash ADC with a novel bubble error correction used in UWB communication systems, in 2014 IEEE International Conference on Electron Devices and Solid-State Circuits, (IEEE, Chengdu, 2014), pp. 1–2 12. P. Ghoshal, S. Sen, A bit swap logic (BSL) based bubble error correction (BEC) method for flash ADCs, in 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) (IEEE, Kolkata, 2016), pp. 111–115 13. S. Hussain, R. Kumar, G. Trivedi, A novel low power high speed BEC for 2 GHz sampling rate Flash ADC in 45 nm technology, in 2017 IEEE International Symposium on Nanoelectronic and Information Systems (iNIS) (IEEE, Bhopal, 2017), pp. 133–138 14. T. Lakshmi, A. Srinivasulu, A low power encoder for a 5-GS/s 5-bit flash ADC, in Sixth International Conference on Advanced Computing (ICoAC) (IEEE, Chennai, 2014), pp. 41– 46 15. T. Lakshmi, A. Srinivasulu, N. Bizon, A power efficient 5-bit 5-gs/s parallel comparator analogue-to-digital converter, in 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), (IEEE, Iasi, 2018), pp. 1–6 16. L. Kumre, N. Ramesh, Design and implementation of flash analog to digital converter. Mater. Today Proc. 5(1), 1104–1113 (2018) 17. S. Padoan, A. Boni, C. Morandi, F. Venturi, A novel coding scheme for the ROM of parallel ADCs, featuring reduced conversion noise in the case of single bubbles in the thermometer code, IEEE International Conference on Electronics, Circuits and Systems (IEEE, Lisboa, 1998), pp. 271–274

Chapter 49

Performance Analysis for Massive MIMO Systems Based on Quadriga Channel Model Saeed Alsabbagh

and Aleksandr Gelgor

Abstract In this paper, we investigate the performance of massive MIMO systems in a single cell using independent and identically distributed (i.i.d.) Rayleigh channels. This includes studying the tradeoff between the spectral and energy efficiency of the system for different linear precoding schemes such as Maximum Ratio Transmission (MRT), Zero Forcing (ZF), and Minimum Mean Square Error (MMSE). In addition, to validate our results, a further examination is conducted using more realistic channel coefficients, generated in QuaDRiGa channel simulator. This aims to study the effect of propagation environment and antenna configuration on the system performance. Two antenna deployments at base station were used: uniform linear array (ULA) and uniform rectangular array (URA) for both propagation conditions: Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS). The investigation indicated that MMSE preforms the best among all linear precoders. Also, it is shown that most of the theoretical benefits of massive MIMO could be realized over real propagation conditions.










Keywords Multi-user MIMO Massive MIMO QuaDRiGa Linear precoding Spectral efficiency Energy efficiency




49.1




Introduction

Wireless systems are considered one of the key technologies in the past two decades. Data traffic has doubled every two and a half years since the beginning of wireless communications [1]. This tendency will certainly continue, driven by new unprecedented applications such as augmented reality and the Internet of Things [2]. Current technologies are trying to provide high quality mobile services with S. Alsabbagh (&)
A. Gelgor Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia e-mail: [email protected] A. Gelgor e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_49

441

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data rates of several megabits per second over wide areas and tens, or even hundreds, of megabits per second locally [3]. However, there may be certain situations that cannot be appropriately addressed along with the ongoing evolution of existing technologies. It soon becomes evident that the current mobile systems will not be able to deliver the necessary QoE [4]. A key goal of the 5G technologies is to improve the area throughput (bit/s/km2) by orders of magnitude; 100 and even 1000 higher throughput [1]. There are three basic ways in which wireless networks may be able to enhance the area throughput: (a) deploying base stations more densely; (b) using more spectrum; and (c) increasing the spectral efficiency (SE) [5]. Several means has been proposed to improve SE by addressing pulse shaping, such as [6–10]. Another important goal of 5G systems is to be more energetically efficient. This has also been discussed in many works [11–13]. Multiple antennas, also known as Multiple-Input Multiple-Output (MIMO) technology is considered one of the revolutionary solutions that has a substantial improvement of both spectral and energy efficiency [5]. MIMO has been adopted in almost all modern wireless communication systems e.g. IEEE 802.11ac/n (Wi-Fi), IEEE 802.16e (WiMAX), LTE/LTE-A (4G) [14]. Nevertheless, the idea of deploying BSs with more than a handful of service antennas is relatively new [15]. Massive MIMO is a new research field in wireless communication, where BSs are equipped with a large number of antennas and simultaneously serving multiple single-antenna users with the same time-frequency resource. Massive MIMO is where the number of antennas at BS M is much greater than the number of users K [5]. Extra antennas at BS help by focusing energy into extremely small regions of space which will lead to huge improvements in throughput and power consumption [16]. Theoretical studies of massive MIMO systems e.g. [7, 8, 17–19], have shown a great potential to provide very promising performance characteristics of high network capacities, link reliability, and energy efficiency. [17] discusses different channel models used for evaluating massive MIMO technology and, through numerical approaches, highlights their effect on system performance. The theoretical analysis of spectral and energy efficiency of massive MIMO systems is presented in details in [18], while [14] discusses different linear precoding scheme used to mitigate the interference between users. The performance of massive MIMO systems in terms of bit error rate (BER) is evaluated in [17] and [18]. Some researches e.g. [20, 21] have adopted the i.i.d. Rayleigh channels to perform a statistical analysis of massive MIMO performance while others [19, 22, 23] used real channel propagation data measured in specific environments. However, there are still a lot of questions to be answered regarding the practical aspects and scenarios of massive MIMO [24]. This includes performance analysis, estimation of the channel state information (CSI), design of low-cost and low-power BSs, and many others [25]. In this paper, we carry out a performance comparison amongst linear precoders: Minimum Mean Square Error (MMSE), Zero Forcing (ZF), and Maximum Ratio Transmission (MRT) precoding. This focus on evaluating the SE while changing the system parameters. After that we study the tradeoff between the spectral and

49

Performance Analysis for Massive …

443

energy efficiency of the system. The novelty of this work is investigating how massive MIMO performs in more realistic channel, generated in QuaDRiGa channel simulator. We studied the relation between the network setup and the system performance. Two antenna deployments at BS were used: uniform linear array (ULA) and uniform rectangular array (URA) for both propagation conditions: Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS). The remainder of this paper is organized as follows. Section 49.2 gives a brief overview on the precoding techniques of massive MIMO systems. In Sect. 49.3, channel modeling problem are mainly addressed. Section 49.4 presents the simulations parameters and demonstrates the performance evaluation and simulation results. Finally, we conclude the paper in Sect. 49.5.

49.2

System Description and Preliminaries

We consider a multi-user MIMO-OFDM system with N subcarriers. The BS is equipped with M antennas and serves K users in the same time-frequency resource [26], assuming that the BS has perfect CSI. Let x be a K  1 information vector, where xk is data symbol for user k and E[| xk|2] = 1. The linear precoding matrix is denoted by A 2 CMK . In this case, the transmitted vector can be written as s = Ax, and its average transmission power is constrained by E[|s|2] = tr(AHA) = Ptr. Then the system model of the MIMO channel can be described as y ¼ Hs þ n;

ð49:1Þ

where n is a K  1 additive noise vector, nk 2 Nð0; 1Þ.

49.2.1 Linear Precoding Techniques In massive MIMO systems, linear precoding techniques have showed near optimal performance while being more computationally efficient than nonlinear approaches. MMSE Scheme. MMSE is a linear precoding technique based on the mean square error (MSE) method. The task here is to find the optimal weight matrix WMMSE and scaling factor aMMSE, where AMMSE = aMMSE  WMMSE, to minimize the MSE of the received signal under the power constraint E[|s|2] = Ptr. Using the Lagrangian method [25], AMMSE can be expressed as
WMMSE ¼ HH HHH þ

K Ptr IK

1

; aMMSE ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ptr : trðWMMSE WH MMSE Þ

ð49:2Þ

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ZF Scheme. ZF is a linear precoding technique in which the interuser interference at each user is being cancelled out. ZF performs a pseudo-inverse of the channel matrix. Therefore, AZF can be expressed as [27] qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  1 WZF ¼ HH HHH ; aZF ¼ tr WPtrWH : ð ZF ZF Þ

ð49:3Þ

MRT Scheme. MRT is a linear precoding technique which maximizes the SNR at each user. Hence, MRT works well in the systems where the transmitted power at BS is low. AMRT can be expressed as [27] WMRT ¼ HH ;

aMRT ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ptr : tr ðWMRT WH MRT Þ

ð49:4Þ

49.2.2 Spectral Efficiency Let yk and xk be the kth symbol of y and x respectively. Then, from (49.1) we write XK y k ¼ h k ak x k þ h a x þ nk : i¼1;i6¼k k i i |fflffl{zfflffl} |{z} |fflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflffl} noise desired signal

ð49:5Þ

interference signal

Thus, the signal to noise and interference ratio at user k is given by: SNIRk ¼ PK

jhk ak j2

i¼1;i6¼k

jhk ai j2 þ 1

:

ð49:6Þ

From Shannon’s theorem, the achievable rate over additive white Gaussian noise (AWGN) is obtained as a function of the signal-to-noise ratio (SNR) [20]. However, in MU-MIMO system, in addition to AWGN there is the interuser interference. Thus, the achievable rate of the k-th user can be expressed as [27] Rk ¼ E½log2 ð1 þ SNIRk Þ:

ð49:7Þ

From (49.6) we can write " Rk ¼ E log2 1 þ PK

jhk ak j2

i¼1;i6¼k

jhk ai j2 þ 1

!# :

ð49:8Þ

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Performance Analysis for Massive …

445

Having the achievable rates at different users, we calculate the sum-rate as follows R¼

XK k¼1

Rk :

ð49:9Þ

In fact, the optimal sum-rate can be achieved by applying nonlinear precoding technique called dirty-paper coding (DPC), which can be written as [28]    RDPC ¼ max log2 IK þ Ptr HH PH : P

ð49:10Þ

The diagonal matrix P allocates the transmit power Ptr among all users. This is a convex optimization problem that can be solved using some numerical methods [5]. Ideally, in the favorable propagation conditions where there is no interference between the users, the capacity of the channel is upper bounded by the interference free (IF) capacity given as

 Ptr C ¼ K  log2 1 þ : K

ð49:11Þ

49.2.3 Energy Efficiency The energy efficiency of a communication system is defined as the sum-rate R divided by the total transmit power Ptr. The energy efficiency is given as [27] g¼

49.3

R Ptr

ðbits/s/J/HzÞ:

ð49:12Þ

Channel Modeling

The performance of any MIMO system is always fundamentally dependent on the characteristics of the radio channel in which the system operates. To carry out a realistic performance assessment of massive MIMO systems we need to have a channel simulator that characterizes the main features of MU-MIMO channel. It must consider the array geometry at BS, the relationship between the channel responses of different antennas, and the physical location and orientation of BSs and UEs. In general, channel models are classified into two main categories based on their modeling approach Analytical modeling and Physical modeling. Analytical models characterize the impulse response or the transfer function of the radio channel mathematically without explicitly considering the underlying wave propagation

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mechanisms [29]. On the other hand, in physical channel models, the channel impulse response is obtained on the basis of physical wave propagation for a given simulation environment. They can further be split into deterministic models and geometry-based stochastic models GBSM [30]. GSBM can be seen as a balance between the two extremes of purely analytical and purely deterministic channel models and it is adopted in this work using the QuaDRiGa radio channel model. For a detailed explanation of QuaDRiGa’s features, software structure, and documentation see [31–33].

49.3.1 Generation of QuaDRiGa Channel Through QuaDRiGa model the network parameters are defined in order to carry out the channel generation process. First, we set the propagation scenario for all simulations. In our case, we chose a macro cell in densely populated urban environment with both LOS and NLOS propagation conditions. These scenarios are defined by “WINNER+” project and they are valid for frequencies between 1 and 6 GHz. The pathloss model for both conditions is: PLLOS ¼ 26 log10 d þ 25 þ 20 log10 fc ; PLNLOS ¼ 36:3783 log10 d þ 25:965 þ 23 log10 fc ;

ð49:13Þ

where d is the distance from the BS to the UE, and fc is the carrier frequency. The center frequency for the uplink/downlink channels is 2.6 GHz. The next step is to set up the antenna configuration at both the transmitters and receivers. All antenna-elements used at BS and UEs are omnidirectional antennas. Two main configurations for the antenna-array at the BS are considered. The first configuration is a 2-dimensional uniform rectangular array URA with 10 antenna-elements in each row and a k=2-spacing between antenna-elements. The other configuration is 1-dimensional uniform linear array ULA with k=2-spacing. After setting up the general simulation parameters, we need to design the network layout. This includes selecting the BS position and UE’ positions and tracks. In all simulations we consider that the BS is mounted on 20 m height while the UEs are randomly located 250 m far from the BS with 25 m separation distance. For each realization the positions of UEs and the scatterers are updated. Once all parameters are set up, the channel coefficients can be generated. On the output of each realization of QuaDRiGa channel simulator we get a list channels of representing each BS-UE pair. Each channel consists of two NRx  NTx  Npaths arrays. These arrays are the complex gains and the delay time of each path. Components from different paths are summed together to form one path since we consider the system is narrow band. Channel normalization was performed in the same manner as in [26].

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Simulation Results

This section is dedicated to present and discuss the simulation process. We studied various aspects of massive MIMO systems. First, we present a performance analysis using i.i.d. Rayleigh channel. After that, we compare the channels generated in QuaDRiGa against i.i.d. Rayleigh channels while using the optimal linear precoders and corroborate to what extent the performance of massive MIMO systems is true. For each simulation, a sufficient number of iterations has been conducted to get a steady smooth result. The main steps of the simulation process are listed below.

49.4.1 Evaluating Spectral Efficiency The first metric to study is the spectral efficiency of massive MIMO system. To estimate SE, we follow the procedures presented in Sect. 49.2.2. In fact, the value of SE is a function of three main parameters: number of BS antennas M, number of active UE K, the average signal-to-noise ratio per user (SNRu). Thus, three scenarios are considered in this simulation as follows. Versus M. First, we investigate the effect of the number of active antennas at BS on SE. We set both K = 10 and SNRu = −5 dB while increasing M. Figure 49.1 provides a quantitative comparison between different linear precoders and nonlinear DPC precoder. Also, for the sake of reference, we draw the upper bound capacity when the condition of favorable propagation (no interference) is fulfilled. 60

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The results show that by increasing the number of antennas at BS we increase the SE of the system. However, there is indeed a performance gap between the sum-rate achieved by DPC and the suboptimal linear precoders, but the gap reduces quickly with M since the channels decorrelate and all the curves get closer to the FP curve. Figure 49.1 also shows that MRT gives good performance when M * K. On the other hand, ZF gives better performance when M⁄K  1. MMSE performs the best sum-rate. Versus K. The second parameter we are interested to study its effect on SE is the number of active users in the cell. We set both M and SNRu while increasing the number of UEs. Figure 49.2 compares between different linear precoders in terms of SE for different numbers of UEs while setting SNRu = −5 dB and M = 100. For ZF scheme, SE increases quickly as the number of users increases. However, when K > 80, SE degraded rapidly. As the result, ZF works well while M⁄K > 1.5. On the other hand, when using MRT precoder, SE increases slowly as the number of users increases and it performs worse than MMSE and ZF. SE with MMSE increases rapidly until K = 80. When 80 < K < 90 SE increased very slowly. After K > 90, SE decreased slightly. As the result, MMSE works well while M⁄K > 1.25 and it provides the best performance. Versus SNRu. The final aspect related to SE is its dependency to the level of transmitted power. We set both M = 40 and K = 20 while increasing the average signal-to-noise ratio per user SNRu. Figure 49.3 illustrates the relation between SE and the average signal-to-noise ratio per user SNRu for different precoding schemes.

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Figure 49.3 shows that by increasing SNRu we increase SE of the system. The results also indicate that MRT gives better performance at low SNRu and get saturated after certain level of SNRu. On the other hand, ZF gives better performance and approaches to MMSE at high SNRu. However, MMSE performs the best SE in all situations.

49.4.2 Evaluating Energy Efficiency Energy efficiency is another important parameter is used to evaluate the performance of communication systems. To estimate the energy efficiency in massive MIMO systems we use the approach discussed in Sect. 2.3. We study the performance of massive MIMO system with linear precoding in terms of energy efficiency versus SE. We focus on the case where K = 10 and M = 50 or 100. Figure 49.4 depicts the tradeoff between SE and the energy efficiency of the system for both cases. In general, SE increases as the transmit power at BS increases. On the contrary, increasing the transmit power decreases the energy efficiency. This is supported by results shown on Fig. 49.4, but we notice that different precoding techniques behave differently. MRT gives better performance at high energy efficiency and low SE, while ZF performs better at high transmit power i.e. low energy efficiency. In all cases MMSE gives the best performance. In addition, it is shown that the energy efficiency significantly improves by increasing the number of antennas M.

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Fig. 49.4 Energy efficiency versus spectral efficiency using i.i.d. Rayleigh channel

49.4.3 QuaDRiGa Results From previous sections we concluded that by increasing the number of antenna-elements M the channels of different users become more orthogonal. Also, we came to that the MMSE precoding is the optimal linear technique and it approaches the DPC nonlinear precoder as M goes large and M⁄K  1. All previous simulations were conducted using i.i.d. Rayleigh channel. Now we move to evaluate if these assumptions stand still for other propagation scenarios built in QuaDRiGa tool. Spectral Efficiency. We first study how the spectral efficiency behaves in realistic scenarios. Figure 49.5 illustrate the performance of massive MIMO system in terms of SE against the number of antennas at BS M for different network setups built in QuaDRiGa while using MMSE precoder and setting K = 10. It is obvious from Fig. 49.5, that there is a gap between the performance in i.i.d. channels and the realistic QuaDRiGa channels. We also notice that the system performs better in NLOS propagation condition than LOS condition. In addition, we see that ULA provides better performance than URA using the same number of antennas at BS. This is true for both propagation scenarios LOS/NLOS. We measure a gain in spectral efficiency of 7–8 bits/s/Hz between the two configurations for large values of M.

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Energy Efficiency. The final step in our study is estimating the energy efficiency of massive MIMO systems in different network setups generated in QuaDRiGa tool. Figure 49.6 depicts the relation between the energy efficiency and the spectral efficiency for M = 100 and K = 10 while using the MMSE optimal linear precoder. From Fig. 49.6, we notice that when SE increased, the energy efficiency is decreased linearly in logarithmic scale for all propagation scenarios. It is clear, that there is a gap in performance between i.i.d. Rayleigh channels and QuaDRiGa channels. Also, we notice that the system performs better in NLOS propagation condition than LOS condition where the energy efficiency is 2–3 times higher in NLOS conditions than LOS. Furthermore, the ULA antenna configuration gives better performance in terms of energy efficiency than URA for both NLOS and LOS conditions.

49.5

Conclusions

Massive MIMO system introduces the opportunity of increasing the spectral efficiency and improving the energy efficiency simultaneously. In this work we managed to investigate and study several aspects of this new technology. First, we introduced a mathematical model of the system and discussed its main characteristics. We presented the main precoding techniques (MRT, MMSE, ZF, DCP). Then, the channel modeling problem has been addressed using the QuaDRiGa channel model. The last part of this paper was dedicated to present and discuss the simulation process. Rayleigh channels were used to carry out a quantitative comparison between different linear precoding technique i.e. MMSE, ZF, and MRT. As a result, we got that the SE of the system increases by increasing the number of antennas at BS. However, each precoding technique follows different curve. Generally, ZF gives better performances at high transmission power and when M ⁄ K  1. On the other hand, MRT gives better performance at low transmission power and when M  K. MMSE works well for both situations and gives the best performance of all linear precoders. Furthermore, the performance of MMSE approach the performance of optimal nonlinear DPC for large numbers of M and when M ⁄ K  1. After evaluating the performance of massive MIMO system in i.i.d. Rayleigh channels, we investigated if it holds these improvements in realistic propagation environment by estimating the presented metrics using a radio channel generated in QuaDRiGa tool. We considered studying two propagation scenarios LOS and NLOS. Also, we studied two antenna configurations at the BS URA and ULA. We came to that there is a notable gap in performance between the i.i.d. channels and realistic QuaDRiGa channels. Also, we got that massive MIMO systems performs better in NLOS conditions than in LOS and that ULA configuration provides 2–3 times higher spectral and energy efficiency over URA. Acknowledgements This research work was supported by Peter the Great St. Petersburg Polytechnic University in the framework of the Program “5-100-2020”.

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References 1. W. Xiang, K. Zheng, X.S. Shen, 5G mobile communications (2016) 2. E. Björnson, J. Hoydis, L. Sanguinetti, Massive MIMO networks: Spectral, energy, and hardware efficiency (2017) 3. A. Gupta, R.K. Jha, A Survey of 5G Network: Architecture and Emerging Technologies (2015) 4. D. Center, Samsung electronics “5G Vision”, White Paper (2015) 5. T.L. Marzetta, Fundamentals of Massive MIMO (Cambridge University Press, Cambridge, 2016) 6. S.V. Zavjalov, S.V. Volvenko, S.B. Makarov, A method for increasing the spectral and energy efficiency SEFDM signals. IEEE Commun. Lett. (2016). https://doi.org/10.1109/ LCOMM.2016.2607742 7. A. Plotnikov, A. Gelgor, Spectral efficiency comparison between FTN signaling and optimal pr signaling for low complexity detection algorithm, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2018) 8. S. Gorbunov, A. Rashich, BER performance of SEFDM signals in LTE fading channels, in 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018 (2018) 9. S.V. Zavjalov, S.B. Makarov, S.V. Volvenko, Application of optimal spectrally efficient signals in systems with frequency division multiplexing, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014) 10. A. Gelgor, A. Gorlov, V.P. Nguyen, Performance analysis of SEFDM with optimal subcarriers spectrum shapes, in 2017 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2017 (2018) 11. A. Gelgor, A. Gorlov, E. Popov, On the synthesis of optimal finite pulses for bandwidth and energy efficient single-carrier modulation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015) 12. A. Gorlov, A. Gelgor, E. Popov, Improving energy efficiency of partial response signals by using coded modulation, in 2015 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2015 (2015) 13. I.I. Lavrenyuk, A.S. Ovsyannikova, S.V. Zavjalov, S.V., Volvenko, S.B. Makarov, Improving energy efficiency of finite time FTN pulses detection by choosing optimal envelope shape, in 2019 26th International Conference on Telecommunications, ICT 2019 (2019) 14. N. Fatema, G. Hua, Y. Xiang, D. Peng, I. Natgunanathan, Massive MIMO Linear Precoding: A Survey (2018) 15. E. Björnson, E.G. Larsson, T.L. Marzetta, Massive MIMO: Ten myths and one critical question. IEEE Commun. Mag. (2016). https://doi.org/10.1109/MCOM.2016.7402270 16. E.G. Larsson, O. Edfors, F. Tufvesson, T.L. Marzetta, Massive MIMO for next generation wireless systems. IEEE Commun. Mag. (2014). https://doi.org/10.1109/MCOM.2014. 6736761 17. M. Kuerbis, N.M. Balasubramanya, L. Lampe, A. Lampe, On the use of channel models and channel estimation techniques for massive MIMO systems, in European Signal Processing Conference (2016) 18. H.Q. Ngo, E.G. Larsson, T.L. Marzetta, Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Trans. Commun. (2013). https://doi.org/10.1109/TCOMM.2013. 020413.110848 19. S. Payami, F. Tufvesson, Channel measurements and analysis for very large array systems at 2.6 GHz, in Proceedings of 6th European Conference on Antennas and Propagation, EuCAP 2012 (2012)

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20. T. Parfait, Y. Kuang, K. Jerry, Performance analysis and comparison of ZF and MRT based downlink massive MIMO systems, in International Conference on Ubiquitous and Future Networks, ICUFN (2014) 21. Y. Lin, X. Li, W. Fu, Y. Hei, Spectral efficiency analysis for downlink massive MIMO systems with MRT precoding. China Commun. (2015). https://doi.org/10.1109/CC.2015. 7386172 22. X. Gao, O. Edfors, F. Rusek, F. Tufvesson, Linear pre-coding performance in measured very-large MIMO channels, in IEEE Vehicular Technology Conference (2011) 23. J. Hoydis, C. Hoek, T. Wild, S. Ten Brink, Channel measurements for large antenna arrays, in Proceedings of the International Symposium on Wireless Communication Systems (2012) 24. D.C. Araújo, T. Maksymyuk, A.L.F. de Almeida, T. Maciel, J.C.M. Mota, M. Jo, Massive MIMO: survey and future research topics. IET Commun. (2016). https://doi.org/10.1049/ietcom.2015.1091 25. H. Ji, Y. Kim, J. Lee, E. Onggosanusi, Y. Nam, J. Zhang, B. Lee, B Shim, Overview of Full-Dimension MIMO in LTE-Advanced Pro (2017) 26. X. Gao, O. Edfors, F. Rusek, F. Tufvesson, Massive MIMO performance evaluation based on measured propagation data. IEEE Trans. Wirel. Commun. (2015). https://doi.org/10.1109/ TWC.2015.2414413 27. V.P. Selvan, M.S. Iqbal, H.S Al-Raweshidy, Performance analysis of linear precoding schemes for very large Multi-user MIMO downlink system, in 4th International Conference on Innovative Computing Technology, INTECH 2014 and 3rd International Conference on Future Generation Communication Technologies, FGCT 2014 (2014) 28. S. Vishwanath, N. Jindal, A. Goldsmith, Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Trans. Inf. Theory (2003). https://doi.org/10.1109/ TIT.2003.817421 29. J. Poutanen, Geometry-based radio channel modeling: Propagation analysis and concept development (2011) 30. P. Almers, E. Bonek, A. Burr, N. Czink, M. Debbah, V. Degli-Esposti, H. Hofstetter, P. Kyölsti, D. Laurenson, G. Matz, A.F. Molisch, C. Oestges, H. Ozcelik, Survey of channel and radio propagation models for wireless MIMO systems. Eurasip J. Wirel. Commun. Netw. (2007). https://doi.org/10.1155/2007/19070 31. S. Jaeckel, L. Raschkowski, K. Börner, L. Thiele, QuaDRiGa - Quasi Deterministic Radio Channel Generator, User Manual and Documentation (2014) 32. F. Burkhardt, S. Jaeckel, E. Eberlein, R. Prieto-Cerdeira, QuaDRiGa: a MIMO channel model for land mobile satellite, in 8th European Conference on Antennas and Propagation, EuCAP 2014 (2014) 33. S. Jaeckel, L. Raschkowski, K. Borner, L. Thiele, QuaDRiGa: a 3-D multi-cell channel model with time evolution for enabling virtual field trials. IEEE Trans. Antennas Propag. (2014). https://doi.org/10.1109/TAP.2014.2310220 34. M. Hassan, A. El Falou, C. El Langlais, Performance assessment of linear precoding for multi-user massive MIMO systems on a realistic 5G mm Wave channel, in 2018 IEEE Middle East and North Africa Communications Conference, MENACOMM 2018 (2018) 35. N. Zarifeh, A. Kabbani, M. El-Absi, T. Kreul, T. Kaiser, Massive MIMO exploitation to reduce HARQ delay in wireless communication system, in 2016 IEEE Middle East Conference on Antennas and Propagation, MECAP 2016 (2016)

Chapter 50

CPU-Based FPGA Algorithm Model of Fiber Optic Current Sensor Demodulator Alexey Mayzel , Andrei Medvedev , and Valentina Temkina

Abstract There is a far distance between computer simulation and hardware implementation of fast inline processing algorithms. This gets even harder when applying such an advanced technology as FPGA, with its benefits of speed and reliability but also caveats like unfamiliar development approaches, integer math and even hours of compilation time. We managed to bypass the complexity of tools by using LabVIEW FPGA and National Instruments hardware platform to rapidly implement control and demodulation functionality into a mock-up of a Fiber Optic Current Sensor. It was a great leap to get an alpha-prototype working in the lab with its parameters exceeding the best industry solutions. However, this implementation needed a plenty of work ahead to implement our ideas, debug and polish algorithms and ensure reliability. And that was the point where we got stuck with minor changes in the code and then hours of compilation to find out that we did wrong. Due to the rapidness of the process in fiber-optic scheme no other implementation could handle real-time processing so we were ought to put up with the delays. Finally, we convinced that this was the dead end. So, we put our efforts to create a reliable simulation model of the optical scheme of current sensor with the same LabVIEW tools. Succeeding in it, we applied our FPGA integer and fixed-point math to the desktop environment. This worked simply great, so we were able to run plenty of experiments, making changes and trying new ideas in real time in the form, that could be seamlessly moved to hardware FPGA-based platform. The article discloses several of our experiment results with the computer simulation correlated to the real-world measurements acquired with the mock-up.

A. Mayzel
A. Medvedev
V. Temkina (&) Peter the Great St. Petersburg Polytechnic University (SPbPU), Polytechnicheskaya, 29, St. Petersburg 195251, Russia e-mail: [email protected] A. Mayzel e-mail: [email protected] A. Medvedev e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_50

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Keywords Fiber optic current sensor FPGA LabVIEW




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Current measuring
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Introduction

Several years of sequential research [1, 2] in fiber-optic current sensing (FOCS) technology performed by authors resulted in functional lab mock-up of FOCS design. This mock-up implemented several new ideas, approaches and algorithms, and showed the remarkable measurement accuracy in different environment conditions. We have made several improvements to a well-known scheme [3] replacing a piezoceramic modulator with an electrooptic one. This allowed to vary the modulation frequency flexibly, even moving to ten times higher frequencies, and exposed a much wider dynamic range than a resonance-based piezoceramic modulator. Moreover, we could apply modulation frequencies equal to power of 2, that required much fewer FPGA resources for inline processing. Moving to higher frequency disclosed an additional economical effect. The higher the modulation frequency is, the shorter delay line is required for operation [4]. This line is represented with hundreds of meters (in its legacy implementation about a kilometer) of expensive bow-tie optical fiber. With a reciprocal length to frequency dependency a 10 times higher frequency of modulation requires a 10 times sorter delay line, resulting 10 times savings on this costly component. In brief the FOCS scheme being researched functions as following: light radiation from source propagates through the fiber-optic coupler, polarizer and phase modulator oriented 45° to the polarizer [5]. The output represented with two orthogonal modes passes through a bow-tie fiber delay line to a quarter-wave fiber plate turning into circularly polarized and then to a magnetic field sensitive part made of spun fiber. At the end, the light reflects at the mirror and propagates backwards with an opposite polarization direction and returns to a photosensor interfering with the onward wave [6–8]. The enabling element of implemented optical scheme is a fiber quarter-wave plate, that transforms orthogonally polarized modes into circularly polarized ones and vice versa. This plate is a noticeably short (about 1–2 mm depending on the fiber used) piece of fiber, that is extremely sensitive on length errors, bending and environment temperature. We have made our efforts to engineer a method of making this component with accuracy enough for proper operation of overall optical scheme. It is well-known that the accuracy and stability of operation of this scheme is strictly dependent on quarter-wave plate conditions, especially its temperature [9]. We equipped the mock-up with a thermal chamber around the plate and run several experiments with temperature control. In temperature limits between 15 and 55 °C the accuracy of electric current measurements drop achieved 5% that is unacceptable for industrial implementation. Then we implemented authors’ algorithms of

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temperature error compensation that reduced the error to 0,2% without any hardware improvements.

50.2

The Problem

Though the mock-up with developed compensation algorithms represented a good stability in variable temperature and current levels, we faced unexpected artifacts sometimes causing the scheme to denial of operation. It was a sudden process appearing in different circumstances which were difficult to determine intuitively. The research of the cause of this trouble appeared to be much more difficult because of hardware FPGA-based implementation of control and processing algorithms, but no other platform than FPGA could provide a nanosecond synchronization [10, 11] of modulation control and measurements with inline demodulation and error processing. With all this benefits, FPGA required a careful handling of fixed-point and integer datatypes avoiding saturation and data loss and took about two hours of compilation time on every change made to the code. This forced us to develop an adequate computer model of the FOCS scheme to discover the limitations of algorithms in simulation instead of physical object [12]. As the mock-up software was implemented in LabVIEW programming environment the decision was made to develop a model using the same tools. It should give a possibility of re-use FPGA code with simulated signals without any significant change and then after applying new patches on it to move it back to FPGA seamlessly. The simulation model was implemented using LabVIEW primitives basing on the Jones matrices formalism. Every subsequent element represented with its integral matrix form and merged. The model is described in detail in [13].

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The Experiment

The simulation ran on 8 generation Core i7 mobile processor, with a time step of 1/2.56E6 s (1/10 of modulation frequency selected), at a speed of approximately 3.5 s per 0.02 s of simulated process which is 1 period of 50 Hz industrial current. It is not a real time, but extremely more efficient than 2 h of FPGA code compilation. But what is the best for choosing LabVIEW for modeling is that we did move the algorithms from FPGA code to execute on host processor keeping the code “as is” with integer and fixed-point variables resolution as shown in Fig. 50.1. Unlike the compiled FPGA instance, the same code running on CPU is fully traceable, every wire data value can be monitored, plotted or saved to file.

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Fig. 50.1 Int32/Int64 and FXP FPGA code in simulation

The demodulation algorithm worked with simulation model exactly like we observed it to operate on physical mock-up. That was the approval of simulation model fidelity over its physical twin. We were able to run a plenty of tests, achieving the best performance over resources needed for demodulation [14, 15]. Skipping the iterative process of algorithms polishing we found out the limitations of phase signal demodulation. A difference between two setups can be seen in Fig. 50.2: 2p current amplitude with 20% of noise and with 21% of noise in measured current signal. It is noticeable that in the first case the signal is noisy but is a sine waveform of 50 Hz. And even more, the noise is far less than 20% of signal, because of averaging in processing algorithm. Next one more percent of noise does not make the demodulated signal worse. The algorithm completely fails! There can be seen significant phase wraps that are unrecoverable with unwrap algorithm included in processing code. Injecting more noise in source signal destroys the output totally, making no sine waveform discoverable. The behavior described above is universal for this algorithm, but the threshold of 20% noise is applicable only to the current amplitude of 2p. There should be a dependency over current vs noise to demodulation potency of algorithm. Thus, we automated the discovery process, sending to model matrix of current amplitudes and noise percentage. The resulting SNR surface distribution and its’ projection are represented in Figs. 50.3 and 50.4. The behavior discussed above is seen on these images. There is a threshold, a limit of algorithm’s robustness, where a minor disturbance makes the method all inapplicable. The projection in Fig. 50.4 discloses the threshold trends. The higher is the measured signal, the less robust is the algorithm against noise. The future experiments sowed that at the levels below p/2 the demodulation processing recovers the signal with 100% of noise, and at above 10p amplitude the unwrapping of demodulated signal returns errors on 0.5% noise. This overall threshold line appears

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Fig. 50.2 Demodulated current signal vs time. Top left to bottom right: current signal of 6.28 radians, 20% noise 1 period, 10 periods, 21% noise 1 period, 10 periods

Fig. 50.3 Measured signal SNR vs. signal amplitude vs. source signal noise

a logarithmic graph with approximately 32 rad of X axis intersection and, in theory, asymptotically approaching Y axis, which is limited in real setup with ADC LSB value. Fortunately, in real-world operation the higher the current signal is, the lower noise percentage it contains. And the tolerance of the algorithm growth as the signal lowers.

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Fig. 50.4 Signal amplitude vs noise % accuracy distribution

50.4

Conclusions

The research on the FPGA algorithm of signal processing for Fiber-Optic Current Sensor made a great impact on future research. The caveats and limitations are now visible, so they can be avoidable. Similarly, the perfect operation conditions with the widest dynamic range and best stability are now easily determinable to achieve best accuracy and robustness of FOCS for industry.

References 1. V. Temkina, A. Medvedev, A. Mayzel, A. Mokeev, Compensation of fiber quarter-wave plate temperature deviation in fiber optic current sensor, in 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), Art. no. 8906876 (IEEE, St. Petersburg, 2019), pp. 339–341 2. V. Temkina, A. Medvedev, A. Mayzel, Computer simulation of the fiber optic electric field sensor. J. Phys. Conf. Ser. 1236, 012031 (2019) 3. M. Bisyarin, O. Kotov, A. Hartog, L. Liokumovich, N. Ushakov, Rayleigh backscattered radiation produced by an arbitrary incident mode in multimode optical fibers. J. Appl. Opt. 57, 6534–6544 (2018) 4. L. Liokumovich, K. Muravyov, P. Skliarov, N. Ushakov, Signal detection algorithms for interferometric sensors with harmonic phase modulation: miscalibration of modulation parameters. J. Appl. Opt. 57, 7127–7134 (2018) 5. A. Varlamov, M. Plotnikov, A, Aleinik, P. Agrusov, I. Il’ichev, A. Shamray, et al., Acoustic vibrations in integrated electro-optic modulators on substrates of lithium niobate. Tech. Phys. Lett. 43, 994–997 (2017) 6. A. Tronev, M. Parfenov, P. Agruzov, I. Ilichev, A. Shamray, High extinction ratio integrated optical modulator for quantum telecommunication systems. J. Phys. Conf. Ser. 951(1), 012002 (2018)

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7. A. Tronev, M. Parfenov, P. Agruzov, L. Ilichev, L. Shamray, A. Shamray, Fabrication of high extinction ratio lithium niobate integrated optical modulators using photorefractive trimming, in Proceedings of SPIE - The International Society for Optical Engineering, vol. 10535 (SPIE, Bellingham, 2018), p. 1053527 8. A. Tronev, M. Parfenov, P. Agruzov, I. Ilichev, A, Shamray, Performance improvement of lithium niobate high extinction ratio modulators by means of photorefractive trimming, in Asia Communication Photonics Conference ACP, paper S3J.4. OSA (2017) 9. A. Petukhov, A. Smirnov, V. Burdin, The temperature properties of fiber quarter-wave plate of minimum length. J. Appl. Photon. 2(1), 80–87 (2015) 10. NI 7935R FlexRIO Documentation, http://www.ni.com/pdf/manuals/375175c.pdf. Accessed 05 July 2020 11. S. Ivanov, A. Lavrov, I. Saenko, S. Bessoltsev, A. Dostovalov, A, Wolf, Microwave photonic beamforming system with broadband chirped fiber Bragg grating, in Proceedings of SPIE The International Society for Optical Engineering, vol. 10774, 107740W (SPIE, Bellingham, 2018) 12. O. Kotov, I. Chapalo, Mode-mode fiber interferometer with impact localization ability, in Proceedings of SPIE - The International Society for Optical Engineering, vol. 9899, 98992J (SPIE, Bellingham, 2016) 13. V. Temkina, A. Medvedev, A. Mayzel, Computer modeling of fiber optic current sensor, in YETI International Youth Conference on Electronics, Telecommunications and Information Technologies (2020, to be published) 14. P. Trubin, E. Savchenko, E. Velichko, Development of polarimetric sensor for identification system, in Proceedings of 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (IEEE, St. Petersburg, 2018), pp. 279–282 15. N, Kozhevnikov, Stokes polarimeter for pulsed radiation based on a series of Brewster plates. J. Opt. Technol. (A Transl. Opt. Zhurnal) 83, 123–126 (2016)

Chapter 51

Configuring the Interval Target in a Multilayer Feedforward Neural Network on the Example of the Problem of Medical Diagnostics Eugeniy Mirkin and Elena Savchenko Abstract In this work a new approach of using the paradigm of “interval teacher” in the classical scheme of training neural networks (NN) with a teacher is presented. At the same time, the training set in the form of a blurred set with clearly defined interval boundaries is proposed. Any element belonging to the interval training set is considered an acceptable “teacher” for a particular training sample. Various concepts of choosing a specific “teacher” are presented, in the process of configuring NN, from a given interval set based on additional requirements for the functioning of the system. An NN architecture has been created that implements a mechanism for selecting a specific “teacher” from a deterministic interval set. For a number of specific requirements for the training process, computational experiments were carried out on the example of training NN in the problems of medical diagnostics. A comparative analysis of the proposed and classical training schemes of the NN is shown, confirming the effectiveness of the proposed training concept.




Keywords Interval training set Training sample configuring mechanism Feedforward neural network Medical diagnostics




51.1




Introduction

Artificial neural network (ANN) entered our lives rapidly and is used to solve a wide range of tasks such as pattern recognition, classification and data clustering, function approximation, control, forecasting system. This is confirmed by many scientific studies and publications in this area [1, 3–5]. ANNs are applied to problems where algorithmic methods are not working or not efficient. Neural networks, like biological ones, are computing systems possissing a huge number of E. Mirkin (&)
E. Savchenko (&) Computer Information Systems, International University of Kyrgyzstan, Bishkek, Kyrgyz Republic e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_51

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simple parallel processors with many connections. ANNs exhibit a surprising number of properties inherent to the brain despite the fact that in the construction of such networks is usually done by a number of assumptions and significant simplifications that distinguish them from their biological counterparts. These properties include training through generalization and extraction significant data from redundant information [2, 6–8]. At the same time, ANN remains in a primitive analogue of the mechanism of human decision making, The problem of choosing the correct goal in the training paradigm of ANN with a teacher is considered fully determined for a purpose and given to some substance having a higher level in the hierarchical scheme of training ANN. That is, in the process of training ANN, the training set is fully specified and does not change in the process all its settings. However, in the real conditions of the process of human training by the teacher or self-learning person, a situation of changing strategies and training plans always arise depending on many factors influencing this process [13, 14]. Among these factors, the main ones are: • ability to train; • speed of training; • training result. If these factors are interpreted from the point of view of ANN training, the ability to learn means choosing the topology, parameters and methods of configuring ANN leading to acceptable training results. This process can be time-consuming and be accompanied by iterative procedures for selecting an acceptable configuration of ANN and the method of its adjustment [9–11, 16]. This process means the selection of a capable student and the method of training to achieve an acceptable training result [17]. At the same time, the natural process of training or the person self-education can be accompanied by an adaptation of the strategy and training plans, depending on his/her abilities identified in the training process. Thus, changing the training goal in the training process of a student is a natural process motivated by his/her abilities, including the ability to speed training, and the result of training. A new system architecture that uses the “interval teacher” paradigm to train ANN was created on the basis on the arguments developed above. The first part of the article proposes a new system architecture that implements the paradigm of ANN training with an “interval teacher”. The second part of the study provides a comparative analysis of the proposed and classical training schemes for an example of the Boolean function XOR. In the third part, a comparative analysis of the proposed and classical training schemes is presented on the example of the problem of synthesizing a medical classifier.

51

Configuring the Interval Target …

51.2

465

Synthesis of the Structure of the System Implementing the Paradigm of Teaching ANN with an “Interval Teacher”

The proposed system architecture that implements the paradigm of ANN training with an “interval teacher” suggests (Fig. 51.1): 1. Configuring the target (training) set in the form of a blurry set with clearly defined interval boundaries defined by the final training results (Goal Coordination Block Fig. 51.1). 2. Definition of the rules for changing the boundaries of the target (training) set depending on the parameters of the training process and the final training result. (Goal Coordination Block Fig. 51.1). 3. The formation of a controlled training set in the form of an adaptive model that provides a mechanism for selecting a specific “teacher” from the permitted target set. (Target Model Fig. 51.1). Note that the model for the formation of the target training set can be implemented, including based on ANN. 4. The formation of the mechanism of influence on the model of formation of a specific “teacher”, from the permitted target set, depending on the parameters of the training process and the final training result. (Setting TM parameters, Goal Coordination Block Fig. 51.1).

Fig. 51.1 The architecture of a closed system that implements the paradigm of training ANN with “interval teacher”, where ^y is ANN training goal; y is ANN training results; E is training error, X is training set, Y is goal set

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Figure 51.1 shows the general scheme of the system that implements the paradigm of ANN training with an “interval teacher”, which includes the following modules: System Model is an ANN, functioning in the classical paradigm with the “teacher”. The architecture of this module is chosen by the researcher for solving specific problems can be of any known configuration and can be trained by any known method (Parameter Adjustment Block), using the traditional network training error (Error). Target Model is a module that implements a controlled model designed to form a target (training) set for ANN (System Model) and can be: • classical (traditional) “teacher” for training ANN; • a teacher formed as an interval set (“interval teacher”); • a teacher, formed in terms of a specially created ANN, training by some known method (Parameter Adjustment Block) using the traditional network learning error (Error). Parameter Adjustment Block is a module that implements training of the ANN System Model and Target Model (if necessary) by some known method. Goal Coordination Block is a module that coordinates the rules for changing the boundaries of the target (training) set depending on the parameters of the training process of the ANN (System Model) (for example, Error) and the final result of training the ANN (System Model). The module works as a coordinator in choosing a strategy for the functioning of the structure of interval self-organization of a neural network. It is responsible for the selection and formation of the System Model and Target Model modules. Goal Coordination Block module is the main in the hierarchical structure of managing the ANN configuring process. It is tuned by person to form the boundaries of the interval training set, the rules for changing the network training strategies and an acceptable training result for the ANN System Model.

51.3

The Paradigm of Teaching ANN with an “Interval Teacher” on the Example of the Implementation of the Boolean Function XOR

In this work it was considered the application of the proposed paradigm of ANN training with an “interval teacher” for the following case. In this case, the System Model represents a classic multilayer ANN of feedforward propagation, and the Target Model generates a training set from the allowed interval range, using as an example the logical Boolean function XOR. Table 51.1 presents the training set (x1 ; x2 Þ, as well as the classical (^y) and interval (ymin; ymax ) goal sets for the Boolean function XOR.

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Table 51.1 Boolean function—XOR

467

Training set x1 0 1 0 1

Goal set Classical teacher x2 0 0 1 1

^y 0 1 1 0

Interval teacher ^ 2 ½ymin ymax  y ymin −0.49 0.51 0.51 −0.49

ymax 0.49 1.49 1.49 0.49

Fig. 51.2 Neural network architecture for the example of Boolean function XOR

A multilayer neural network of feedforward propagation (Fig. 51.1) consists of two layers: • the first layer has 2 neurons, where the activation function is sigmoidal, • the second layer has one neuron and a linear activation function. The Levenberg-Marquardt training algorithm was chosen for training. For this example, the choice of a training set from a given interval was carried out using the goal correction mechanism (Goal Modification, Fig. 51.1). The training goal ^y, minimizing the total training error E on the entire training set X was calculated for the current epoch Fig. 51.4. In the proposed paradigm, after each epoch of training, we will further configure the teacher for the neural network, depending on the results obtained in one epoch of training, according to the following rules: 8 y  ymin < ymin; if ^y ¼ ymax ; if ð51:1Þ y  ymax : y; if ymin \y\ymax where ymax , ymin are intervals of “interval teacher”; y is neural network training result; ^y is a teacher for the next training epoch.

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This algorithm for calculating the training goal ^y for the current epoch minimizes the total training error E on the entire training set X. The effectiveness of the proposed training paradigm by the example of the implementation of the Boolean function XOR is confirmed by examples for various initial initializations of the neural network coefficients (four different options) (Fig. 51.3).

Fig. 51.3 Training curves of the classical neural network (a–d) and neural network with setting interval targets for four different initializations of ANN coefficients

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Fig. 51.3 (continued)

As can be seen from Fig. 51.3, the proposed paradigm with interval teacher for all variants of random initialization of the neural network showed a high training speed with a guaranteed initial result.

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Example of the Synthesis of a Medical Classifier

We apply the proposed system architecture that implements the paradigm of training ANN with an “interval teacher” (Fig. 51.1) for the problem of synthesizing a medical classifier. Formulation of the problem. It is required to classify (separate) a malignant tumor from non-malignant tumors according to the description of the sample cells (Wisconsin Breast Cancer Database). This breast cancer database was obtained at the University of Wisconsin Hospital, Madison by Dr. William H. Wolbergh (1992) [12]. We apply the proposed paradigm of ANN training with “interval teacher” using the classification problem of medical diagnostics as an example. Training and target sets are presented in Table 51.2. As can be seen from the Table 51.2, the training sample consists of 683 entries. The input data were divided into training (554), validation (61) and test (68) samples. A multilayer neural network of feedforward propagation (Fig. 51.1) consists of two layers: • the first layer has 2 neurons, where the activation function is sigmoidal, • the second layer has one neuron and a linear activation function. The Levenberg-Marquardt training algorithm was chosen for training. For this example, the choice of a training set from a given interval was carried out using the goal correction mechanism (Goal Modification, Fig. 51.1). The training goal ^y, minimizing the total training error E on the entire training set X was calculated for the current epoch Fig. 51.2. In accordance with the proposed methodology, the target sets were formed in the paradigm (Table 51.2): • classical (traditional) “teacher”; • a teacher formed as an interval set (“interval teacher”). In the proposed paradigm, after each training epoch, the target set of training for the ANN (System Model) was set up, depending on the results obtained in one of training epochs, according to rule (51.1).

Training set

5

3

6

3

4

10

6

682

683

…

5

2

Clump Thickness

1

№

1

7

8

1

4

1

Uniformity of Cell Size

1

7

8

1

4

1

Uniformity of Cell Shape

1

6

1

1

5

1

Marginal Adhesion

2

4

3

2

7

2

Single Epithelial Cell Size

1

10

4

2

10

1

Bare Nuclei

Table 51.2 Input data for the problem of synthesis of a medical classifier

3

4

3

3

3

3

Bland Chromatin

1

1

7

1

2

1

Normal Nucleoli

1

2

1

1

1

1

Mitoses

Target set

0

1

0

0

0

0

Классический учитель

0,49

−0,49

1,49

0,49

−0,49

0,49

0,49

−0,49

0,51

0,49

−0,49

−0,49

Interval teacher ^ y 2 ½ymin ymax  ymax

Interval teacher ^y 2 ½ymin ymax  ymin

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Fig. 51.4 Neural network architecture

Fig. 51.5 Training curves of the classical neural network and neural network with the setting of interval goals

As can be seen from Fig. 51.5, the proposed paradigm with interval teacher showed a high training rate with a guaranteed result. The effectiveness of the model designed to solve classification problems, typically estimated by the coefficient of error (error rate). Classifier Error was determined on a test set of 68 examples on which the classifier was not trained, that is determined by the error of generalization. Classifier Error is presented in graphical form in a matrix of inconsistencies (Confusion Matrix) Fig. 51.6.

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Fig. 51.6 Graphs showing data classification accuracy

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For neural networks: with a classical teacher. (Classic setting) From the confusion matrix it is shown that in the data from the first row 55 examples is referred to the first class that it is 80.9% of all test 68 objects. 13 examples of the first class were wrongly classified as objects to the second class, which is 19.1%. 80.9% were correctly classified to the first class and 19.1% were classified incorrectly. The average row of the matrix shows that the objects of the second class were not classified by the neural network. The last row of the matrix shows the accuracy of the classifier, which is 80.9% from the 68 examples. 19.1% of the objects were classified incorrectly. teacher with interval (Interval goal setting) From the confusion matrix it is shown that in the data from the first row 54 example is referred to the first class that it is 79.4% of the 68 objects. Zero objects of the first class were wrongly classified as objects of the second class. 100% of the first class objects were correctly classified. The average row of the matrix shows that 13 examples were referred to the second class, that is 19.1% of all 68 sites. 1 example of the second class were wrongly classified as first class objects. 92.9% correctly classified to the examples of the first class and 7.1% are classified incorrectly. The last row of the matrix shows the accuracy of the classifier, which is 98.5% from the 68 examples. 1.5% of the objects were classified incorrectly. Thus, the accuracy of the classifier of a neural network with an interval teacher shows the best result in comparison with a neural network with a classical teacher.

51.5

Conclusion

A system structure is proposed that implements the mechanism for selecting a specific “teacher” in the process of configuring neural network from a given interval set based on additional requirements for the functioning of the system. A computational experiment was carried out to synthesize a medical classifier in recognition of a cancerous tumor according to the description of the sample cells. A comparative analysis of the classical and proposed training schemes showed the effectiveness of the last one in terms of the convergence rate and the recognition quality of the training process.

References 1. W. Liu et al., A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017) 2. A. Wojtowicz et al., Solving the problem of incomplete data in medical diagnosis via interval modeling. Appl. Soft Comput. 47, 424–437 (2017) 3. S. Khaikin, Neural networks: full course. M. Williams (2006) 4. O. Sporns, R.F. Betzel, Modular brain networks. Annu. Rev. Psychol. 67, 613–640 (2016)

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5. Z. Zhang, Multivariate Time Series Analysis in Climate and Environmental Research. Artificial Neural Network, pp. 1–35. Springer, Cham (2018) 6. Z. Musakulova, E. Mirkin, E. Savchenko, Synthesis of the backpropagation error algorithm for a multilayer neural network with nonlinear synaptic inputs. in 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), IEEE, pp. 131–135 (2018) 7. T. Nguyen et al., Medical data classification using interval type-2 fuzzy logic system and wavelets. Applied Soft Computing 30, 812–822 (2015) 8. P.A. Phong, K.Q. Thien, Classification of cardiac arrhythmias using interval type-2 TSK fuzzy system. in 2009 International Conference on Knowledge and Systems Engineering, pp. 1–6. IEEE (2009) 9. O.B. Kuznetsova, E.A. Savchenko, A.A. Andryakov, E.Y. Savchenko, Z.A. Musakulova, Image processing in total internal reflection fluorescence microscopy. J. Phys.: Conf. Ser. 1236(1), 012039 (2019) 10. M.H.F. Zarandi, M.R. Faraji, M. Karbasian, Interval type-2 fuzzy expert system for prediction of carbon monoxide concentration in mega-cities. Appl. Soft Comput. 12(5), 291–301 (2012) 11. V.E. Andrade, C.H. Fontes Embiruçu, An interval type-2 fuzzy logic approach for instrument fault detection and diagnosis. in Proceedings of the World Congress on Engineering, vol. 2, pp. 1008–1012 (2011) 12. UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/breast+cancer +wisconsin+(original). Accessed 01 Jun 2020 13. EYu. Savchenko, Innovative approaches to use of neural network technologies for solving the TASKS of adaptive test knowledge control of knowledge. Innov. Act. 3(8), 61–67 (2009) 14. P. Venkatesan, S. Anitha, Application of a radial basis function neural network for diagnosis of diabetes mellitus. Curr. Sci. 91(9), 1195–1199 (2006) 15. A.A. Aidaraliev, O.V. Volkovich, E.L. Mirkin, S.S. Nezhinsky, Neural network prediction of difficult tracheal intubation risk by using the patient’s face image. HERALD North-Western State Medical University named after II Mechnikov 11(3), 23–32 (2019) 16. A. Hajian, P. Styles, Artificial neural networks, in Application of Soft Computing and Intelligent Methods in Geophysics, ed. by A. Hajian, P. Styles (Springer, Cham, 2018), pp. 3– 69 17. Z. Zhang, Artificial neural network, in Multivariate Time Series Analysis in Climate and Environmental Research, ed. by Z. Zhang, K. Zhang, A. Khelifi (Springer, Cham, 2018), pp. 1–35

Chapter 52

Investigation of the Effect of ADC Imperfections on the Amplitude Spectrum Measurement Error for a Quadrature Demodulator Technique Alexander R. Senchenko

and Andrey N. Serov

Abstract Currently, the quadrature demodulation technique is applied to measure the amplitude, phase spectra and frequency of periodic nonsinusoidal signals. Compared to the discrete Fourier transform, this approach allows for a significantly smaller measurement error. The input signal of the quadrature demodulator comes from the analog-to-digital converter (ADC) output. The conversion function of a real ADC is not ideal: there is an offset, multiplicative and nonlinear components of the error, quantization error. All of these features affect the measurement errors of the amplitude spectrum obtained by applying the quadrature demodulator. This article discusses the influence of each of the listed error components on the measurement error of the amplitude spectrum. The ADC nonlinearity is represented as a polynomial function of the third order, in the form of a random function and by the “worst case” method. Analytical expressions have been obtained that make possible to estimate the amplitude measurement error for the considered forms of ADC nonlinearity representation. By using software package Simulink, simulation models of a quadrature demodulator and ADC are performed. A quadrature demodulator with a third-order Butterworth IIR-filter is considered. The reliability of the analytical relationships is confirmed by the coincidence of the results which are obtained by using analytical expressions and the results of simulation at the check points.




Keywords Quadrature demodulator Measurement error Approximation Spectrum Analog-to-digital converter








Nonlinearity


A. R. Senchenko
A. N. Serov (&) National Research University “Moscow Power Engineering Institute”, Krasnokazarmennaya St. 14, 111250 Moscow, Russia e-mail: [email protected] A. R. Senchenko e-mail: [email protected] © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_52

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Introduction

Currently, the most common method for the spectrum measurement of electrical signals is based on the application of discrete Fourier transform (DFT) or on the application of fast Fourier transform (FFT)—a popular algorithm for implementation of DFT [1, 2]. In measurements for the electric power industry, the first three signal harmonics are of the greatest interest. There is no need to measure all harmonics of the signal spectrum, it is enough to measure only the largest spectral components. To solve this problem, following approaches can be used: the direct implementation of the DFT, the implementation of the DFT by using the Goertzel algorithm, the FFT algorithm, or, alternatively, the quadrature demodulation technique method [3, 4]. While the accuracy of the DFT strongly depends on the deviation of the input signal frequency from the nominal value, the quadrature demodulation method allows to reduce the error caused by this frequency deviation by selecting the parameters of the applied digital filter.

52.2

The Application of a Quadrature Demodulator for the Amplitude Spectrum Measurement

52.2.1 Operating Fundamentals The quadrature demodulation method is based on extraction of the complex spectral components from the input signal by multiplying the input signal with orthogonal harmonic signals with a frequency of x0 and subsequent low-pass filtration. If the input signal is a sinusoidal, this product consists of sum of two components: useful signal of frequency (x – x0), and an interference signal of frequency (x + x0). The frequency of the reference signal should be chosen equal to the frequency of the measured signal, then in the absence of a frequency difference, the useful component will be a constant signal, and the interference—a signal with a double frequency. The useful component can be distinguished by applying a low-pass filter (LPF). The output signal of the demodulator is a complex signal that can be used to obtain the amplitude, phase and frequency values of the measured harmonic [3, 4]. To minimize amplitude measurement error, associated with the input signal noise, harmonics and with the frequency deviation from the nominal value, an adaptive averaging unit can be applied. The discrete-time output signal of the demodulator in the case of a sinusoidal input signal is described by the following equation: y_ ½n ¼ Xm ðH1 ejðDxn þ a þ uLP ðDxÞÞ  H2 ejððx1 þ x0 Þn þ a þ uLP ðx1 þ x0 ÞÞ Þ=ð2jÞ;

ð52:1Þ

where Xm—amplitude of the input signal; x—normalized angular frequency of the input signal; x0—normalized angular frequency of the reference signal; n—sample

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479

number; H1, H2—filter magnitude response at frequencies Dx = (x1 – x0) and (x1 + x0); a—initial phase of the signal; uLP(Dx), uLP(x1 + x0)—filter phase response at frequencies Dx and (x1 + x0).

52.2.2 Amplitude Measurement For the purpose of compactness of the subsequent expressions, the following notation will be used:    
   H1 ¼ H ejDx ; H2 ¼ H ejðx1 þ x0 ; ð52:2Þ u1 ¼ Dxn þ a  p=2 þ uLP ðDxÞ;

ð52:3Þ

u2 ¼ ðx1 þ x0 Þn þ a þ p=2 þ uLP ðx1 þ x0 Þ:

ð52:4Þ

Then according to (52.2)–(52.4): y_ ½n ¼ 0:5Xm H1 eju1  0:5Xm H2 eju2 :

ð52:5Þ

The square of the complex modulus y_ ½n is equal to the product of the complex conjugate values of y_ ½n:

 jy_ ½nj2 ¼ 0:25Xm2 H1 eju1  H2 eju2 H1 eju1  H2 eju2 :

ð52:6Þ

After multiplication (52.6), the expression takes the following form:
 jy_ ½nj2 ¼ 0:25Xm2 H12 þ H22  2H1 H2 cosðu1 þ u2 Þ :

ð52:7Þ

By using the last expression, taking into account the inequality H1 >> H2 and approximating the square root by the first two terms of the Taylor series (expansion is performed by H2/H1), the following expression is derived for the module |y[n]|:     Xm H1 1  cos2 ð2x1 n þ hÞ H2 2 H2 1þ jy_ ½nj ¼  cosð2x1 n þ hÞ ; 2 2 H1 H1 ð52:8Þ Where h ¼ 2a þ uLP ðDxÞ þ uLP ðx1 þ x0 Þ. Then the amplitude value of the signal for the case of an ideal LPF can be obtained by using the following expression:

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 Xm ¼ 2jy_ ½nj=H ejDx ;

ð52:9Þ

Then the relative measurement error of the fundamental harmonic amplitude dX is defined as: dX ¼ 0:25ðH2 =H1 Þ2 ð1  cosð4x1 n þ 2hÞÞ  ðH2 =H1 Þ cosð2x1 n þ hÞ:

ð52:10Þ

In the case of an arbitrary value of the initial phase, the maximum value of the amplitude measurement error according to formula (52.9) is determined by the expression: dXm jmax ¼ 0:5ðH2 =H1 Þ2 þ ðH2 =H1 Þ;

ð52:11Þ

52.2.3 Selection of the Characteristics of the Demodulator Output Filter The digital filter at the output of the quadrature demodulator is purposed for ensuring minimal passband ripple for the frequencies at which the useful signal can be located and for interference signal suppression. As was previously considered, in the case of a harmonic signal, it is necessary to suppress the component with the frequency (x1 + x0). For a polyharmonic signal, each multiple harmonic kx1 (where k is integer) will introduce two components with frequencies (kx1 – x0) and (kx1 + x0) of the demodulator output signal spectrum. It follows that the filter should provide maximal suppression ratio at all frequencies that are multiples of the frequency of fundamental harmonic. The characteristics of the applied LPF determinate the amplitude spectrum measurement error. Thus, the filter passband frequency is determined by the maximum deviation of the signal frequency from the nominal value Dx = |x1 – x0|, and the filter stopband frequency—by the minimum frequency of the input signal (x1,nom – Dx) (where x1,nom denotes the nominal value of the angular frequency).

52.2.4 The Method of Reducing of the Amplitude Measurement Error It can be seen from formulas (52.8), (52.10) that the amplitude measurement error contains a constant component and a component that oscillates with a doubled frequency 2x1, although the interference of the demodulator signal, as follows from (52.1), is of a (x1 + x0) frequency. That is, this algorithm for amplitude measurement causes a transformation of the interference frequency.

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When performing averaging, the number of samples of the amplitude must correspond to the value of the signal period, which requires an additional measurement of the frequency of the input signal fmes . This operation can be performed by applying a quadrature demodulator by determining the phase increment according to the following expression [4, 5]: fmes ¼ f1;nom þ fs ðu½n  u½n  1Þ=2p;

ð52:12Þ

where f1,nom—nominal frequency of the measured signal (frequency of the reference signal); fS—sampling frequency; u[n]—the n-th sample of the input signal phase (argument) measurement result by the quadrature demodulator. Then the number of averaged samples L is determined by the formula: L ¼ round ð0:5kfs =fmes Þ;

ð52:13Þ

where round(*)—rounding operation to nearest integer; k—number of the input signal periods being averaged.

52.3

The Influence of ADC Errors on the Amplitude Measurement Error

52.3.1 General Provisions When receiving signal samples, as a result of the imperfections of the ADC (or the entire measurement channel) the following components of the instrumental error occur: additive, multiplicative, nonlinearity and quantization errors. The output signal of the ADC (or the entire measuring channel) can be represented as follows: x½n ¼ DxA þ xid ½ndM þ DxNL ½n þ DxQ ½n;

ð52:14Þ

where DxA—additive component; dM—multiplicative component; DNL[n]—linearity error component (nonlinearity); DQ[n]—quantization error; xid ½n—ideal sample value. Consider the effect of each component on the amplitude measurement error when applying a quadrature demodulator.

52.3.2 Additive Component The additive component of the error does not depend on the value of the input signal being measured, this component is constant in time and in the spectral

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representation corresponds to a DC component (spectral component with zero frequency). The quadrature modulator allows the measurement of only one spectral component with a frequency x1 close to the reference frequency x0. If an ideal output filter is applied, all other spectral components (including the DC component, to which the additive error refers) will be suppressed. For a filter with finite stopband rejection the demodulator output signal error is determined by following equation:   D_y½n ¼ DxA H3 ejðx0 n þ a þ u3 Þ  ejðx0 n þ a þ u3 Þ =ð2jÞ;

ð52:15Þ

where H3—filter magnitude response value at frequency x0; u3—filter phase response value at frequency x0. Then the additional amplitude measurement error of the spectral component being measured is described by the following equation (see expression (52.8)):

  Djy_ ½nj ¼ DxA H3 0:5 1 þ 0:5 1  cos2 ðuLP ððx0 ÞÞÞ  cosðuLP ðx0 ÞÞ :

ð52:16Þ

It can be seen from expression (52.16) that, with the correct choice of filter parameters providing a low H3 value, the influence of the additive component of the ADC error on the amplitude measurement error can be significantly neglected.

52.3.3 Multiplicative Component The multiplicative component of the error is proportional to the value of the input signal. For the case of an ideal demodulator LPF, the amplitude error of the harmonic being measured will correspond to the multiplicative component of the signal samples error. This is due to the fact that an ideal demodulator allows to get the amplitude of the spectral component under measurement. In the case of a real filter, additional error components appear that are similar in nature to the “spectrum leakage” effect for the case of applying DFT. Then the additional measurement error of the amplitude spectrum is determined by the following relation (see expression (52.8)):    dM Xm H1 1  cos2 ð2x1 n þ hÞ H2 2 H2 DM jy_ ½nj ¼ 1þ  cosð2x1 n þ hÞ : 2 2 H1 H1 ð52:17Þ In the first approximation, the second and third terms of expression (52.17) can be excluded in comparison with the first term (second order of smallness errors component).

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Investigation of the Effect of ADC Imperfections …

483

52.3.4 Nonlinearity The linearity error of the ADC is nonlinearly related to the value of the input signal. The main reason for the occurrence of this error component is related to the nonlinearity of the conversion function of the ADC, which is necessarily present in the measurement channel. The presence of nonlinearity of the conversion function in the frequency domain results to two effects: the amplitude values of nonzero spectral components of the input signal are distorted and additional (imaginary) spectral components appear. Obviously, the deviation of the amplitude value of the measured spectral component will directly affects to the measurement error of the amplitude spectrum. To calculate this error component, expression (52.17) can be used. The multiplicative error component must be replaced by amplitude deviation value (of the measured spectral component) caused by the ADC nonlinearity: DNL jy_ ½nj ¼

DXNL H1 2

1þ

   1  cos2 ð2x1 n þ hÞ H2 2 H2  cosð2x1 n þ hÞ : 2 H1 H1 ð52:18Þ

where DXNL—deviation of the amplitude value of the measured spectral component caused by the ADC nonlinearity. As mentioned above, an ideal quadrature demodulator allows to derive the spectral component of the frequency x0. The presence of additional (imaginary spectral components that appeared due to the ADC nonlinearity) for an ideal demodulator will not affect the measurement error of the amplitude value of the considered spectral component. This is caused by the complete suppression of all spectral components whose frequencies are different from x0. For real demodulators, the output filters of which have finite stopband rejection, the presence of imaginary spectral components will result to an addition amplitude measurement error. Simulation results shows that this component of the error can be neglected in comparison with the component caused by the deviation of the fundamental spectral component. Thus, the calculation of the error caused by the ADC nonlinearity is reduced to finding the deviation of the amplitude value of the measured spectral component. Currently, there are a large number of approaches [5–15] for ADC nonlinearity representation: the “worst case” method, representing nonlinearity as multiplicative, using polynomial functions, using random functions and as called “combined” method. Simulation results shows that the largest deviation of the amplitude value is observed when ADC nonlinearity is representing by the “worst case” method [8, 9]:

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A. R. Senchenko and A. N. Serov

DLIN ½n ¼

INL
q; for x½n  0; INL
q; for x½n [ 0:

ð52:19Þ

where INL—ADC integral nonlinearity; q—ADC least significant bit (LSB) value. For the case of a sinusoidal signal, the relative amplitude error [8, 9]: d1;WC ffi ð4
NL
qÞ=ðN
Xm
sinðp=N ÞÞ:

ð52:20Þ

where N—number of samples per period of the input signal. By using a third-order polynomial function is suitable for representing ADC nonlinearity with a smooth nonlinearity curve, for example, for a Sigma-Delta ADC or dual-slope (integrating) ADC [10–13]. The value of the measurement error of the amplitude spectrum is determined by the following expression [10–13]: 2 q
INL
Xref q
INL   d1;P3 ¼ 0:75  Xm3 þ  Xm : 2  X2
X 2  X2
X X Xref z z z z ref

ð52:21Þ

where Xref—ADC reference voltage, determines upper and lower limit of the ADC input range; Xz = Xref/√3—input voltage value for which the maximum value of the ADC nonlinearity is observed. The use of random functions is most suitable for cases where the dependence of the ADC nonlinearity is pseudo-random. This is successive approximation ADCs and pipeline ADCs characteristics [9, 14, 15]. The value of the amplitude spectrum measurement error is determined by the following formula [9]: d1;RF ¼ d1;WC ðB0 þ B1 =fS þ B2 DfS Þ;

ð52:22Þ

where B0 = 0.144; B1 = 587; B2 = −1.6∙10−6 are valid for the fS range 1  20 kHz [9].

52.4

Simulation

Table 52.1 represents the simulation modeling results performed in the Simulink software package. The results are obtained for the case of a quadrature demodulator with a third-order IIR Butterworth filter as an output filter. The filter passband frequency is 10 Hz, the stopband frequency is equal to 45 Hz, the maximum passband attenuation is 0.1%, and the minimum stopband rejection is 0.01. The input signal is sinusoidal, of unit amplitude and of a nominal frequency of 50 Hz. The sampling frequency is chosen equal to 10 kHz. Measurement time is equal to 0.2 s. The results which are represented in the Table 52.1 are the maximum error values obtained in the time interval of 0.1–0.2 s. relative to the start of the

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Table 52.1 The amplitude measurement error caused by the ADC nonlinearity (amplitude error values are given in percent) Nonlinearity representation

Frequency deviation, df, % −0.05 −0.01 0.00

0.01

0.05

Without nonlinearity “Worst case” method Third-order polynomial function Pseudo-random function

0.045 0.061 0.051 0.038

0.039 0.054 0.044 0.039

0.037 0.053 0.043 0.038

0.043 0.059 0.049 0.040

0.041 0.057 0.046 0.041

measurement. The input signal range is chosen equal to the ADC input signal range; ADC has a bipolar input, 14-bit resolution.

52.5

Conclusion

As a result of the research, we can draw the following conclusions. The quadrature demodulation method can be successfully applied to measure the spectrum parameters (amplitude and phase spectrum) as well as to measure the frequency. The analytical relations (52.10), (52.11) are obtained that relate the amplitude measurement error and demodulator output filter parameters. It is shown that the additional error caused by the deviation of the signal frequency can be reduced by additional post-filtration by the adaptive averaging of the amplitude measurement results. It was found that the additive error of the signal samples practically does not affect the amplitude measurement error; the multiplicative error is proportional to the error of the measurement of the amplitude (see relation (52.17)). The influence of the ADC nonlinearity is mainly due to the deviation of the amplitude value of the measured spectral component; error estimates were obtained for the representation of ADC nonlinearity by the “worst case” method, a third-degree polynomial, and a pseudo-random function—see expressions (52.19), (52.20) and (52.21).

References 1. A. Ferrero, R. Ottoboni, High accuracy Fourier analysis based on synchronous sampling techniques. IEEE Trans. Instrum. Meas. 41(6), 780–785 (1992) 2. S.N. Mikhalin, V.M. Gevorkyan, The problems of digital processing of signals in the system of automated power quality control and accounting quantity of electricity (ASQAE). MPEI Vestnik 2005(1), 86–92 (2005) 3. A.N. Serov, N.A. Serov, S.I. Gerasimov, Application of the quadrature demodulation for the measurement of electric power parameters. in Proceedings of 18th International Symposium INFOTEH-JAHORINA, pp. 1–6. IEEE (2019)

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4. A.N. Serov, A.A. Shatokhin, G.V. Antipov, Method to reduce the measurement error of the spectrum by the demodulation technique. in Proceedings of International Conference on Industrial Engineering, Applications and Manufacturing, pp. 1–6. IEEE (2018) 5. IEEE standard for terminology and test methods for analog-to-digital converters IEEE Std 1241–2010 (Revision of IEEE Std 1241-2000), pp. 1 – 139. IEEE (2011) 6. M.S. Yenuchenko, M.M. Pilipko, Reshuffled diagonal rotated walk switching scheme for DAC INL reduction. IEEE Trans. Circ. Syst. II Express Briefs (2020). IEEE 7. K. Kim, Analog-to-digital conversion and harmonic noises due to the integral nonlinearity. IEEE Trans. Instrum. Meas. 43(2), 151–156 (1994). IEEE 8. A.N. Serov, N.A. Serov, E.A. Dolgacheva, comparative analysis of the methods for estimating the measurement error of the amplitude spectrum caused by the ADC nonlinearity. in 18th International Conference on Smart Technologies, pp. 1–6. IEEE (2019) 9. A.N. Serov, N.A. Serov, P.K. Makarychev, Evaluation of the effect of nonlinearity of the successive approximation ADC to the measurement error of RMS. in International Symposium on Industrial Electronics, pp. 1–6. IEEE (2018) 10. P. Suchanek, D. Slepicka, V. Haasz, Experimental verification of different models of the ADC transfer function. in Workshop on ADC Modelling and Testing (2008) 11. P. Suchanek, D. Slepicka, V. Haasz, Several approaches to ADC transfer function approximation and their application for ADC nonlinearity correction. Metrol. Measur. Syst. 15(4), 501–511 (2008). IEEE 12. P. Arpaia, P. Daponte, L. Michaeli, Influence of the architecture on ADC error modeling. IEEE Trans. Instrum. Meas. 48(5), 956–966 (1999). IEEE 13. L. Michaeli, P. Michalko, J. Šaliga, Unified ADC nonlinearity error model for SAR ADC. Measurement 41(2), 198–204 (2008) 14. S. Medawar, P. Handel, N. Bjorsell, M. Jansson, Postcorrection of pipelined analog–digital converters based on input-dependent integral nonlinearity modeling. IEEE Trans. Instrum. Meas. 60(10), 3342–3350. IEEE (2011) 15. S. Medawar, P. Händel, N. Björsell, M. Jansson, Model order determination and segmentation of analog-digital converters integral nonlinearity. in IEEE Instrumentation & Measurement Technology Conference Proceedings, pp. 36–41. IEEE (2010)

Chapter 53

Analysis of the Possibility of Correcting the Shape of the Average Cardiac Complex in the Presence of Synchronization Errors During Accumulation Irina A. Kondratyeva , Alexander S. Krasichkov, Eugene M. Nifontov , and Fabien Shikama

Abstract One of the most common and effective diagnostic methods is registration and analysis of ECG. To assess the functional form of the repeating low-amplitude, noisy cardiac complexes, synchronous accumulation is performed. The application of this method consists in the fact that the electrocardiosignal is divided into cardiocomplexes as close as possible in shape, which are then sequentially superimposed on one another. However, in the accumulation process, due to the presence of interference, the position of the maximum of the R-wave deviates from the true value. In order to obtain a true averaged cardiocomplex, there is a need to correct the shape of the cardiocomplex due to synchronization errors during the accumulation process. The article investigates the fundamental possibility of correcting the shape of the averaged cardiocomplex due to synchronization errors during accumulation. Distortion of the accumulated cardiocomplex can lead to a significant deterioration in the quality of diagnosis. A method is proposed that allows correcting the distortion of the accumulated cardiocomplex associated with synchronization errors.




Keywords Electro cardio signal Synchronous accumulation estimate SNR Synchronization errors








ECG parameters

I. A. Kondratyeva (&)
A. S. Krasichkov
F. Shikama Saint Petersburg Electrotechnical University ‘LETI’, St. Petersburg, Russian Federation e-mail: [email protected] E. M. Nifontov Pavlov First Saint Petersburg State Medical University, St. Petersburg, Russian Federation © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_53

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I. A. Kondratyeva et al.

Introduction

According to the World Health Organization (WHO) death from cardiovascular diseases is taking an estimated 31% and is the most common cause of death worldwide. One of the most popular diagnostic methods for cardiovascular diseases is the long-term electrocardiogram (ECG) recording and subsequent ECG analysis. However, the electrocardiogram is exposed to various interference in the process of monitoring in an active life of a human. The most dangerous interferences are myographic interference (in fact it is a broadband noise) and the isoelectric line drift. At the present time, there is a significant number of methods to eliminate the drift of isoelectric line, for example, using a median filter [1]. Meanwhile, filtering does not eliminate myographic interference [9, 10], since the spectrum of myographic interference overlaps with its useful signal. There is an effective approach for evaluating the functional appearance of repetitive low-amplitude noisy signals. This approach is based on the principle of synchronous accumulation [7]. In modern cardiology the application of this method consists in the fact that the electrocardiogram is divided into cardiocomplexes, which are as close to each other in shape as possible, and then the cardiocomplexes are sequentially superimposed on each other, simultaneously synchronizing with the maximum R-wave [2, 3]. Averaged cardio complexes (prototypes) are displayed on a monitor screen for a doctor-researcher [11–14].

53.2

Methods

For the effective implementation of the proposed approach, it is necessary to ensure the selection of characteristic points that are used to synchronize the accumulation of cardiac complexes. Usually, it is the maximum position of R-wave [2]. However, due to the presence of interference, binding errors occur (deviation of the estimate of the position of the maximum of j-th R wave from the true value by an amount (see Fig. 53.1), which are characterized by the probability density (PD) WR ðsÞ. Taking into account that the accumulated signals due to the use of choice have a small difference in shape Sj ðtÞ ¼ SðtÞ, the average electrocardiogram SR ðtÞ can be M1  P  S t þ sj ; where sj is the error in fixing the position of represented as: SR ðtÞ ¼ M1 j¼0

the j-th cardio complex, M is the number of averaged cardiocomplexes.

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489

Fig. 53.1 Synchronization error due to displacement of cardio complexes

Fig. 53.2 Averaging error due to inaccurate synchronization

Assuming that the errors PD of fixing the position of the R-wave for the j-th cardio complex W ðsÞ are independent of, it can be shown that the average value of the function SR ðtÞ is the result of the convolution of the desired signal SðtÞ and the probability density function [4] mfSR ðtÞg ¼ SðtÞ  W ðsÞ;

ð53:1Þ

where  – the convolution operator, mf
g – symbol of statistical averaging. Figure 53.2 illustrates the distortion of the accumulated cardio complex (black thin line mfSR ðtÞg) due to synchronization errors relative to the desired signal (light thick line SðtÞ). The result was obtained by modeling using a normal model of fluctuations in the position of R-wave. The figure shows significant distortions of the accumulated cardio complex (for example, a change in the amplitude of the R-wave, an increase in the width of the QRS complex), which can affect a significant deterioration in the quality of diagnostics. Therefore, it is necessary to carry out the correction of the accumulated cardio complex. If we analyze expression (53.1), it becomes obvious that in order to find a “true” estimate of the shape of the cardio complex, it is necessary to solve the problem of reverse convolution (deconvolution).

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However, when solving the task of finding the reverse convolution, and actually correcting the accumulated cardiocomplex instead of the determinate function mfSR ðtÞg, the researcher actually has only its estimate ^ SR ðtÞ, obtained by averaging the observed signal realizations ^SR ðtÞ ¼ mfSR ðtÞg þ e ¼ SðtÞ  W ðsÞ þ e;

ð53:2Þ

where e – approximation error. The presence of even a small error can lead to significant deviations of the function obtained as a result of deconvolution (reverse convolution) ^ SðtÞ from the desired one SðtÞ. For this reason, finding reverse convolution with a direct solution is usually not used in practice. The paper [8] proposes a solution to this problem in a discrete form. It was shown that finding estimates ^SðtÞ is possible if information on the structural properties of the electrocardiogram is used. Namely, the possibility of accurate approximation of small fragments of the cardiocomplex in the vicinity of a point by second-order polynomials [5, 6] Sði þ jÞ ¼ C2i j2 þ C1i j þ C0i ;

j 2 ½n  1; n þ 1;

(where 2n þ 1 – polynomial duration, Ci – polynomial coefficients). In this case, the estimation ^SðtÞ is based on the solution of Eq. (53.1), which can n P be written as follows mfSR ðiÞg ¼ aj Sði þ jÞ, where aj is coefficients in the j¼n

expression for the probability density of a discrete random variable n P W ð sÞ ¼ aj dðs  jÞ. j¼n

It has been shown that in the process of analyzing lengthy recordings of an electrocardiogram during prototype formation, a significant number of cardiac complexes are averaged [3]. This allows us to assume that in a first approximation mfSR ðiÞg  ^SR ðiÞ Then, the desired estimate is equal is   ^SðiÞ ¼ ð1 þ aÞ^SR ðiÞ  1 a ^SR ði þ 1Þ þ ^ SR ð i  1Þ ; 2 where a ¼

n P j¼n

ð53:3Þ

j2 aj , which in fact is an assessment correction ^ SR ðiÞ.

Figure 53.3 shows the result of the correction in accordance with the expression (53.3) - thin solid line (“reference” signal - light thick line SðtÞ). However, the result of the correction, although approaching the “true” form of the analyzed cardiocomplex, still contains residual distortions (Fig. 53.3).

53

Analysis of the Possibility …

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Fig. 53.3 The result of correction of the shape of the accumulated signal when using the expression (53.3)

Fig. 53.4 The result of correction of the shape of the accumulated signal when using the expression (53.4)

In this part of the work, new results were added, namely, to increase the accuracy of correction, fourth-order polynomials were used when approximating small fragments of the cardiocomplex in the vicinity of point i Sði þ jÞ ¼ C4i j4 þ C3i j3 þ C2i j2 þ C1i j þ C0i ; j 2 ½n  1; n þ 1 In this case, correction assessment ^SR ðiÞ is based on the following expressions.   ^SðiÞ ¼ ^SR ðiÞ  b B  4A  a A  B  4A ð1 þ 6aÞ ; 12 12 where: A¼

^SR ði þ 1Þ þ ^SR ði  1Þ ^ SR ðiÞ; 2

B¼

^SR ði þ 4Þ þ ^SR ði  4Þ ^ SR ðiÞ; 2

ð53:4Þ

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a¼

n X j¼n

j2 a j ; b ¼

n X

j 4 aj :

j¼n

Figure 53.4 shows the result of the correction in accordance with the expression (53.4) - a thin solid line (“reference” signal - light thick line SðtÞ). The estimate obtained as a result of correction based on the expression (53.4) practically coincides with the desired cardiocomplex (light line). The use of fourth-order polynomials in the description of the signal made it possible to obtain a more accurate correction of the averaged cardiocomplex [8, 15, 16] that especially relevant in tasks aimed at studying the “fine” structure of the electrocardiogram.

53.3

Conclusion

The article investigates the fundamental possibility of correcting the shape of the averaged cardiocomplex due to synchronization errors that occur during accumulation. Distortion of the accumulated cardiocomplex can lead to a significant deterioration in the quality of diagnosis. The process of finding a “true” assessment of the shape of the cardiocomplex does not present any computational difficulties and has the right solution, if there is the possibility of deconvolution of deterministic signals. The search process for a “true” assessment of the shape of the cardiocomplex does not present any computational difficulties and has the right solution, provided that it is possible to perform the operation of deconvolution of the determined signals. However, when it is necessary to solve the problem of correcting the accumulated cardiocomplex instead of the determinate function mfSR ðtÞg, the researcher actually has only its estimate ^SR ðtÞ, which is obtained by averaging the observed signal realizations. The presence of insignificant differences between these functions can lead to significant deviations obtained as a result of the operation of deconvolution of the function ^ Sð t Þ from the desired SðtÞ. A correct solution to the problem of finding estimates ^ SðtÞ is possible if you use information about the structural properties of the electrocardiogram. This work confirms the fundamental possibility of accruing correcting the shape of the averaged cardiocomplex in the presence of synchronization errors.

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References 1. N. Siddiah, T. Srikanth, Y. Satish Kumar, Nonlinear filtering in ECG signal enhancement. Int. J. Computer Sci. Commun. Netw. 2, 134–139 (2012) 2. Rangayyan, R.M. Biomedical signal analysis. IEEE Press, Wiley-Interscienc 2002. 439 p. F. Author, Contribution title. in 9th International Proceedings on Proceedings (2010) 3. A.S. Krasichkov, E.M. Nifontov, V.S. Ivanov, Algorithm for sorting cardiocomplexes for analysis of long recordings of an electrocardiogram. Biomed. Radioeng. 11, 24–28 (2011) 4. A.S. Krasichkov, A.A. Sokolova, Estimation of the cardio signal representation accuary during its synchronous accumulation. J. Russian Univ. Radioelectro. 3, 48–53 (2010) 5. V.V. Pipin, M.V. Ragulskaya, S.M. Chibisov, The analysis of dynamic models and reconstruction of an electrocardiogram at influence cosmo- and geographysical. Int. J. Appl. Fundaminational Res. 5, 17–24 (2009) 6. S. Lee, Y. Jeong, D. Park, B.-J. Yun, K. Park, Efficient fiducial point detection of ECG QRS complex based on polygonal approximation. Sensors 18(12), 4502 (2018) 7. J.M. O’Connor, P.H. Pretorius, K. Johnson, M.A. King, A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list-mode acquisitions. Med. Phys. 40(12), 122502 (2013) 8. I.A. Kondratyeva, A.S. Krasichkov, Analysis of the possibility of correcting the shape of the average cardiac complex in the presence of synchronization errors during accumulation Collection of reports of the 75th scientific and technical conference St. Petersburg NTO RES named after A.S. Popov, dedicated to Radio Day (2020) 9. A.A. Fedotov, Myographic interference filtering from ECG signals using multiresolution wavelet transform. Biomed. Eng. 52, 344–347 (2019) 10. S. Poungponsri, X.H. Yu, An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing 117, 206–213 (2013) 11. J.L. Ma, T.T. Zhang, M.C. Dong, A Novel ECG data compression method using adaptive fourier decomposition with security guarantee in e-health applications. IEEE J. Biomed. Health Inform. 19(3), 986–994 (2015) 12. A. Kumar, M. Singh, Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes. Trans. Inst. Measur. Control 40(3), 819–830 (2016) 13. A.S.G. Kindi, T. Reza, Real-time detection of myocardial infarction by evaluation of ST-segment in digital ECG. J. Med. Imaging Health Inform. 1(1), 225–230 (2011) 14. F. Castells, P. Laguna, L. Sörnmo, A. Bollmann, J.M. Roig, Principal component analysis in ECG signal processing. EURASIP J. Adv. Signal Process. 2007, 074580 (2007) 15. S. Pathoumvanh, K. Hamamoto, P. Indahak, Arrhythmias detection and classification base on single beat ECG analysis. in The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE) (2014) 16. K. Balaskas, K. Siozios, ECG analysis and heartbeat classification based on shallow neural networks. in 2019 8th International Conference on Modern Circuits and Systems Technologies (2019)

Chapter 54

Glucose Variability in Gestational Diabetes Patients with Different Glycemic Goals Evgenii Pustozerov, Nikol Sachkova, Aleksandra Tkachuk, Elena Vasukova, Aleksandra Dronova, Tatiana Pervunina, Elena Grineva, and Polina Popova

Abstract Glycemic variability plays a crucial role in estimating the risk of diabetes complications, defining glycemic goals and evaluating the success of treatment in diabetes patients of all types. Yet there are many of shallowly studied and promising indexes depicting different characteristics of signals acquired from continuous glucose monitoring (CGM) systems. We analyzed various glycemic variability measures, including CONGA, LI, J-Index, LBGI, HBGI, GRADE, M-value, MODD, MAGE and ADRR among others, and evaluated them on a set of 247 weekly CGM records from gestational diabetes (GDM) patients and healthy pregnant women. In our study GDM patients were divided into two groups according to glycemic targets: tight control (7.0 mmol/L after meal, 5.1 mmol/L fasting) in the first group and less tight control (7.8 mmol/L after meal, 5.3 mmol/L fasting) in the second. The study has shown how various glucose variability indexes and features of CGM signals differ in GDM patients with different treatment goals and in healthy pregnant women. Keywords Gestational diabetes mellitus Glycemic variability Glycemic targets





Continuous glucose monitoring


E. Pustozerov (&)
N. Sachkova Saint Petersburg State Electrotechnical University, 197376 Saint Petersburg, Russia e-mail: [email protected] E. Pustozerov
A. Tkachuk
E. Vasukova
A. Dronova
T. Pervunina
E. Grineva
P. Popova Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia P. Popova Pavlov First Saint Petersburg State Electrotechnical University, 197022 Saint Petersburg, Russia © Springer Nature Switzerland AG 2021 E. Velichko et al. (eds.), International Youth Conference on Electronics, Telecommunications and Information Technologies, Springer Proceedings in Physics 255, https://doi.org/10.1007/978-3-030-58868-7_54

495

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E. Pustozerov et al.

Introduction

Glucose variability is the key measure that helps to evaluate the effectiveness of diabetes compensation. While there are many different measures developed for both discrete blood glucose (BG) measurements made with glucometers and for continuous glucose monitoring (CGM) only time in range (TIR), time above range and time below range are known to be widely accepted and used characteristics [1]. While there is a variety of different glucose variability metrics proposed in literature, there is still not that much of an evidence of utilization of these features in estimating different aspects of BG profiles in type 1, type 2 diabetes and especially gestational diabetes mellitus (GDM). GDM is currently among the most common endocrine disorders during gestation. Maintaining normal blood glucose (BG) levels during pregnancy is critical for preventing adverse pregnancy outcomes. In our study we investigated a number of the most common parameters of glucose variability for women with GDM with different glycemic targets compared to healthy women.

54.2

Materials and Methods

The participants of the study were pregnant women with GDM and pregnant women with normal glucose tolerance (control group) who took part in the GEM-GDM randomized controlled trial (Genetic and Epigenetic Mechanisms of Developing Gestational Diabetes Mellitus and Its Effects on the Fetus), met the inclusion criteria and gave their informed consent to participate in the present study. The study was conducted between November 2015 and April 2020 in the Almazov National Medical Research Centre (ANMRC). The description of the parent study protocol can be found elsewhere [2]. Pregnant women were included into GDM group if they were diagnosed with GDM according to the WHO 2013 criteria [3], were 18–45 years old and their gestational age was : HE0 p 2 2 zE0G ¼ 2 cosðhE Þ þ a  b sin n
180

ð17Þ

where HE0 is the geomagnetic field on the planet surface; hE , uE —defining the angles of the geomagnetic field on the planet surface; a and b are the semi-major and semi-minor axes of the elliptical orbit, respectively; n is the communication satellite inclination angle to the equatorial plane; xE0G , yE0G , zE0G are coordinates of one defining points G Earth’s magnetic field vector. The coordinates of second point O will be with opposite signs, that is xE0O ¼ xE0G , yE0O ¼ yE0G , zE0O ¼ zE0G . Then the geomagnetic field vector ! GO will have the following coordinates: ! GO f2xE0G ; 2yE0G ; 2zE0G g:

ð18Þ

In some sources you can find information that the Earth’s magnetic field axis doesn’t pass through the planet center, but is close enough to it, which can be neglected. Knowing the geomagnetic field vector points coordinates, its motion is calculated during the observation time due to the rotation of the planet around its

770

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axis. Obviously, the satellite travels at different intervals. It was previously said that the program discretized the path by an eccentric anomaly. Knowing this parameter, one can also find the elapsed time between adjacent discrete points in orbit: h i p t ¼ E þ  eðsinðEÞ þ 1Þ 2

sffiffiffiffiffi a3 ; l

ð19Þ

where t is the time taken for a satellite to travel between two sampling points in its orbit; E is an eccentric anomaly; a – semi-major axis; e is the elliptical orbit eccentricity; l is the gravitational parameter, which is defined as l ¼ GME (G is the gravitational parameter, and ME is the Earth mass). Now, as t is known, the program algorithm finds the new points coordinates G0 and O0 of the geomagnetic vector, where the points G и O will be shifted, respectively: 1. for the point G0 :  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  8 0 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   p > xE0G ¼ x2E0G þ y2E0G cos p2 þ T2pE t  a2  b2 cos n
180  > <  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  0 p 2p 2 2 ; y ¼ x þ y þ t sin E0G E0G E0G 2 T > E > pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   : 0 p zE0G ¼ zE0G þ a2  b2 sin n
180  0

0

ð20Þ

0

where TE is the Earth’s rotation period; xE0G , yE0G , zE0G new point G0 coordinates; xE0G , yE0G , zE0G is the point G coordinates. 2. For the point O0 :  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  8 0 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   p 2p p 2 2 > x ¼ x þ y þ p þ t  a2  b2 cos n
180 cos  > E0O E0O E0O 2 TE <  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  0 ; yE0O ¼ x2E0O þ y2E0O sin p2 þ p þ T2pE t > > pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   : 0 p zE0O ¼ zE0O þ a2  b2 sin n
180  0

0

ð21Þ

0

where TE is the Earth rotation period; xE0O , yE0O , zE0O —new point O0 coordinates; xE0O , yE0O , zE0O – point O coordinates. ! The vector G0 O0 has the following coordinates: o !n 0 0 0 0 0 0 G0 O0 xE0G  xE0O ; yE0G  yE0O ; zE0G  zE0O :

ð22Þ

Figure 83.3 shows that over time (the communications satellite has almost passed its orbital period) the geomagnetic vector has shifted (points G and O have moved to G0 and O0 , respectively).

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Fig. 83.3 The motion of geomagnetic field vector

83.2.4 Finding an Orientation Frequency Shift The program has the coordinates of two vectors: the working magnetic field vector ! MS FS fxFS  xMS ; yFS  yMS ; zFS  zMS g and the geomagnetic field vector on the

! 0 0 0 0 0 0 planet’s surface G0 O0 xE0G  xE0O ; yE0G  yE0O ; zE0G  zE0O at time t (formula (19)). Next, the program needs to calculate the frequency light shift component  ! 0 0 values, for this you need to calculate the coordinates GW OW —a geomagnetic vector, which is weakened by the distance from the planet center and magnetic shielding.  ! 0 0 The vector GW OW is searched to two steps. 8 ! ! d ! > 0 0  G0 O0 H ; G >  ! > O
MS FS 6¼ p2 l >   > 0 0 < G O k  ! 0 0 ; GW OW ¼ ! ! d ! p > > 0 0  G0 O0 H ; G >  !
M O F ¼ l S S > 2 > : G0 O0 k þ

ð23Þ

!d! where G0 O0
MS FS is the angle between the corresponding vectors;k þ and k are the transverse and longitudinal shielding coefficients; Hl is an approximated

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function that depends on the distance l between the planet center and the communication satellite [19, 20]: Hl ¼ Hl1 þ Hl2  Hl3 ; 32 8 ffi; Hl1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; where Hl1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 ½0:6
ð15000lÞ þ 1000000

½22000l þ 1000000

9 ffi. Hl3 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ½0:5
ð20500lÞ þ 1000000

To calculate the light shift components, the program uses the formula: 2 ! 3 13 0 0 ! ! GW OW þ MS FS
OA B 6 7 C7 6 Dv 7C7;    ¼ R6 1  3cos2 B arccos6  @ 4 4  !  0 0 v !  ! 5A5 G O þ M S FS 
 OA   W W 2

0

ð24Þ

where R is the weight coefficient, depending on the intensity and spectral compo! ! ! sition of the pump source; OA is optical axis ( OA ¼ MS FS ).

83.2.5 Algorithm Results The communication satellite orbit shape was set by a constant angle of inclination n ¼ 54:6 (as with the GPS satellite) and variations of the semi-major axis a and eccentricity e. Then the program result is to obtain the dependences Dv v ða; t Þ and Dv v ðe; tÞ, which are presented in Fig. 83.4 and Fig. 83.5 respectively.

Fig. 83.4 The dependence of the frequency shift on time and semi-major axis of elliptical orbit of the communication satellite

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Fig. 83.5 The dependence of the frequency shift on time and eccentricity of elliptical orbit of the communication satellite

Figure 83.4 и Fig. 83.5 show that the frequency shift Dv v increases with increasing values of the eccentricity e and the semi-major axis a. Also, the frequency shift has a periodic along the axis of the time at the moment when  !increase 0 0 ! ! the angle between GW OW þ MS FS and OA is the largest.

83.3

Conclusion

As a result of modeling to determine the fluctuations effect in the Earth geomagnetic field on the orientation error of a communication satellite, an algorithm has been developed and software implemented, which includes the following steps: setting the working magnetic field vector initial coordinate; calculation of the working magnetic field vector direction at different points of the orbit; calculation of the Earth’s magnetic axis rotation over time; finding the Dv v (atomic clock frequency shift). The influence of the geomagnetic field orientation is considered for an onboard atomic clock containing a cell with rubidium atoms and a buffer gas. The use of a cell with a walls anti-relaxation coating, which ensures atomic clock miniaturization, will lead to a significant increase in the orientation dependence, which makes the topic of research presented in this article even more relevant. The developed algorithm makes it possible to determine the onboard atomic clock frequency shift for communication satellite’s orbit frequency shift. It can be seen that the more the elliptical orbit departs from the circular in shape, the greater the atomic clock frequency shift, which leads to an increase in the error in determining the ephemeris of communication satellites.

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Acknowledgements This work was carried out with the help of a grant from the Russian Science Foundation № 20-19-00146.

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