Sensors, Circuits & Instrumentation Systems 9783110470444, 9783110468199

Citation preview

Olfa Kanoun, Faouzi Derbel and Nabil Derbel (Eds.) Sensor, Circuits and Instrumentation Systems

Advances in Systems, Signals and Devices

|

Edited by Olfa Kanoun, University of Chemnitz, Germany

Volume 2

Sensor, Circuits and Instrumentation Systems | Edited by Olfa Kanoun, Faouzi Derbel and Nabil Derbel

Editors of this Volume Prof. Dr.-Ing. Olfa Kanoun Technische Universität Chemnitz Chair of Measurement and Sensor Technology Reichenhainer Strasse 70 09126 Chemnitz [email protected]

Prof. Dr.-Eng. Nabil Derbel University of Sfax Sfax National Engineering School Control & Energy Management Laboratory 1173 BP, 3038 SFAX, Tunisia [email protected]

Prof. Dr.-Ing. Faouzi Derbel Leipzig University of Applied Sciences Chair of Smart Diagnostic and Online Monitoring Wächterstrasse 13 04107 Leipzig, Germany [email protected]

ISBN 978-3-11-046819-9 e-ISBN (PDF) 978-3-11-047044-4 e-ISBN (EPUB) 978-3-11-046849-6 Set-ISBN 978-3-11-047045-1 ISSN 2365-7493 e-ISSN 2365-7507 Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2017 Walter de Gruyter GmbH, Berlin/Boston Typesetting: Konvertus, Haarlem Printing and binding: CPI books GmbH, Leck ♾ Printed on acid-free paper Printed in Germany www.degruyter.com

Preface of the Volume Editor The second volume of the Series “Advances in Systems, Signals and Devices” (ASSD), contains peer reviewed international scientific articles devoted to the field of sensors, circuits and instrumentation systems. The scope of the volume encompasses all aspects of research, development and applications of the science and technology in these fields. The topics include sensors and measurement systems, optical sensors, chemical sensors, mechanical sensors, inductive sensors, capacitive sensors, microsensors, thermal sensors, biomedical and environmental sensors, fexible sensors, nano sensors, micro electronic systems, nano systems and nano technology, sensor signal processing, sensor interfaces, modeling, data acquisition, multi sensor data fusion, distributed measurements, device characterization and modeling, custom and semicustom circuits, analog circuit design, low-voltage, low-power VLSI design, circuit test, packaging and reliability, impedance spectroscopy, wireless sensors, wireless interfaces, wireless sensor networks, energy harvesting, circuits and systems design. These fields are addressed by a separate volume of the series. All volumes are edited by a special editorial board made up by renowned scientist from all over the world. Authors are encouraged to submit novel contributions which include results of research or experimental work discussing new developments in the field of sensors, circuits and instrumentation systems. The series can be also addressed for editing special issues for novel developments in specific fields. Guest editors are encouraged to make proposals to the editor in chief of the corresponding main field. The aim of this international series is to promote the international scientific progress in the fields of systems, signals and devices. It provides at the same time an opportunity to be informed about interesting results that were reported during the international SSD conferences. It is a big pleasure of ours to work together with the international editorial board consisting of renowned scientists in the field of sensors, circuits and instrumentation systems. The Editors Olfa Kanoun, Faouzi Derbel and Nabil Derbel

Advances in Systems, Signals and Devices Series Editor: Prof. Dr.-Ing. Olfa Kanoun Technische Universität Chemnitz, Germany. [email protected]

Editors in Chief: Systems, Automation & Control Prof. Dr.-Eng. Nabil Derbel ENIS, University of Sfax, Tunisia [email protected]

Power Systems & Smart Energies Prof. Dr.-Ing. Faouzi Derbel Leipzig Univ. of Applied Sciences, Germany [email protected]

Communication, Signal Processing & Information Technology Prof. Dr.-Ing. Faouzi Derbel Leipzig Univ. of Applied Sciences, Germany [email protected]

Sensors, Circuits & Instrumentation Systems Prof. Dr.-Ing. Olfa Kanoun Technische Universität Chemnitz, Germany [email protected]

Editorial Board Members: Systems, Automation & Control Dumitru Baleanu, Çankaya University, Ankara, Turkey Ridha Ben Abdennour, Engineering School of Gabès, Tunisia Naceur Benhadj, Braïek, ESSTT, Tunis, Tunisia Mohamed Benrejeb, Engineering School of Tunis, Tunisia Riccardo Caponetto, Universita’ degli Studi di Catania, Italy Yang Quan Chen, Utah State University, Logan, USA Mohamed Chtourou, Engineering School of Sfax, Tunisia Boutaïeb Dahhou, Univ. Paul Sabatier Toulouse, France Gérard Favier, Université de Nice, France Florin G. Filip, Romanian Academy Bucharest Romania Dorin Isoc, Tech. Univ. of Cluj Napoca, Romania Pierre Melchior, Université de Bordeaux, France Faïçal Mnif, Sultan qabous Univ. Muscat, Oman Ahmet B. Özgüler, Bilkent University, Bilkent, Turkey Manabu Sano, Hiroshima City Univ. Hiroshima, Japan Abdul-Wahid Saif, King Fahd University, Saudi Arabia José A. Tenreiro Machado, Engineering Institute of Porto, Portugal Alexander Pozniak, Instituto Politecniko, National Mexico Herbert Werner, Univ. of Technology, Hamburg, German Ronald R. Yager, Mach. Intelligence Inst. Iona College USA Blas M. Vinagre, Univ. of Extremadura, Badajos, Spain Lotfi Zadeh, Univ. of California, Berkeley, CA, USA

Power Systems & Smart Energies Sylvain Allano, Ecole Normale Sup. de Cachan, France Ibrahim Badran, Philadelphia Univ., Amman, Jordan Ronnie Belmans, University of Leuven, Belgium Frdéric Bouillault, University of Paris XI, France Pascal Brochet, Ecole Centrale de Lille, France Mohamed Elleuch, Tunis Engineering School, Tunisia Mohamed B. A. Kamoun, Sfax Engineering School, Tunisia Mohamed R. Mékidèche, University of Jijel, Algeria Bernard Multon, Ecole Normale Sup. Cachan, France Francesco Parasiliti, University of L’Aquila, Italy Manuel Pérez,Donsión, University of Vigo, Spain Michel Poloujadoff, University of Paris VI, France Francesco Profumo, Politecnico di Torino, Italy Alfred Rufer, Ecole Polytech. Lausanne, Switzerland Junji Tamura, Kitami Institute of Technology, Japan

Communication, Signal Processing & Information Technology Til Aach, Achen University, Germany Kasim Al-Aubidy, Philadelphia Univ., Amman, Jordan Adel Alimi, Engineering School of Sfax, Tunisia Najoua Benamara, Engineering School of Sousse, Tunisia Ridha Bouallegue, Engineering School of Sousse, Tunisia Dominique Dallet, ENSEIRB, Bordeaux, France Mohamed Deriche, King Fahd University, Saudi Arabia Khalifa Djemal, Université d’Evry, Val d’Essonne, France Daniela Dragomirescu, LAAS, CNRS, Toulouse, France Khalil Drira, LAAS, CNRS, Toulouse, France Noureddine Ellouze, Engineering School of Tunis, Tunisia Faouzi Ghorbel, ENSI, Tunis, Tunisia Karl Holger, University of Paderborn, Germany Berthold Lankl, Univ. Bundeswehr, München, Germany George Moschytz, ETH Zürich, Switzerland Radu Popescu-Zeletin, Fraunhofer Inst. Fokus, Berlin, Germany Basel Solimane, ENST, Bretagne, France Philippe Vanheeghe, Ecole Centrale de Lille France

Sensors, Circuits & Instrumentation Systems Ali Boukabache, Univ. Paul, Sabatier, Toulouse, France Georg Brasseur, Graz University of Technology, Austria Serge Demidenko, Monash University, Selangor, Malaysia Gerhard Fischerauer, Universität Bayreuth, Germany Patrick Garda, Univ. Pierre & Marie Curie, Paris, France P. M. B. Silva Girão, Inst. Superior Técnico, Lisboa, Portugal Voicu Groza, University of Ottawa, Ottawa, Canada Volker Hans, University of Essen, Germany Aimé Lay Ekuakille, Università degli Studi di Lecce, Italy Mourad Loulou, Engineering School of Sfax, Tunisia Mohamed Masmoudi, Engineering School of Sfax, Tunisia Subha Mukhopadhyay, Massey University Turitea, New Zealand Fernando Puente León, Technical Univ. of München, Germany Leonard Reindl, Inst. Mikrosystemtec., Freiburg Germany Pavel Ripka, Tech. Univ. Praha, Czech Republic Abdulmotaleb El Saddik, SITE, Univ. Ottawa, Ontario, Canada Gordon Silverman, Manhattan College Riverdale, NY, USA Rached Tourki, Faculty of Sciences, Monastir, Tunisia Bernhard Zagar, Johannes Kepler Univ. of Linz, Austria

Contents Preface of the Volume Editor | V S. Kehrberg, P. Wellner, C. Geckeler and J. Mehner Modal Analysis of MEMS Using Ultrasonic Base Excitation | 1 M. Saihi, B. Boussaid, A. Zouinkhi and M.N. Abdelkrim Decentralized Fault Detection in Wireless Sensor Network based on gaussian function error | 13 B. Mezghani, F. Tounsi and M. Masmoudi Static Behavior Analytical and Numerical Analysis of Micromachined Thermal Accelerometers | 27 M. Hadj Said, F. Tounsi, P. Gkotsis, M. Masmoudi and L. A. Francis MEMS-Based Clamped-Clamped Beam Resonator Capacitive Magnetometer | 47 G.U. Gamm and L.M. Reindl Range Extension for Single Hop Wireless Sensor Networks with Wake-up Receivers | 63 A. Ghorbel, M. Jallouli, L. Amouri and N. Ben Amor A HW/SW Implementation on FPGA of Absolute Robot Localization Using Webcam Data | 75

S. Kehrberg, P. Wellner, C. Geckeler and J. Mehner

Modal Analysis of MEMS Using Ultrasonic Base Excitation Abstract: We developed a new test facility based on the principle of piezoelectric base excitation in order to meet the requirements for modal analysis of a micromachined angular rate sensor at very high frequencies. A combination of a 0.5 mm thin piezoelectric disc, a seismic mass and a damping element mounted in a vacuum chamber results in a linear frequency response over a frequency range from 10 kHz to 200 kHz. Furthermore, the test facility can be used for finding eigenmodes of microelectromechanical systems (MEMS) up to 1 MHz. Keywords: MEMS, Modal analysis, Ultrasonic excitation, Piezoelectric. Mathematics Subject Classification 2010: 65C05, 62M20, 93E11, 62F15, 86A22

1 Introduction A typical MEMS, like an angular rate sensor or accelerometer, can be described as a coupled spring-mass system. Modal frequencies of MEMS can be measured more precisely than geometry parameters. Therefore, the preferred way for model validation and parameter extraction of such systems is the experimental modal analysis [1]. Micromachined structures with widths of a few micrometers typically have eigenfrequencies above 100 kHz. For MEMS the excitation is often done electrostatically due to the easier implementation and wide bandwidth [2]. In this case, the measured frequency response includes nonlinearities of the electrostatic forces and crosstalk effects [3]. If the interest is in a pure mechanical analysis of a MEMS device, including mechanical nonlinearities, vibration excitation is needed. For this analysis, a shaker with a clean out-of-plane movement, without resonance frequencies in the interested bandwidth, is required to exclude nonlinearity effects from the base excitation. Standard measurement equipments (e. g. electromagnetic shakers) for macro scale devices are not entirely suitable for this task since they lack the appropriate bandwidth. References [4, 5] already showed the capability of thicker piezoelectric cuboids/discs as out-of-plane shakers for lower frequencies up to respectively 100 kHz and 50 kHz.

S. Kehrberg, P. Wellner, C. Geckeler: Automotive Electronics, Robert Bosch GmbH, D-72762 Reutlingen, Germany, email: [email protected]. J. Mehner: Department of Microsystems and Precision Engineering, Chemnitz University of Technology, D-09107 Chemnitz, Germany De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 2, 2017, pp. 1–11. DOI 10.1515/9783110470444-001

2 | S. Kehrberg et al.

The developed test facility described in this paper uses a thinner piezoelectric disc and a different substructure to allow measurements in the range from 10 up to 1000 kHz. This gives the opportunity to analyze more modes and leads to a more precise parameter extraction. A wider bandwidth is especially important to meet the trend of smaller MEMS with higher eigenfrequencies [6].

Fig. 1. Angular rate sensor glued on piezoelectric disc.

2 Test Facility 2.1 Piezoelectric Disc The developed test facility shown in Fig. 1 employs a low cost PRYY+ 0226 piezoelectric disc (10 mm diameter, 0.5 mm thickness) made by PI Ceramic GmbH. The actuator disc is made of a soft lead zirconate titanate called PIC 255. The wrapped electrode option is used for connecting both sides of the piezoelectric disc from the top. Electrical connections are made via enameled copper wires. Loctite 401 (cyanoacrylate adhesive) is used for gluing the micromachined angular rate sensor onto the piezoelectric disc. The elongation of the piezoelectric disc and thereby the displacement of the whole chip is approximately proportional to the applied voltage V over time t due to the indirect piezoelectric effect [7]. Therefore, velocity and acceleration increase in an ideal case

Modal Analysis of MEMS Using Ultrasonic Base Excitation

| 3

over frequency f as shown in (1) – (3) for a sinusoidal voltage. displacement ∝ V sin(2πft) velocity ∝ 2πfV cos(2πft) 2

acceleration ∝ −(2πf ) V sin(2πft)

(1) (2) (3)

2.2 Seismic Mass The law of conservation of momentum (4) connects the velocities on both sides of the actuator [4]. Consequently a seismic mass, which acts as counterweight with a mass mMass and a velocity vMass , must be applied on the other side of the disc in order to amplify the velocity vChip of the chip with the mass mChip . mChip vChip = −mMass vMass vChip m = − Mass vMass mChip

(4) (5)

In contrast, the thickness mode resonant frequency of the seismic mass must be as high as possible to realize the desired monotonic behavior. We have chosen a small steel plate (25 × 25 × 2 mm3 ) as counterweight. The mass of the steel plate is factor 400 higher than the mass of the sensor chip. As can be seen in (5), the velocity of the chip is therefore factor 400 higher than the velocity of the seismic mass . The thickness mode frequency fth of the seismic mass can be calculated with (6) where c is the speed of sound and t the thickness of the plate [8]. Accordingly, the calculated thickness mode frequency of the used steel plate is above 1 MHz. fth =

c 2t

(6)

The piezoelectric disc is glued with Kapton (polyimide film) tape and Loctite 401 on the steel plate. Kapton tape can be removed and therefore allows the reuse of the steel plate. Since the tape is 50 μm thin and stiff, it adds no extra damping to the system. Hi-Bond 4100C (double-sided adhesive foamed acrylic tape, 1 mm thick) is used for gluing the steel plate on a cylindrical aluminum plate (10 mm thick, 100 mm diameter). The viscoelastic tape uncouples the aluminum base plate from high frequency vibrations at the steel plate caused by the piezoelectric disc. Measurements showed that the double-sided tape has eigenfrequencies below 2 kHz. For higher frequencies the rubbery tape acts as damper. Fig. 2 gives a summary over all used layers.

4 | S. Kehrberg et al. Device under test Superglue Piezo actuator Superglue Kapton tape

Steel plate Double-sided adhesive tape

Aluminum plate

Fig. 2. Sketch of the different layers of the test facility.

2.3 Vacuum Chamber and Measurement System The whole test facility is mounted in a vacuum chamber. An ambient pressure of 1 mbar inside the vacuum chamber is applied to reduce damping and hence increase the displacement of the micromachined angular rate sensor. The vacuum chamber itself is then mounted on a positioning table under a Polytec MSA500 Micro System Analyzer. The whole measurement system is mounted on an air-cushioned table to damp environment frequencies. We used the integrated scanning vibrometer for out-of-plane measurements and the stroboscopic video microscopy for in-plane motion detection. The built-in frequency generator connected to the piezoelectric disc provides a maximum peak to peak voltage of 20 V.

3 Angular Rate Sensor A silicon surface micromachined Bosch angular rate sensor [9] is used to show the capability of the developed test facility. The sensor shown in Fig. 3 is a capacitive type Coriolis vibratory gyroscope. The sensing element consists of two identical spring-mass structures connected by the coupling spring presented in Fig. 4. A Bosch foundry process with a thickness of ∼ 11 μm is used for the sensor. As the angular rate sensor is made for automotive safety applications, such as the electronic stability program, it is very robust against vibration. Accordingly, the first mode can be seen at a high frequency of ∼ 15 kHz. Fabrication tolerances lead to a minimal misalignment of the sidewall angles of surface micromachined structures, resulting in the springs having asymmetric profiles which lead to quadrature effects [10–12]. Quadrature enables excitation of in-plane modes with an out-of-plane force. Therefore, we can analyze all modes in the range up to 1 MHz with our developed test facility. Furthermore, the quadrature allows detecting in-plane modes because they will also appear in the out-of-plane frequency response function.

Modal Analysis of MEMS Using Ultrasonic Base Excitation

| 5

Fig. 3. Bosch angular rate sensor with two identical spring-mass structures.

Fig. 4. Coupling spring between the two masses.

4 Results 4.1 Behavior of the Test Facility We measured the frequency response function of the test facility using a periodic chirp signal in the range from 10 kHz to 1000 kHz with constant peak voltage. A laser Doppler vibrometer was focused on the substrate of the angular rate sensor. The measured frequency response function (Fig. 5) shows up to 200 kHz neither in acceleration nor phase a significant deviation from the desired monotonic behavior. Therefore, the test facility can be used in this range for analyzing mechanical nonlinearities of MEMS.

6 | S. Kehrberg et al.

Furthermore, the test bench can be utilized to find eigenmodes in the range up to 1 MHz. The frequency response function shows in this range significant eigenmodes of the test facility. These resonance peaks have low quality factors of less than 50 at 1 mbar. The eigenmodes of the angular rate sensor have at the same pressure and frequency at least one order of magnitude higher quality factors. Consequently it is easily possible to distinguish between eigenmodes of the test facility and eigenmodes of the device under test.

1e9 Acceleration (a.u.) 1e7 1e5 1e3 1e1 1e–1

0

180

200

400

600

800

1000

400 600 800 Frequency (kHz)

1000

Phase (°)

90 0 –90 –180 0

200

Fig. 5. Bode plot of the measured out-of-plane acceleration.

1e3

Magnitude (a.u)

1e–1

1e–5

0

200

400 600 Frequency (kHz)

800

1000

Fig. 6. Bode plot of the measured out-of-plane displacement of the coupling spring.

Modal Analysis of MEMS Using Ultrasonic Base Excitation

| 7

4.2 Analysis of the Angular Rate Sensor For showing the capability of the developed facility we measured the Bode plot of the coupling spring employing the laser Doppler vibrometer which is presented in Fig. 6. We used a periodic chirp as input signal. After this measurement, we utilized a single frequency sinusoidal voltage to stimulate only one eigenmode at a time. The laser Doppler vibrometer was now used in scanning mode over ∼100 equidistant points along the coupling spring. Afterwards, the deflection of the whole structure was calculated by the Polytec PSV Software. As examples of the different measured out-of-plane modes below 200 kHz (measurements without significant modes of the test facility), Fig. 7 presents a symmetric mode and Fig. 8 an asymmetric mode.

Fig. 7. Out-of-plane mode of the coupling spring at ∼ 115 kHz.

Fig. 8. Out-of-plane mode of the coupling spring at ∼ 190 kHz.

8 | S. Kehrberg et al.

The frequency response function presented in Fig. 6 also includes in-plane modes due to the quadrature effect. This makes it simpler to find in-plane modes but increases the risk to confuse in-plane with out-of-plane modes. Thus we analyzed different modes using stroboscopic video microscopy afterwards to clearly identify in-plane modes. Fig. 9 presents a measured asymmetric in-plane mode at 198 kHz. The capability of measuring modes in the range up to 1 MHz can be seen in Fig. 6. The presented frequency response function includes a large number of resonance peaks of the coupling spring which can be clearly distinguished from the eigenmodes of the test facility by comparing quality factors. An example of a measured eigenmode in this frequency range can be seen in Fig. 10.

Fig. 9. In-plane mode of the coupling spring at ∼ 198 kHz.

Fig. 10. Out-of-plane mode of the coupling spring at ∼ 704 kHz.

Modal Analysis of MEMS Using Ultrasonic Base Excitation

| 9

5 Conclusion The 0.5 mm thin piezoelectric disc, in combination with a seismic mass and a damping layer, is appropriate for base excitation in the range between 10 and 1000 kHz. We employed the presented test facility for the modal analysis of a Bosch angular rate sensor and showed the capability of the new shaker setup. The main advantage of the presented facility is, besides its wide bandwidth, the monotonic frequency response function without significant deviations in the range from 10 kHz to 200 kHz. In consequence, it is qualified for analysis of mechanical nonlinearities of MEMS. The ability of performing mechanical stimulations and optical measurements up to 1 MHz allows to precisely characterize almost all kinds of MEMS. Acknowledgment: The authors thank all colleagues at Robert Bosch Automotive Electronics, Engineering Sensor Technology, for their valuable contributions to this work.

Bibliography [1] [2]

[3] [4]

[5]

[6] [7] [8] [9]

A. W. Phillips and R. J. Allemang. An overview of MIMO-FRF excitation/averaging/processing techniques. Journal of sound and vibration, 262(3):651–675, 2003. J. E. Massad, H. Sumali, D. S. Epp, and C. W. Dyck. Modeling, simulation, and testing of the mechanical dynamics of an RF MEMS switch. In Int. Conf. on MEMS, NANO and Smart Systems, Proceedings, pp. 237–240, 2005. N. Dumas, F. Azaïs, F. Mailly, and P. Nouet. Study of an Electrical Setup for Capacitive MEMS Accelerometers Test and Calibration. Journal of Electronic Testing, 26(1):111–125, 2010. D. S. Epp, O. B. Ozdoganlar, P. M. Chaplya, B. D. Hansche, and T. G. Carne. A base excitation test facility for dynamic testing of microsystems. In 22nd Int. Modal Analysis Conf. (IMAC), Proceddings, 2004. J. S. Burdess, A. J. Harris, D. Wood, R. J. Pitcher, and D. Glennie. A system for the dynamic characterization of microstructures. Journal of Microelectromechanical Systems, 6(4):322–328, 1997. Jiri Marek. Automotive MEMS sensors – trends and applications. 2011 International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA), 1–2, 2011. H. J. M. T. A. Adriaens, W. L. De Koning, and R. Banning. Modeling piezoelectric actuators. IEEE/ASME Transactions on Mechatronics, 5(4):331–341, 2000. H. Sherman and L. Butler. Transducers as projectors. Transducers and Arrays for Underwater Sound, 76–151, 2007. U.-M. Gomez, B. Kuhlmann, J. Classen, W. Bauer, C. Lang, M. Veith, E. Esch, J. Frey, F. Grabmaier, K. Offterdinger, T. Raab, H.-J. Faisst, R. Willig, and R. Neul. New surface micromachined angular rate sensor for vehicle stabilizing systems in automotive applications. TRANSDUCERS ’05. The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, Digest of Technical Papers, 1:184–187, 2005.

10 | S. Kehrberg et al.

[10] M. S. Weinberg and A. Kourepenis. Error sources in in-plane silicon tuning-fork MEMS gyroscopes. Journal of Microelectromechanical Systems, 15(3):479–491, 2006. [11] E. Tatar, S. Alper, and T. Akin. Effect of quadrature error on the performance of a fully-decoupled MEMS gyroscope. IEEE 24th Int. Conf. on Mechanical Systems (MEMS), 569–572, 2011. [12] M. Saukoski, L. Aaltonen, and K. A. Halonen. Zero-rate output and quadrature compensation in vibratory MEMS gyroscopes. IEEE Sensors Journal, 7(12):1639–1652, 2007.

Biographies Steven Kehrberg received his Dipl.-Ing. degree in electrical engineering with focus on microsystems and precision engineering at the Chemnitz University of Technology, Germany, in 2011. Since 2011 he has been working at the Automotive Electronics department of Robert Bosch GmbH in Reutlingen, Germany. His research interests include design, simulation and characterization of MEMS gyroscopes.

Patrick Wellner studied material science at the University of Stuttgart and received his Diploma degree in 1999. From 2000 to 2003 he studied the mechanical behavior of thin NiAl-films at the Max-Planck-Institute for Metals Research (now Max-Planck-Institute for Intelligent Systems) and received his PhD in 2003 for this work. Since 2004 he has been working in the sensor development unit at Robert Bosch GmbH, Automotive Electronics. He worked as a simulation engineer and team leader for acceleration and angular rate MEMS sensors for several years. In his current position he is senior manager in the field of sensor development for both automotive and non-automotive customers. Carsten Geckeler received his Diploma degree in physics from the Eberhard-Karls University in Tübingen in 2002. From 2002 to 2008 he worked at the university of Tübingen and later at the university of Bonn in the field of experimental atom optics with Bose-Einstein condensates in optical dipole traps. Since 2008 he has been working at the Automotive Electronics department of Robert Bosch GmbH in Reutlingen, Germany on design and simulation of acceleration and angular rate MEMS sensors. Currently his main interests are multi-axis gyroscope MEMS for consumer applications.

Modal Analysis of MEMS Using Ultrasonic Base Excitation

| 11

Jan Mehner received his Dr.-Ing. degree in electrical engineering and information technology from the Chemnitz University of Technology, Germany, in 1994. From 1998 to 1999 he was visiting scientist at the Massachusetts Institute of Technology, Cambridge, where he was involved in software development for MEMS design. 1999 he received a post-doctoral qualification for teaching at universities (habilitation). From 2004 to 2007, he was scientific assistant at the Fraunhofer Institute for Reliabilty and Microintegration Berlin. In 2007, he became full professor for microsystems and precision engineering at the Chemnitz University of Technology. His research interests are analytical and numerical methods for microsystems design, coupled field analysis, sensor and actuator applications as well as experimental characterization of microstructures.

M. Saihi, B. Boussaid, A. Zouinkhi and M.N. Abdelkrim

Decentralized Fault Detection in Wireless Sensor Network based on gaussian function error Abstract: Wireless Sensor Networks (WSNs) have become an information collection and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the sensor device itself and the harsh environment where the sensor nodes are deployed. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. In this paper, we deal with a new approach for detecting distributed faults in wireless sensor network based on error function for gaussian distribution proportional to the deviation between the measurements. Besides, new coefficients to measure the difference between measurements and to do the test on the status of the node are determined in order to improve the fault detection algorithm. The result of simulations show an improvement of the accuracy of the proposed algorithm compared to the classic one. Keywords: Wireless sensor network, distributed fault detection, gaussian distribution, error function. Mathematics Subject Classification 2010: 65C05, 62M20, 93E11, 62F15, 86A22

1 Introduction Wireless sensor networks are emerging as computing platforms for monitoring various environments including remote geographical regions [1]. They are composed of a large number of tiny sensor nodes equipped with limited computing and communication capabilities [24]. Failed nodes may decrease the quality of service of the entire WSN [18]. The node status in WSN scan be divided into two types [2, 3]: normal and faulty. Faulty in turn can be “permanent” or “static”. The so-called “permanent” means failed nodes will remain faulty until they are replaced, and the so-called “static” means new faults will not be generated during fault detection. WSN node faults are usually due to the following causes : the failure of module (such as communication and sensing module) due to fabrication process problems [21], environmental factors, battery power depletion [22] and so on. A sensor network must be capable of

M. Saihi, B. Boussaid, A. Zouinkhi and M.N. Abdelkrim: Gabes University, National School of Engineers of Gabes, Gabes, Tunisia, Research Unit of Modeling, Analysis and Control Systems (MACS), emails: [email protected], [email protected]. De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 2, 2017, pp. 13–26. DOI 10.1515/9783110470444-002

14 | M. Saihi et al.

identifying and replacing the faulty nodes in order to make sure that the network’s quality-of-service is maintained [19]. We proposed an improved DFD scheme based on error function. In fact, the old test result used a binary test; it caused an undesired abrupt change so we have tried to calculate the test result differently by introducing the error function to enlarge the accuracy of the algorithm. A distributed fault detection scheme for sensor networks has been proposed; it uses local comparisons with a modified majority voting, where each sensor node makes a decision based on comparison between its own sensing data and its neighbors data [25], while considering the confidence level of its neighbors. High fault detection accuracy can be reached only when it is applied to the sensor network with many neighbors of nodes to be diagnosed [20]. In this paper, we propose an improved DFD scheme by defining new detection criterion. The remainder of the paper is organized as follows: In section 2, related works in the area of fault detection in WSN, in section 3, fault model and DFD algorithm, the proposed algorithm in section 4 and simulation example in the last section.

2 Previous work In this section, we briefly review the related works in the area of fault detection in wireless sensor networks. Several works have addressed the problem of how to deal with faults occurring in wireless sensor networks in order to achieve fault tolerance [4–6]. In [7], a watchdog processor is used for concurrent system level error detection techniques. A watchdog processor is a small and simple coprocessor that detects errors by monitoring the behavior of a system. They showed that a large number of errors can be detected by monitoring the control flow and memory-access behavior. The research proposed in [4] makes use of redundancy and uses a technique to decide on which nodes to keep active and on which to put in a sleep mode. The technique aims to provide the sensor field with the best possible coverage. In addition, it maintains network connectivity to route information. When an active node fails it is substituted by one of the sleeping nodes. However, other researchers have addressed the problem of having active nodes that provide incorrect data which results in making inappropriate decisions. The authors in [8] proposed a general approach to fault diagnosis that is widely applicable and only needs a limited number of connections among units. The algorithm uses a majority vote among the neighbors of a unit to determine the status of the unit. The research proposed in [5] focused on such issues and proposed a mechanism to detect and diagnose data in consistency failures in wireless sensor networks.

Decentralized fault detection in WSN

| 15

The mechanism proposed in [5] uses two disjoint paths to send the sensed data to a static sink. After the sink receives both copies, it will compare them to check if they match. If the two copies match, both the data and the paths are considered to be fault free otherwise, a third disjoint path will be established. Then the sensor node will send three copies on the three disjoint paths to the sink. The sink will compare these copies and decides on the faulty path. Finally, a diagnosis routine will be executed to identify the faulty node within the faulty path. Article [9] proposes an agreement-based fault detection mechanism for detecting cluster-head failures in clustered Underwater Sensor Networks (UWSNs). Each cluster member is allowed to independently detect the fault status of its cluster head and at the same time a distributed agreement protocol is employed to reach an agreement on the fault status of the cluster head among multiple cluster members. The detection mechanism is based on a TDMA MAC protocol used in the network and runs concurrently with normal network operation by periodically performing a distributed detection process at each cluster member. It makes use of the data periodically sent by a cluster head as the heartbeats for fault detection. The research described in [7] proposed a scheme based on multi-path routing combined with channel coding to achieve fault tolerance. It uses a fuzzy logic based algorithm that is energy and mobility aware to select multiple paths. When selecting the paths, the algorithm takes the remaining energy, mobility and the distance to the destination into account. Article [10] shows it is important to provide failure recovery and avoid unnecessary traffic when the routing topology needs to be rebuilt. It presents an inference engine, called Diffuse, designed to detect when the routing topology needs to be rebuilt based on different goals, such as to recover from routing failures, improve data aggregation, and balance the energy consumption. Diffuse approaches efficiently avoid unnecessary topology constructions. The authors use information/data fusion to detect routing failures, which is a different and promising approach. A general framework to achieve fault tolerance in wireless sensor networks was proposed in [9]. The framework is based on a learning and refinement module which provides adaptive and self-configurable solutions. The authors in [13] proposed and evaluated a localized fault detection scheme to identify the faulty sensors. Distributed fault detection (DFD) method has some shortcomings as follows: the fault detection accuracy will decrease rapidly in the case of the number of neighbor nodes to be diagnosed is all small and the node’s failure ratio is high. High fault detection accuracy can be reached only when it is applied to the sensor network with many neighbors of nodes to be diagnosed. In [13], the authors proposed fault tolerant algorithms to detect the region of an event in wireless sensor networks. Also, they assume that nodes report a binary decision to indicate the presence of an event or not and considered a hazardous behavior for the faulty nodes, which means that the faulty nodes will be providing arbitrary values. They proposed a randomized decision scheme and a threshold

16 | M. Saihi et al.

decision scheme which a sensor node can use to decide on which binary decision to send by comparing the decision it has with the decisions of its neighbors. Article [14] investigates using the spatial correlation of sensor measurements to detect faults in WSNs. An approach of weighting the neighbors’ measurement and presents a method to characterize the difference between sensor measurements are introduced. A weighted median fault detection scheme (WMFDS) is proposed and evaluated for both binary decisions and real number measurements. In [15], a fault map was constructed using a fault estimation model. In order to build the fault map, sensor nodes are required to send additional information that can be used by the fault estimation model. Furthermore, a cluster based algorithm to estimate faults in wireless sensor networks was proposed. In [16], a target detection model for sensor networks was proposed. In addition, two algorithms to facilitate fault tolerant decision making were presented. The first algorithm is based on collecting the actual readings from the neighboring nodes. In the second algorithm, the sensor node obtains the decisions made by the other neighboring nodes to take a final decision. In [17], the design of a distributed fault-tolerant decision fusion in the presence of sensor faults when the local sensors sequentially send their decisions to a fusion center is addressed. A collaborative sensor fault detection (CSFD) scheme is proposed to eliminate unreliable local decisions when performing distributed decision fusion. Based on the pre-designed fusion rule, assuming identical local decision rules and fault-free environments, an upper bound is established on the fusion error probability. According to this error boundary, a criterion is proposed to search the faulty nodes. Once the fusion center identifies the faulty nodes, all corresponding local decisions are removed from the computation of the likelihood ratios that are adopted to make the final decision. The authors in [17] apply error correcting codes to achieve fault tolerance. As a result, a distributed fault tolerant classification approach was proposed. The approach proposed is base of fault tolerant fusion rules that are used to obtain local decision rules at every sensor. In addition, the authors proposed two algorithms that can be used to find good code matrices to be used by the classification approach.

3 Network model and fault model We assume sensors are randomly deployed in the interested area and all sensors have a common transmission range. The area is assumed to be entirely covered by the sensors. As shown in Fig. 1, the dark circles represent faulty sensors and the light gray circles are good sensors. There could be a failure occurring in a certain area as illustrated in this figure. All sensors in the area go out of service. Since we are depending on majority voting, we assume that each sensor has at least 3 neighboring nodes. Because a large amount of sensors are cast into the

Decentralized fault detection in WSN

| 17

interested area to form a wireless network, this condition can be easily obtained. Each sensor node is able to locate the neighbors within its transmission range through a broadcast/acknowledge protocol. Faults may occur at different levels of the sensor network, such as physical layer, hardware, system software, and middleware [17]. In this paper, we focus on hardware level faults by assuming all system software as well as the application software are already fault tolerant. The first of the two groups of components at hardware level consists of a computation engine, storage subsystem and power supply infrastructure, which are very reliable. Another group of components are sensors and actuators which are most prone to malfunctioning. Because in the first group of components the heterogeneous fault tolerant schemes will provide the targeted level of fault tolerance [17], we only consider the sensor faults which include three types of faults: calibration systematic error, random noise error, and complete malfunctioning. Remark: Nodes are still capable of receiving, sending, and processing when they are faulty.

Faulty node

Working node

Fig. 1. Wireless Sensor Network with both faulty and working nodes.

4 Detection of the distributed defects with density of probability 4.1 Gaussian distribution of measurements Generally the measures stemming from sensors of measure have a random behavior but concentrated around an average by training a bell as that of Gauss. Consequently, we can assimilate that the measures follow the normal distribution. Indeed, the

18 | M. Saihi et al.

expression of the result of measure spells under the following shape: x = m + ∆x where m is the best estimation of the moderate size x and ∆x the uncertainty on the measure. Let us be not afraid of insisting on the importance of the estimation of the uncertainty ∆x. In fact, we cannot know without knowing the uncertainty if a size evolved, if such process of measure leads to the same result that such the other one, or if the difference possibly observed.

4.2 Presentation of the normal distribution The normal distribution is a theoretical distribution, in the sense that it is a mathematical idealized image which never meets exactly in the nature. But numerous really observed distributions get closer to it and have this famous shape of “bell” (many individuals around the average, less and less in the fur as we go away from it, and this in a symmetric way). On the other hand, it is very used in inferential statistics: we shall see in particular that an average calculated on a sample is random variable which tends to follow a normal distribution when the size of the sample increases, even if the initial population has quite a different distribution.

4.2.1 The bell curve Let consider the normal distribution with the parameters m and σ noted N(m, σ) which is defined on R by the density:   1 1  x − m 2 (1) f (x) = √ exp − 2 σ σ 2π Let us note that: – the right x = m is an axis of symmetry, – the inflexion points are situated at a distance σ of this axis of symmetry.

4.2.2 The central theorem - limit The CTL (Central Theorem-Limit) will be very precious because he explains us that if we make the sum of one very large number of random(unpredictable) variables of any law, this sum follows approximately a normal law (in fact, without going into the detail of the hypotheses, he says to us that the variable X = X1 + X2 · · · X n tends to follow a normal distribution when n aims towards the infinity).

Decentralized fault detection in WSN

| 19

On one hand, it allows us to understand why so many distributions observed in the reality have approximately this shape of bell: they describe phenomena which result from the addition of a large number of independent causes of fluctuation.

4.2.3 The reduced centered normal distribution To center and to reduce a variable, it is to argue in number of standard deviations σ with regard to the average m. All the relative events in X can be expressed as well according to T. The reduced centered variable T has for hope 0 and for standard deviation 1 because: x−m 1 = [E(x) − m] = 0 because E(x) = m – E(T) = E  x σ− m  σ1 = 2 V(x) = 1 because V(x) = (σ)2 – V(T) = V σ σ Thus, the density of probability y(t) of the reduced centered normal distribution N(0, 1) spells: 1 2 1 y(t) = √ e− 2 t 2π

(2)

This expression gives in particular the probability, F(x) = Pr(t ≤ x) that no measure of the size t is smaller than the value x: 1 2 1 F(x) = √ e− 2 x 2π

(3)

Rather than f and F, we note generally y the density, and P the function of distribution of the distribution N(0, 1).

4.2.4 The error function We define also the “error function” erf (t) (see Fig. 2) as: 2 erf (x) = √ π

x

2

e−t dt

(4)

0

This function allows to calculate these same probability in means of the expression:    x−μ 1 √ 1 + erf (5) Pr(t ≤ x/ gaussian,μ,σ ) = 2 σ 2 We notice that this error can be exploited as a function of test replacing the binary test.

20 | M. Saihi et al. Function Error 1 0.8 0.6 0.4

erf

0.2 0 −0.2 −0.4 −0.6 −0.8 −1 −4

−3

−2

−1 0 1 Randomly produced numbers

2

3

4

Fig. 2. The Function Error representation, erf (x).

4.3 Definitions Before studying the exploitation of the normal distribution rules in DFD, we give the following definitions that will be used later: – n: total number of sensors; – p: probability of failure of a sensor; – k: number of neighbor sensors; – S: set of all the sensors; – N(S i ): set of the neighbors of S i ; – x i : measurement of S i ; – d ij (t): measurement difference between S i and S j at time t, d ij (t) = x i (t) − x j (t) – ∆d ij (t): measurement difference between S i and S j from time t to t + 1, ∆d ij (t) = d ij (t + 1) − d ij (t) = (x i (t + 1) − x j (t + 1)) − (x i (t) − x j (t)) – – –

(6)

C ij : test between S i and S j T i : tendency value of a sensor, T i ∈ {LG, LT, GD, FT }; σ1 and σ2 : the two assumed standard deviations associated to the normal distribution of the measurements;

Decentralized fault detection in WSN

| 21

4.4 Application of the normal distribution in DFD We apply the same improved algorithm and we try to introduce result of measure into a function of error to obtain a more successful evaluation of the states of knots: 2 erf (d ij (t)) = √ π 2 erf (∆d ij (t)) = √ π

dij (t)

2

e−u du = erf ij1 (t)

0 ∆d  ij (t)

2

e−u du = erf ij2 (t)

(7)

(8)

0

Then we calculate C ij which is the product of erf ij1 and erf ij2 and we repeat the same details of calculation treated in the classic DFD algorithm with the error function.

4.5 Description of the algorithm Step 1: For every node S i and any node S j in neighbor (S i ) we calculate: • erf ij1 , • erf ij2 , and • C ij = (erf ij1 × erf ij2 ) 

C ij < Num(S i ) − 1 , set initial detection status T i of S i as possibly Step 2: If S j ∈N(S i )

normal (LG), otherwise T i is possibly faulty. Step 3: Num(N(S i )(T j =LG) ) is the number of neighbor nodes of (S i ) whose initial detection  status is LG.

C ij < Num(S i )(T j =LG) − 1 , set the status of S i as normal (GD), – If S j ∈N(S i ),T j =LG

otherwise it is faulty (FT). Step 4: Check whether detection of the status of all nodes in network is completed or not. If it has been completed, then exit. Otherwise, repeat steps of (1), (2), (3) and (4).

5 Simulation example We consider the following example of measuring temperature were the sensors are deployed regularly in the surface of 100 m2 using ZigBee technology [23] which is the pronoun of IEEE802.15.4. Technology based on this protocol regulation is a kind of a short-distance and low-power wireless communication technology. Let the number of sensors is n = 10, the thresholds θ1 and θ2 are fixed to 3 ◦ C. The measurements of temperature in ◦ C extracted on 10 periods are in Tab. 1 and Tab. 2. The distribution of this measurements are shown in Fig. 3 which is conform to gaussian distribution.

22 | M. Saihi et al.

Tab. 1. A sample of temperature measurements (part 1). Period Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6 Sensor 7 Sensor 8 Sensor 9 Sensor 10

1

2

3

4

5

33.9937 33.7978 33.9018 24.0613 33.9945 33.8881 33.9374 34.0250 33.9007 34.0975

33.9359 34.1809 33.8920 34.0199 33.8479 33.9276 33.9407 34.0401 34.0942 34.0300

81.9627 14.0815 24.0799 24.0120 34.0571 34.0413 43.9013 33.0760 33.9343 33.0440

34.0177 33.9692 33.9868 34.0595 34.1047 33.9802 34.0328 33.9762 34.0230 34.0440

33.9383 34.0275 34.0601 34.0092 34.1730 33.9391 33.9092 33.8250 34.0910 34.0867

Tab. 2. A sample of temperature measurements (part 2). Period Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6 Sensor 7 Sensor 8 Sensor 9 Sensor 10

6

7

8

9

10

33.9920 34.0898 34.0184 34.0291 34.0113 34.0440 34.0102 34.2787 33.8833 33.8146

33.8859 33.8907 33.9566 13.9832 33.9781 34.0541 34.0389 30.0751 34.1778 34.1223

33.8717 33.7671 34.0902 33.8164 34.0067 34.0035 34.2227 33.9931 33.9493 34.0236

34.0246 34.0070 21.9391 33.8777 34.0317 33.8657 33.8968 34.1331 33.9581 33.9860

34.0900 33.9700 34.1029 33.9655 44.1013 34.0629 33.9787 28.9134 33.8957 33.9730

We apply the standard algorithm of DFD (with test binary) and our new algorithm (with error function) on TrueTime 1.5 Wireless Network simulator. This simulator is developed through Simulink Matlab and offer true time simulation for wireless network environment toolbox. After simulation, we get the following results which are regrouped in Tab. 3 and Tab. 4, respectively, where “1” means “Faulty Node” or “FT” and “0” means “Normal Node” or “GD”. More tests are done with various cases of faults for both algorithm which prove that the standard DFD algorithm is efficient only when the faulty sensors are less than the half of sensors. However, the proposed improved algorithm is efficient until 6 faults among 10 sensors which equivalent to 60% of efficiency. This result is very important in case of high probability of fault occurrence especially when the number of sensors in Wireless Networks is not very important which always the case.

Decentralized fault detection in WSN | 23

Bell Curve 0.14

Gauss Distribution

0.12 0.1 0.08 0.06 0.04 0.02 0 0

10

20 30 40 50 Randomly produced numbers

60

Fig. 3. The distribution of measurements. Tab. 3. Standard DFD algorithm result. Period

1

2

3

4

5

6

7

8

9

10

Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6 Sensor 7 Sensor 8 Sensor 9 Sensor 10

0 0 0 1 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0

1 1 1 1 0 0 1 0 0 1

1 1 1 1 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 1 0 0

0 0 0 1 0 0 0 1 0 0

0 0 1 0 0 0 0 0 0 0

0 0 1 0 1 0 0 1 0 0

Tab. 4. Improved DFD algorithm result. Period

1

2

3

4

5

6

7

8

9

10

Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Sensor 6 Sensor 7 Sensor 8 Sensor 9 Sensor 10

0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

1 1 1 1 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0

0 0 0 0 1 0 0 1 0 0

70

24 | M. Saihi et al.

6 Conclusion In this work, a new approach to detect distributed faults in wireless sensor network based on error function proportional to the deviation between the measurements is proposed. In fact, more the difference is greater, more the possibility of falling down is greater. Upon this remark, we have designed a new algorithm which calculates the difference between measurements and generates a normal probability based on the gaussian function error in order do deduce the status of the node. The new test procedure replaces the binary test in the previous work in DFD as mentioned above. The application of this algorithm on a sample of 10 sensors show an efficiency of 60% versus 50% in the classis one. This result is very important if the case of a small node neighborhood in the small small sensor network applications.

Bibliography [1] [2] [3] [4]

[5]

[6] [7] [8] [9] [10] [11]

[12] [13]

I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cyirci. Wireless sensor networks: a survey. Computer Networks, 38(4):393–402, 2002. S. Chessa and P. Santi. Comparison based system level fault diagnosis in Ad hoc networks. 20th Symp. on Reliable Distributed Systems (SRDS), New Orleans, :257–266, 2001. S. Chessa and P. Santi. Crash faults identification in wireless sensor networks. Computer Communications, 25(14):1273–1282, 2002. Y. Zou and K. Chakrabarty. A Distributed Coverage and connectivity Centric Technique for Selecting Active Nodes in Wireless Sensor Networks. IEEE Trans. on Computers, 54(8):978–991, 2005. K. Ssu, C. Chou, H. C. Jiau and W. Hu. Detection and diagnosis of data inconsistency failures in wireless sensor networks. Int. J. of Computer and Telecommunications Networking, 50(9):1247–1260, 2006. G. Gupta and M. Younis. Fault-tolerant clustering of wireless sensor networks. Conf. on Wireless Communications and Networking (WCNC), :1579–1584, 2003. A. Mahmood and E. J. McCluskey. Concurrent error detection using watchdog processors-a survey. IEEE Trans. on Computers, 37(2):160–174, 1988. D. Blough, S. Sullivan, and G. Masson. Fault diagnosis for sparsely interconnected multiporcessor systems. 19th Symp. on Fault-Tolerant Computing, 19:62–69, 1989. P. Wang, J. Zheng and C. Li. An Agreement-Based Fault Detection Mechanism for Under Water Sensor Networks. Global Telecommunications Conf., Washington, DC, :1195–1200, 2007. E.F. Nakamura, C.M.S. Figueiredo, F.G. Nakamura and A.A.F. Loureiro. Diffuse: A topology building engine for wireless sensor networks. Signal Processing, 87(12):2991–3009, 2007. J. Staddon, D. Balfanz and G. Durfee. Efficient tracing of failed nodes in sensor networks. 1st ACM Int. Workshop on Wireless sensor networks and applications, Atlanta, Georgia, USA, 2002. J.R. Chen, S. Kher and A. Somani. Distributed fault detection of wireless sensor networks. Int. Conf. on Mobile Computing and Networkings, Los Angeles, CA, USA, :65–72, 2006. Y.S. Chang, T.Y. Juang, C.J. Lo, M. T. Hsu and J.H. Huang. Fault Estimation and Fault Map Construction on Cluster-based Wireless Sensor Network. IEEE Int. Conf. on Sensor Networks, Ubiquitous, and Trustworthy Computing, 5–7 June 2006, :14–19, 2006.

Decentralized fault detection in WSN | 25

[14] J.L. Gao, Y.J. Xu and X.W. Li. Weighted-median based distributed fault detection for wireless sensor networks. J. of Software, 18(5):1208–1217, 2007. [15] Y. Lai and H. Chen. Energy-Efficient Fault-Tolerant Mechanism for Clustered Wireless Sensor Networks. 16th Int. Conf. on Computer Communications and Networks, ICCCN, :272–277, Auguste 2007. [16] T.Y. Wang, L.Y. Chang, D.R. Dun and J.Y. Wu. Distributed fault-tolerant detection via sensor fault detection in sensor networks. 10th IEEE Int. Conf. on Information Fusion, Quebec, Canada, :1–6, 2007. [17] F. Koushanfar, M. Potkonjak and A. Sangiovanni-Vincentelli. Fault-Tolerance in Sensor Networks. Handbook of Sensor Networks. I. Mahgoub and M. Ilyas (eds.), CRC press, Section VIII, no. 36, 2004. [18] R. Haung, X. Qiu and L. Ye. Probability-based fault detection in wireless sensor networks. Int. Conf. on Network and Service Mangement (CNSM), Bejing, China, :218–221, 2010. [19] A. Akbari, A. Dana, A. Khadme Zadeh and N. Beikmahdavi. Fault detection and recovery in wireless sensor network using clustering. Int. J. of Wireless and Mobile Networks, 3(1):130–138, 2011. [20] A. Mahani, M.S. Ansari and Y.S. Kavian. Reliability or performance: A tradeoff in wireless sensor networks. 8th IEEE TCI Int. Symp. on Communication Systems, Networks and Digital Signal Processing, :1–5, 2012. [21] K. Lok Man, C. Chen and D. Hughes. Decentralized Fault Detection and Management for Wireless Sensor Networks. 5th Int. Conf. on Future Information Technology (FutureTech), May 2010, Busan, Korea [22] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler and K. Pister. System Architecture Directions for Networked Sensors. ACM SIGPLAN notices, 35(11):93–104, 2000. [23] http://www.zigbee.org/Specifications/ZigBee/Overview.aspx, consulted: 06.10.2013, 2013. [24] A. Zouinkhi, E. Bajic, E. Rondeau and M.N. Abdelkrim. Simulation and modeling of active products cooperation for active security system management. Trans. on Systems, Signals and Devices, 5(4):1–23, 2011. [25] M. Saihi, B. Boussaid, A. Zouinkhi and M.N. Abdelkrim. New Approach for Decentralized Fault Detection in Wireless Sensor Network. 13th Int. Conf. on Sciences and Techniques of Automatic control and computer engineering, :1–9, Monastir, Tunisia, 2012.

Biographies Marwa Saihi born in 1988, in Tunis, Tunisia. She is a PhD student at the National Engineering School of Gabes (Tunisia) and a member of Modeling, Analysis and Control Systems (MACS) research unit. She received the Notional engineering and Master Degrees from the Notional Engineering School of Gabes (ENIG), Tunisia in 2012 in Electrical Engineering and Automatic Control. She received the Bachelor Degree in 2007 in Sciences. Currently, her research frameworks focuses on wireless sensor networks and fault detection for reliable centrelized/decentralized control.

26 | M. Saihi et al. Boumedyen Boussaid born in 1972, is associate professor at National School of Engineers of Gabes, Tunisia, in Electrical Engineering and Automatic Control Department. He received his PhD degree in 2011 in Automatic Control and Computer Sciences from both University of Nancy and University of Gabes. He received his Engineering Degree in 1997 in Electrical engineering from National School of Engineers of Tunis. He is currently a member of the Research Centre in Automation of Nancy (CRAN) and the Research unit on Modeling, Analysis and Control of Systems (MACS). His research interests focus on fault tolerant control and fault detection in wireless sensor networks with application to wind firms and robots networks.

Ahmed Zouinkhi is Associate Professor at the National Engineering School of Gabes (Tunisia) and a member of Modeling, Analysis and Control Systems (MACS) laboratory. He received the Notional engineering Degree from the Notional Engineering School of Monastir (ENIM), Tunisia in 1997 in industrial computing. He received his PhD degree in 2011 in Automatic Control from the National Engineering School of Gabes (Tunisia) and a PhD degree in Computer Engineering from the Nancy University (France). His research activities are focused on Distributed Systems, Smart Objects theory and applications, Ambient Intelligence systems and architectures, RFID and Wireless Sensors Network Concepts and Applications in manufacturing and supply chain. Mohamed Naceur Abdelkrim was born in Tunisia in 1958. He obtained a Diploma in Technical Sciences in 1980, his Master Degree in Control in 1981 from the ENSET school of Tunis (Tunisia), and his PhD in Control in 1985 and the Doctorate in Sciences Degree (Electrical engineering) in 2003 from the ENIT School of Tunis. Since 2003 he is Professor at the Electrical Engineering Department (Control) of the National Engineering School of Gabes (Tunisia) and he is manager of the Modeling, Analysis and Control Systems (MACS) laboratory.

B. Mezghani, F. Tounsi and M. Masmoudi

Static Behavior Analytical and Numerical Analysis of Micromachined Thermal Accelerometers Abstract: This paper presents static behavior analytical study of micromachined convective accelerometers. This includes both heat conduction and convection behavior study and modeling. A mixed modeling technique has been used to derive general expressions governing heat conduction and convection of MEMS thermal accelerometers. This technique is based on the use of results from FEM simulations to develop an analytical model where all derived expressions are as a function of biasing temperatures and key design geometry parameters. For conduction behavior analysis, two variables are being used in FEM simulations: heater temperature and micromachined cavity depth. The latter parameter has a large impact on the overall conductive behavior of thermal accelerometers since it fixes the volume where the heat bubble can expand. In addition, heater temperature is considered to be the only parameter that fixes heat distribution in the cavity. This modeling has led to the derivation of expressions for both heater heat transfer coefficient and common mode temperature. These physically-based derived expressions govern the overall sensor conductive behavior. Concerning heat convection behavior, cavity width parameter has been added as a third variable. Using simulation data points, fitting technique has been used to develop an analytical expression of differential temperature, proportional to sensitivity, as a function of the above design and temperature parameters. This study helps to predict sensor performance at an early design stage and more importantly for different sensor design geometries and temperatures. Keywords: Thermal accelerometer, micromachined, analytical model, FEM simulation, spherical model. Mathematics Subject Classification 2010: 65C05, 62M20, 93E11, 62F15, 86A22

B. Mezghani, F. Tounsi and M. Masmoudi: University of Sfax, National Engineering School of Sfax (ENIS) Electronics, Micro-technology and Communication (EMC) Research Group, Sfax, Tunisia, e-mails: [email protected]; [email protected]; [email protected]. De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 2, 2017, pp. 27–45. DOI 10.1515/9783110470444-003

28 | B. Mezghani et al.

1 Introduction Since the introduction of miniature micromachined components, many new sensors are reported in literature [1–7]. One of these new microscopic electromechanical structures is the inertial sensor which was introduced after the recent progress in the development of semiconductor processing technology. Inertial sensors, as accelerometers, measure accelerations in one, two or three orthogonal axes. Acceleration measurements are typically used to calculate in-plane velocity and position, inclination, tilt or orientation in two or three dimensions with respect to the acceleration of gravity, as well as to measure vibration and impact. Over the last few years, numerous types of micromachined accelerometers have been designed and developed [6–15]. Due to their integrated nature, they all have the key features of miniature size, compact design, light weight, sensitive to small accelerations, low power consumption and low cost due to the batch fabrication integrated process. Thanks to their numerous advantages, MEMS accelerometers have been widely used in both consumer and military fields. This includes automotive industry, consumer applications as laptops, navigation systems, military industry as in missile guidance and in robotics and system automation. As a natural outcome, research centers are increasingly interested in the development of new, smaller, more sensitive, faster and also more reliable MEMS accelerometers. These needed developments in sensor performance require the development, in the first place, of a fabrication process which is obviously a crucial factor to provide the good stability and the needed high reproducibility as well as having a high yield of the integrated sensor. Usually, micromachined accelerometers such as capacitive accelerometers [8] and piezoresistive accelerometers [9] measure the applied acceleration by means of using a proof mass. By quantifying the displacement of this proof mass, these types of accelerometers are able to measure the applied acceleration. We have to note here that this operation mode, which is based on the proof mass displacement, constitutes the main disadvantage of these types of sensors. This is mainly due to the possibility of system breakdown under a high signal giving a low shock survival rate and also due to the complex fabrication process of such sensors. One design that overcomes such problems is the thermal convection-based micromachined accelerometer. Over the last few years, several designs with multiple sensitive axes have been presented in literature [11–15]. The key feature of this new sensor lies in the fact that its design does not have a solid proof mass but it relies on free convection of a heated air bubble. Convective micromachined accelerometers are being largely analyzed in recent years because of the simplicity of sensor structure. These accelerometers have been widely studied, but their optimization still needs to be investigated because of the complex effects of design parameters on

Analysis of micromachined thermal accelerometers

| 29

both conduction and convection operation. In addition, the numerous requirements for the monolithic process compatibility impose strict constraint on sensor design parameters as heater temperature and geometry, cavity size and package size and form [16–23]. Among many thermal convection-based accelerometers, standard CMOS design ones are of particular interest. This is because it is gives a solution which will be compatible with monolithic integration with all the necessary interface and signal conditioning circuitry on a single chip using wafer level packaging. This will obviously lead to a device with low cost, robust and with a miniature die size. Dynamic response, or frequency bandwidth, is the other area where thermal accelerometers do not show good performance. However, for many consumer applications as in mobile phones, this is not considered as an important issue because the lower frequency response is considered to be more than sufficient. This concern has been already dealt with using correction electronic circuitry, as feedback scheme, and maximum frequency response has been pushed from about 30 Hz to above 100 Hz [24]. Under specific conditions, a bandwidth greater than 300 Hz has been reported without any electronic signal treatment [25]. This enhanced frequency response has been attained using various gas nature medium and pressure inside the cavity. Using both analytical and numerical tools, modeling and simulation of thermal accelerometers still need to be largely investigated. This is due to the numerous new design geometries offering single, dual and three axes configurations. As a contrast to single axis thermal accelerometers, where cylindrical model is generally being used, dual and three axis counterparts are normally more adapted for spherical 3D model. Several theoretical analyses using this model are detailed in [19–23, 26, 27]. However, thermal accelerometers specific design parameters can be quite different from one sensor design to another. Therefore, to be able to properly use spherical model expressions, FEM simulations could be used to fit these parameters into different modeling equations. The derived result will be complete general analytical model expressions which are function of different design parameters. Based on a newly developed 3D geometrical model of thermal accelerometers, FEM simulations, using the Ansys© software, are done on the sensor. The obtained specific values and their respective fitting curve expressions are used in detailed analytical modeling to derive different modeling equations of the sensor under test general design. We will first treat the conductive behavior which includes the expressions derivation of the heat transfer coefficient and common mode temperature. Second, we will model the convective behavior which comes down to the derivation of the differential temperature expression as a function of key geometric design parameters and biasing temperatures.

30 | B. Mezghani et al.

2 Thermal accelerometer and FEM model presentation All three known types of MEMS convective accelerometers have similar operating mode, thoroughly explained in literature, concerning static behavior: common mode and convection phenomena. This includes single, dual and three axis micromachined thermal accelerometers. Different convective accelerometer configurations and designs were fabricated then characterized and published: – In [12], Garraud et al. presented a dual-axis CMOS MEMS thermal accelerometer (Fig. 1) with a square shaped hot plate containing a metallic meander forming the heater.

Sensing directions rdet 2.rin

2.rout

t

Micromachined cavity

SiO2

Al

Polysilicon Fig. 1. Schematic of a dual axis accelerometer showing main geometry parameters and sensing directions [12].

Temperature detectors are placed orthogonally in the heater plane. A post-process etching step is added to the CMOS chip to release the structures. Detectors are made with a set of thermocouples, which are based on the Seebeck effect and provide a potential difference proportional to the temperature difference between their hot and cold junctions. This gives a better performance of the bias shift with respect to any room temperature variation.

Analysis of micromachined thermal accelerometers

–

| 31

In [13], Park et al. introduced a dual-axis MEMS convective accelerometer with diamond-shaped heater geometry. Since temperature gradient, proportional to sensitivity is larger for this heater shape then it is better than the square shape. To achieve a greater concentration of generated heat, a new implementation of heater complex pattern, which is thinner and longer than conventional heaters, is used (Fig. 2).

Fig. 2. SEM image of a diamond-shaped heater prototype showing new heater and thermopiles design implementation [13].

–

In [14], Nguyen et al. published a prototype of a monolithic 3-axis thermal accelerometer. This CMOS MEMS senor is claimed to be the first to be designed and fabricated using standard planar technology, available through the CMP service in Grenoble, France. The used process is a 0.35μm CMOS process from AMS, followed by a post-process used to form the bottom cavity and release the structures. As shown in Fig. 3, the heater is designed on the central square plate and the four thermal detectors are placed on medians of the cavity. For x and y axis sensing directions, all four resistive sensors are used. For the third z-axis detection, only two sensing resistances are used and two reference resistances are added on silicon substrate. Each z-sensing resistance is then split into a pair to measure the average common mode temperature on the four sensing locations on x and y medians.

In the literature and also in patents, it can be seen that various research groups designed, fabricated and tested convective accelerometers. Most shown results focus on the chosen design geometry and fabrication steps. However, both analytical and numerical analyses which are in some cases being shown were developed to fit only on their specific design. This leads to say that the specific modeling development cannot be used for a different design or biasing temperatures. Moreover, only few studies focused on the use of analytical modeling in predicting sensor performance

32 | B. Mezghani et al.

as a function of key geometric parameters. This is obviously due to the complicated nature of this kind of study in spherical model coordinate.

Fig. 3. 3D representation of the micro-heater and the 4 sensing resistances [14].

In this study, we try to use a newly developed 3D geometry model in FEM simulations to adapt the available spherical model equations on a general thermal accelerometer design. The 3D model, at first presented in [19, 20] for a single axis thermal accelerometer, is nearly the same for dual and three axis ones. Moreover, the exact same FEM technique is used to evaluate average common mode and differential temperature values of the sensor. In this actual study, numerical simulations are performed for heat transfer of a laminar and incompressible flow of a thermal-bubble inside an enclosed chamber. In computations, the air (fluid used) thermal properties are assumed to be constant in the operating temperature range. Boundary conditions for both micromachined cavity and package are regarded as the isothermal walls and are set to 300 K. In addition, a meshing refinement is used so that we have the finest mesh size near the heater, temperature detection location, and largest at the boundaries. This used meshing model has been validated by proving that simulation results for different designs are independent of the mesh minimum size. In addition, we have used thermal properties of compressible air as a function of temperature and steady laminar natural convection with continuity, mass, energy and momentum conservation. We will first treat the conductive behavior which includes the expressions derivation of the heat transfer coefficient and common mode temperature. Second, we will model the convective behavior which comes down to the derivation of the differential temperature expression. In the following sections, the sensor parameters and their

Analysis of micromachined thermal accelerometers

| 33

nominal values, used in both FEM simulations and equations evaluation, are given in Tab. 1. Tab. 1. List of accelerometer parameters and nominal values Symbol

Description

Value

Unit

rd rH ro h1 h2 e d2 w

Distance from heater center to detector 120 Heater half width 30 Cavity half width 300 Bottom cavity depth 300 Top cover height 1000 Suspended structures thickness 7 Distance from bottom cavity edge to cover 600 1800 Top cover width w = 2(r o + d2 )

μm μm μm μm μm μm μm μm

As shown in [21, 22], values chosen for r d , h2 , h1 and d2 give maximum sensitivity readings for the ro value of 300μm. In addition, the specified r H value offers maximum efficiency. These key geometric parameters which will be used in both numerical and analytical modeling are explained in the simplified cross-sectional view shown in Fig. 4. The analytical modeling which will be used concerns spherical model which is the most adapted for dual and three axis accelerometer design geometries. This model is based on free convection heat transfer between two concentric spheres (Fig. 5): from an inner small sphere to an outer larger sphere. Relating this model to our thermal accelerometer, we can state that: – the inner small sphere, with radius ri and temperature Ti, models the suspended central plate where the heater is being implemented, – the outer larger sphere, with radius ro and temperature To, models the isothermal walls which are in our case the substrate and package.

Si Package

Bonding

Heater

h2

Bonding

Detector ro

rd 2rH h1

d2 Bottom cavity

Si substrate

Fig. 4. Simplified thermal accelerometer cross-sectional view with different modeling parameters.

34 | B. Mezghani et al.

To

ro

Ti ri

Fig. 5. Inner and outer spheres, modeling the heater and substrate.

3 Expressions describing the conduction behavior Harry C. Hardee studied in details both conduction and free convection heat transfer in a closed chamber [26, 27]. He expressed both common mode and differential temperatures as a function of parameters shown in Fig. 5. As was previously shown in [21, 22], thermal accelerometers inner and outer produced heat isotherm shapes show considerable deformation when compared to a perfect sphere shape as those shown in Fig. 5. Specifically, the outer isotherm radius and form were found to be closely related to the overall sensor geometry and therefore fixes both conductive and differential temperature readings. Among most thermal accelerometer parameters, bottom cavity depth, h1 , is the only distance which is definitely fixed only at the end of the post-process micromachining step. This is because it can be easily affected by etching time and etching solution movements, composition and also temperature. As a result, the final cavity depth value can be easily modified if ideal conditions are not met during the micromachining post-process. Based on these important fabrication details, one of our main modeling parameter will therefore be h1 . In convective accelerometers, heat conduction behavior can be analyzed by the specification of two main parameters: – Heat transfer coefficient which is related primarily to the heater temperature through heat conduction in the cavity, and is closely related to cavity depth, – Common mode temperature set from heater temperature value and which will set detectors initial temperature.

3.1 Heater - Heat transfer coefficient In [23], the step by step derivation of the heat transfer coefficient h H , expression was detailed. The evaluation of an analytic value of this coefficient can be done using the following expressions: hH =

R o λ0 C λ (T) R i (R o − R i )

(1)

Analysis of micromachined thermal accelerometers

| 35

where C λ (T) is given by: C λ (T) = 1 +

δ1 T i2 − T o2 δ2 T i3 − T o3 δ3 T i4 − T o4 + + × × × 2 Ti − To 3 Ti − To 4 Ti − To

(2)

where λ0 = −3.93 × 10 − 4Wm−1 K−1 is the air conductivity extrapolated at T = 0K (the negative sign is due to the fact that we extrapolate at 0 K an expression that is only valid from 100 K to 600 K). The other three parameters are given by: δ1 = −0.259K−1 , δ2 = 1.23 × 10−4 K−2 and δ3 = −3.87 × 10−8 K−3 , which represent the coefficients of thermal conductivity variation for air. The equivalent inner sphere equivalent radius value is R i = 28μm. This chosen value is considered to be a trade-off between a radius obtained from the central plate and that of the heater meander. The outer isotherm equivalent radius R o can be found using the following expression: Ro =

ro × 2 4

h1  r 4 h41 + 0 3

(3)

As it is quite clear, the analytical evaluated value of the heat transfer coefficient will be dependent on both design geometry parameters and heater/substrate temperatures. Therefore, we can predict this value well before fabrication which is preferably at the early design level.

3.2 Common mode - Fluid conduction Detectors’ initial temperature is set by heat conduction in the air filling the cavity. This parameter is denoted common mode temperature TCM. The analytical general expression derivation of TCM as a function of design geometry parameters and biasing temperatures was also detailed by Mezghani et al. in [23]. This expression is given by: δ1 2 δ δ 3 4 − T o3 ) + 3 (T CM − T o4 ) (T − T o2 ) + 2 (T CM 2 CM 3 4   δ δ δ R R o (Ro − d) (T i − T o )+ 1 (T i2 − T o2 )+ 2 (T i3 − T o3 )+ 3 (T i4 − T o4 ) = i 2 3 4 R o d(R o − R i )

(T CM − T o ) +

(4)

where Cλ (T) is given by: C λ (T) = 1 +

3 4 2 − T o3 δ3 T CM − T o4 − T o2 δ2 T CM δ1 T CM + + × × × 2 T CM − T o 3 T CM − T o 4 T CM − T o

(5)

The equivalent outer radius Ro derived analytical expression is given by: Ro (h1 ) =

ro + d × 2 4

ro − d h1  r 4 + 2 o h41 + 2

(6)

36 | B. Mezghani et al.

Again, it is quite clear that the analytical evaluated value of the common mode temperature will be dependent on both design geometry parameters and heater/substrate temperatures. Therefore, we can predict this value well before fabrication which is preferably at the early design level.

4 Expressions derivation describing convection behavior The second most important parameter describing sensor performance is known as sensitivity, S. When no acceleration is applied, temperature detectors are located on identical temperature isotherms, obtained for symmetry reasons. Therefore, common-mode temperature (T CM ) will be indicated by both detectors. Detector temperature reading is made feasible using two different techniques. One uses the high resistivity dependence of polysilicon to temperature (Temperature Coefficient of Resistance, TCR = 9 × 10−4 /ˇrC). The second uses a set of thermocouples, which are based on the Seebeck effect and provide a potential difference proportional to the temperature difference between their hot and cold junctions. Under acceleration along a specific sensitive axis, cavity temperature distribution deforms due to free convection and each detector will therefore indicate a new temperature, T Di . Then, the temperature variation ∆T Di of each detector can be evaluated by taking the difference between the two previous readings (∆T Di = T Di − T CM ). The differential temperature, proportional to the sensor sensitivity, is then obtained from both detectors and given by δT D = ∆T D1 − ∆T D2 . Finally, the thermal signal is converted into an output voltage by means of an integrated Wheatstone bridge. This voltage is then amplified by an on-chip instrumentation amplifier. Sensor sensitivity (S expressed in K/g) is therefore proportional to the measured differential temperature δT D : δT D = ∆T D1 − ∆T D2 and S =

δT D Γ

(7)

Consequently, when an acceleration (Γ in g, 1g = 9.81ms−2 ) of 1g is applied along one sensitive axis, the differential temperature (δT D ) is obtained from temperature variations ∆T D1 and ∆T D2 , which are symmetrically measured by both detectors. From the above study, we clearly see that the analysis of sensitivity expression can be done through the study of the differential temperature behavior and measured values using unity acceleration. In the following, we will report this study and will express the sensor differential temperature expression as a function of key geometric design parameters and biasing temperature values.

Analysis of micromachined thermal accelerometers

| 37

4.1 Differential temperature dependence on cavity depth One of the most important geometric parameter is obviously the depth of the bottom micromachined cavity. This is clearly explained at the beginning of section three. Therefore, we will start our differential temperature modeling from this specific parameter. Along with this latter variable, the heater temperature value is also considered extremely important since it is the only parameter that fixes the initial temperature settings in the cavity which obviously will determine this thermal sensor overall response. In order to be able to find this dependence, FEM simulations were done, using our new 3D model, to evaluate differential temperature values for various technologically possible bottom cavity depths and for different heater temperatures. Next, these FEM evaluated values are plotted and a fitting is done on the data points. Both plotted values and respective fitting curves are shown in Fig. 6.

Ti = 600K

0.06 0.05

Ti = 550K

δ T (K)

0.04 Ti = 500K

0.03 0.02

Ti = 440K

0.01

Ti = 390K Ti = 350K

0 0

50

100 150 200 250 300 Bottom cavity depth, h1 (μm)

350

400

Fig. 6. Differential temperature vs. bottom cavity depth for different heater temperatures (solid line: fitting curves, dots: FEM data).

From Fig. 6, we can clearly see that differential temperature values are proportional to heater temperatures. This phenomenon is only due to the variation of heat isotherms surrounding the heater as its temperature is being varied. It is therefore concluded that: – for high h1 values, differential temperature values are constant for constant heater temperatures. This can be explained by the fact that silicon body walls of the micromachined cavity will be the limiting factor of the heat bubbles.

38 | B. Mezghani et al.

–

for low h1 values, differential temperature values increase for constant heater temperatures. As bottom substrate silicon gets closer to the heater, this micromachined cavity depth will be the limiting factor of the heat bubbles which in turn are considered to be the only mechanism that fixes the measured differential temperature.

This can be explained by the shape of the hot bubble created by the heater, as illustrated in Fig. 7. In the case that cavity depth is high enough; the four lateral silicon cavity side walls will limit the hot bubble size. Therefore, hot bubble size is not affected by cavity depth value and the differential temperature just depends on heater temperature and cavity lateral dimensions. On the other hand, the hot bubble size reduces considerably for low cavity depth values, which becomes the main limiting factor of the produced isotherms and therefore of the differential temperature measured values.

(a) h1 = 300μm

(b) h1 = 75μm

Fig. 7. Temperature profile cross section inside cavity for two cavity depths.

From Fig. 6, we can see that the transition between these two behaviors occurs around h1 = 150μm. It is very important to note that this transition value is independent of heater temperature. The fitting of one single curve can be written using the following expression: h1 (8) δT h1 = 4  4 h41 + 150 × 10−6 In this equation, the denominator root order was chosen to give the best transition fit between linear and saturation regions shown in Fig. 6. To make this expression a general one and specifically dependant on the key geometric parameter which fixes the cavity side walls distance (cavity half width, r o ), we should include this latter parameter in the fitting expression. Here, we express the differential temperature expression to reflect a linear relationship for low values of h1 and a saturation at a

Analysis of micromachined thermal accelerometers

| 39

value around r o = 150μm for high values of h1 . As we can notice, this corresponds to a distance of (r o /2) from the heater center to the cavity border. Therefore, the general fitting expression for a single curve in Fig. 6 can be written using the following formula: δT h1 = 4

h1  r 4 o h41 + 2

(9)

It should be noted that the effect of ro is modeled in this expression only for low values of h1 . However, the effect of r o is not taken into account for both transition and saturation effects. This will be studied in detail in the next sections by analyzing the complete relation of cavity half width variation on differential temperature readings.

4.2 Differential temperature dependence on heater temperature In order to be able to specify differential temperature expression for all heater temperatures shown in Fig. 6, we have to quantify in the first place the differential temperature variation as a function of heater temperature value. This biasing temperature relation can be easily found from curve fitting of maximum δT D values plotted as a function of heater temperature. Therefore, to express the dependence of sensor sensitivity on heater temperature, we propose to find the relation between the maximum differential temperature readings, evaluated using FEM simulations, and heater temperature. The relation can be easily found through curve fitting of FEM data points which will then give the analytical expression describing this important dependence. The fitting expression of the FEM data points can be written as: δT Ti = 3.6 × 10−6 (T i − T o )1.7

(10)

Therefore, using unity acceleration, sensor sensitivity dependence on both heater temperature and micromachined cavity depth can be expressed as: δT h1,Ti = 3.6 × 10−6 (T i − T o )1.7 × 4

h1  r 4 o h41 + 2

(11)

Again, it should be noted here that this expression models the effect of r o only for low values of h1 . For sensitivity transition and saturation regions, this effect is not taken into account in this expression. As it was clearly explained earlier, since the

40 | B. Mezghani et al.

volume where convection phenomena occurs will be largely affected when r o varies, therefore, this will have a direct effect on differential temperature measured by both detectors. Accordingly, transition and saturation sensitivity values will be largely affected. This gives the half width cavity design parameter an added importance and should therefore be included in the modeling general equation. This will help in predicting the effect of this parameter variation on the thermal accelerometer sensitivity value and therefore on the overall sensor performance.

4.3 Differential temperature dependence on cavity width To study the differential temperature value dependence on r o for different sensor design geometries, we use the 3D model in FEM simulations where all other design parameters are set to their maximum values. Therefore, we use a square cover with h2 = w = 3mm, heater temperature T H variable, cavity depth h1 variable and cavity half width r o variable [200μm – 800μm]. Heater temperature and cavity depth variations are done in pairs given by: [(T H , h1 ) = (390K,0.25r o ), (440K,0.5r o ), (500K,0.75r o ), (550K,r o ), (600K,1.25r o )]. This is done so that all possible variation values will be studied and derived expressions can therefore be as general as possible. This means that these derived expressions can be used to estimate differential temperature readings for any technologically possible design geometry and also at an early design stage. Using the above set of data, various FEM simulations were done to compute and plot sensitivity (differential temperature) values for a fixed cavity half width of r o = 300μm as a function of various ro values from 200μm to 800μm. Evaluated data points are plotted in Fig. 8, where the dashed lines are included to show that there is a linear relationship between differential temperature and micromachined cavity half width. This linear relationship between evaluated differential temperature values (proportional to sensitivity) and cavity half width values obviously says that when varying r o , sensitivity will be multiplied by a factor. This so called multiplication factor can be computed by plotting it and then using data fitting on these points. The curve fitting expression will be the exact multiplication factor which can be used as a general modeling expression to predict the sensitivity value when cavity width is varied. Both of these data points and respective fitting curve are shown in Fig. 9. From FEM data fitting, we deduce the analytic expression describing the effect of r o variations on differential temperature readings. This multiplication factor, C ro , is given by: (12) C ro = 2.63 × 106 r2o + 5119 r o

Analysis of micromachined thermal accelerometers

| 41

Sensitivity for ro = 200−800μm (mK/g)

450 ro= 200μ

400

ro= 300μ ro= 400μ ro= 500μ ro= 600μ ro= 700μ ro= 800μ

350 300 250 200 150 100 50 0 0

10

20 30 40 50 60 70 Sensitivity for ro = 300μm (mK/g)

80

90

Fig. 8. Sensitivity values for a fixed cavity width vs sensitivity values using various cavity width values (dashed lines to show the linear relationship).

6

Multipication factor, Cro

5 4 3 2 FEM data Fitting curve

1 0

200

300

400 500 600 Cavity half width, ro (μm)

700

800

Fig. 9. Multiplication factor for various cavity half width values (solid line: fitting curve, dots: FEM data points).

It should be noted here that the r o value is in μm. Furthermore, this fitting expression cannot be used by itself and should be added to the expression of the differential temperature previously derived. This will give a general modeling equation which will

42 | B. Mezghani et al.

predict the value of the differential temperature for a fixed h1 , T i and r o values. This expression can therefore be written under the following form: δT h1,Ti,ro = 3.6 × 10−6 (T i − T o )1.7 × 4

 × 2.63 × 106 r2o + 5119 r o

h1  r 4 o h41 + 2  (13)

Another very important parameter is the optimal position of detectors in the cavity. This also can be seen as how far is the detector from the heater. This is a crucial concern since it can dramatically affect differential temperature readings if detectors are not placed in their optimal location. To study the effect of detectors location on differential temperature readings, FEM data points are taken along the sensitive x-axis for various half cavity width values. When plotted (Fig. 10), these values clearly show that detectors location can in fact affect differential temperature readings.

250 200 150

δ T (mK)

100 50 0 −50 ro= 300μm ro= 400μm ro= 500μm ro= 600μm ro= 700μm

−100 −150 −200 −250

−600

−400

−200 0 200 400 x position in the cavity (μm)

600

Fig. 10. Differential temperature values evolution along sensitive x-axis for various cavity half widths.

This proves that detector location in the cavity is a crucial factor and should be included in this specific modeling of differential temperature as a function of cavity width. This analysis and equations derivation should obviously be continued so that it includes further design parameters to finally give a general modeling expression that contains all geometry factors and biasing temperature of thermal accelerometers. The

Analysis of micromachined thermal accelerometers

| 43

other design parameters which can be included in the study concern cover height and width and also heater width and thickness.

5 Conclusion In this paper, we used a newly developed 3D model in FEM simulations with previously derived analytical model to study conduction and convection behavior of CMOS MEMS thermal accelerometers. Heat conduction distribution in the cavity is governed by heater temperature, set by its heat transfer coefficient. This will fix cavity common mode temperature and specifically along each sensitive axis. Moreover, the volume where the conduction phenomenon is to take place is fixed by bottom cavity depth. These dominating parameters are analytically modeled and values were verified using FEM simulations. A detailed modeling of differential temperature as a function of heater temperature along with micromachined cavity depth and width has been done. A derived expression was given which includes these effects and therefore predicts a sensitivity level at an early design stage. This analytical study could obviously be applied for different sensor design parameters and biasing temperatures. Acknowledgments: Authors are indebted to their colleagues at the LIRMM in France for providing valuable help during the first steps in understanding convective accelerometer modeling. Special thanks are due to Frederic Mailly and Pascal Nouet not only for providing 2D Ansys script, but also for offering the use of available simulation tools.

Bibliography [1] [2]

[3] [4]

[5] [6] [7]

S. Semancik and R. Cavicchi. Kinetically Controlled Chemical Sensing Using Micromachined Structures. Accounts of Chemical Research, 31(5):279–287, 1998. A.Padmanabhan, M. Sheplak, K.S. Breuer and M.A. Schmidt. Micromachined sensors for static and dynamic shear-stress measurements in aerodynamic flows. Int. Conf. on Solid State Sensors and Actuators, TRANSDUCERS’97, 1(16–19):137–140, 1997. H. Lakdawala and G.K. Fedder. Temperature control of CMOS micromachined sensors. 5th IEEE Int. Conf. on Micro Electro Mechanical Systems, :324–327, 2002. A.M. Shkel, A.A. Trusov, I.P. Prikhodko and S.A. Zotov. Three-dimensional wafer-scale batch-micromachined sensor and method of fabrication for the same. United States Patent 8,567,247, October 29, 2013. J. Hermann. Micromachined measuring cell with arm supported sensor. United States Patent 5,551,294, September 3, 1996. M.L. Lee, C.H. Je, S.S. Lee, S.H. Jung, C.A. Choi and G. Hwang. Micromachined sensor for measuring vibration. United States Patent 7,975,550, July 12, 2011. B.E.Boser and R.T. Howe. Surface micromachined accelerometers. IEEE J. of Solid-State Circuits, 31(3):366–375, 1996.

44 | B. Mezghani et al.

[8]

[9]

[10] [11] [12] [13]

[14]

[15] [16]

[17] [18] [19]

[20] [21]

[22]

[23]

[24] [25]

[26] [27]

T. Mineta S., Kobayashi, Y. Watanabe, S. Kanauchi, I. Nakagawa, E. Wuganuma and M. Esashi. Three-axis capacitive accelerometer with uniform axial sensitivities. J. of Micromechanics and Microengineering, 6(4):431–435, 1996. A. Partridge, J.K. Reynolds, B.W. Chui, E.M. Chow, A.M. Fitzgerald, L. Zhang, N.I. Maluf, T.W. Kenny. A high-performance planar piezoresistive accelerometer. J. Microelectromechanical Systems, 9:58–66, 2000. L.C. Spangler and C.J. Kemp. ISAAC: integrate silicon automotive accelerometer. Sensors and Actuators A, 54:523–529, 1996. S.J. Chen and C.H. Shen. A novel two-axis CMOS accelerometer based on thermal convection. IEEE Trans. on Instrumentation and Measurement, 57(8), 2008. A. Garraud, A. Giani, P. Combette, B. Charlot and M. Richard. A dual axis CMOS micromachined convective thermal accelerometer. Sensors and Actuators A, 170:44–50, 2011. P. Usung, P. Byoungkyoo, M. Il-Kwon, K. Dongsik and K. Joonwon. Development of a dual-axis micromachined convective accelerometer with an effective heater geometry. Microelectronic Engineering 88(3):276–281, 2011. H.-B.Nguyen, F. Mailly, L. Latorre and P. Nouet. Design of a monolithic 3-axis Thermal Convective Accelerometer. Symp. on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP), Barcelona, Spain, 2013. Y. Zhao and Y. Cai. Z-axis thermal accelerometer. United States Patent 7,392,703, July, 2008. A.A.Rekik, B. Mezghani, F. Azaïz, N. Dumas, M. Masmoudi, F. Mailly and P. Nouet. Investigation on the effect of geometrical dimensions on the conductive behaviour of MEMS convective accelerometers. Symp. on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP), Aix-en-Provence, France, 2011. X.B. Luo, Z.X. Li, Z.Y. Guo and Y.J. Yang. Thermal optimization on micromachined convective accelerometer. Int. J. of Heat and Mass Transfer, 38(7–8):705–712, 2002. J. Courteaud, P. Combette and N. Crespy. Thermal simulation and experimental results of a micromachined thermal inclinometer. Sensors and Actuators A, 141:307–313, 2008. B. Mezghani, A. Brahim, F. Tounsi, A.A. Rekik, M. Masmoudi and P. Nouet. From 2D to 3D FEM simulations of a CMOS MEMS convective accelerometer. Int. Conf. on Microelectronics (ICM), Tunisia, 2011. B. Mezghani, F. Tounsi, A.A. Rekik, F. Mailly, M. Masmoudi and P. Nouet. Sensitivity and power modeling of CMOS MEMS single axis convective accelerometers. Microelectronics J., July 2013. B. Mezghani, F. Tounsi and M. Masmoudi. Sensitivity Modeling of dual-axis CMOS MEMS Convective Accelerometers using FEM and Spherical Model. Symp. on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP), Barcelona, Spain, 2013. B. Mezghani, F. Tounsi and M. Masmoudi M. Convection behavior analysis of CMOS MEMS thermal accelerometers using FEM and Hardee’s model. Analog Integrated Circuits and Signal Processing (ALOG), Springer, 2013. B. Mezghani, F. Tounsi, H. Yaich and M. Masmoudi. Conductive Behavior Modeling of Dual-axis CMOS MEMS Convective Accelerometers Using 3D FEM and Spherical Model. Int. Multi-Conf. on Systems, Signals & Devices, (IEEE-SSD’13), Tunisia, 2013. http://www.memsic.com/index.cfm. A. Garraud, P. Combette, F. Pichot, J. Courteaud, B. Charlot and A. Giani. Frequency response analysis of an accelerometer based on thermal convection, J. of Micromechanics and Microengineering 21(3), 2011. H.C. Hardee. Natural convection between concentric spheres at low Rayleigh numbers. Ph.D. Dissertation, University of Texas, Austin, Texas, 1966. L.R.Mack and H.C. Hardee. Natural convection between concentric spheres at low Rayleigh numbers. Int. J. of Heat and Mass Transfer, 11(3):387–396, 1968.

Analysis of micromachined thermal accelerometers

| 45

Biographies Brahim Mezghani received the BSc and MSc degrees from the University of Minnesota, U.S.A. He received the PhD degree from National Engineering School of Sfax (ENIS) in Tunisia, where he is currently holding an assistant professor position. He is currently working on the design, modeling and simulation of new micromachined sensors. This includes both mechanical and electronic conditioning parts. His recent interests include the development of nanomaterials for sensor applications.

Fares Tounsi received the BSc’01 and MSc’03 degrees from National Engineering School of Sfax (ENIS) in Tunisia, and the PhD’10 in Micro and Nano-electronics from Grenoble Institute of Technology (INPG), France. He is currently an assistant professor in Institut Supérieur d’Informatique et de Mathématiques de Monastir (ISIMM), Tunisia. He is currently working on the design, modeling and simulation of new CMOS compatible micromachined sensors. Specifically, he is interested in novel designs of microphones, accelerometers and RF switches. In addition, he is now focused on the field of nanotransducers, evaluation of new materials/structures for MEMS and advanced microsystems. Mohamed Masmoudi was born in Sfax, Tunisia in 1961. He received the BSc’85 from National Engineering School of Sfax (ENIS) and the PhD’89 in Microelectronics from the Laboratory of Computer Sciences, Robotics and Microelectronics of Montpellier, France. From 1989 to 1994, he was holding an Associate Professor position at National Engineering School of Monastir, Tunisia. Since 1995, he has been a Professor at ENIS where he has been engaged in developing Microelectronics in the engineering program of the university, and where he is also the head of the research group Electronics, Microtechnology and Communication (EMC). He is the author and co-author of several papers in the Microelectronic field. He has been a reviewer for several journals. Prof. Masmoudi organized several international Conferences and has served on several technical program committees.

M. Hadj Said, F. Tounsi, P. Gkotsis, M. Masmoudi and L. A. Francis

MEMS-Based Clamped-Clamped Beam Resonator Capacitive Magnetometer Abstract: In this paper, a MEMS resonant magnetometer based on Lorentz force is presented modeled and characterized. A MEMS magnetometer sensor is generally used to detect the variation of an external magnetic field using a resonant structure suspended. In the proposed design, the deformation of this structure, excited at its first resonance frequency through the establishment of a Lorentz force, will be measured by a capacitive sensing method. The Lorentz force is generated by the interaction of an alternative current (AC) which excites the suspended clamped-clamped conducting beam, obtained through a bulk micro-machining, and a 150 mT external static magnetic field. The out-of-plane structure displacement is converted into a capacitance change by a series of interdigitated combs. The frequency of the AC supply current is chosen to be equal to the resonant frequency of the suspended beam, leading to the increase of its deflection magnitude and the overall sensitivity. Throughout this paper, the first-order resonance frequency and the capacity expressions are developed, simulated with Comsol and thereafter compared to the measured values. The resonance frequency simulated and measured is in the vicinity of 15 kHz. On the other hand, the quality-factor, electric model and sensitivity of the sensor, which is found to be around 10 fF/mA, are determined from the characterization of the fabricated sensor prototype. Keywords: Capacitive sensing, Lorentz force, Magnetic field sensor, MEMS Magnetometer, Resonant Sensors. Mathematics Subject Classification 2010: 65C05, 62M20, 93E11, 62F15, 86A22

1 Introduction MAGNETIC-field sensors are mostly based in their operation principle on the Hall effect, magneto-resistive (amorphous and giant magneto-resistors), magnetotransistor, magneto-diode, fluxgate, or other semiconductor effects in order to measure the magnetic fields [1–5]. Magnetometer sensors offer new capabilities

M. Hadj Said, P. Gkotsis, L. A. Francis: Sensors, Microsystems and Actuators Laboratory of Louvain-la-Neuve (SMALL), Université catholique de Louvain, Belgium, email: [email protected]. M. Hadj Said, F. Tounsi, M. Masmoudi: University of Sfax, National Engineering School of Sfax, Electronics, Microtechnology and Communication (EMC), Sfax, Tunisia, email: [email protected]. De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 2, 2017, pp. 47–62. DOI 10.1515/9783110470444-004

48 | Hadj Said et al.

that can be used in different domains of engineering, industry, military or telecommunications [6]. The list of magnetic sensor applications includes position-sensing, noncontact switching [7], vehicle detection [8], navigation [9], mineral prospecting [10], brain function mapping [11], etc... In some biomedical applications, with the high requirements in sensitivity and accuracy, magnetometers should also be small enough and have low power consumption, whereas the performances of most present sensors are not satisfactory. MEMS-based magnetic field sensor provides an opportunity to solve this problem by offering small-size solution for magnetic field sensing. In addition, smaller device can be placed closer to the measurement spots and thereby achieving higher spatial resolution. For MEMS technologies, many resonant magnetic field sensors exploit the Lorentz force principle, where the structure displacement, which vibrates at the resonance, is measured either by optical, piezoresistive, or capacitive sensing techniques [6]. The first section of this article will introduce the operational principle of the resonant sensor. The second section will provide a comparison between the theoretical and the measured resonance frequency values for both types of manufactured prototypes followed by the interdigitated combs capacitance evaluation of the sensor. Then, after the determination of the sensor quality factor, the complete electric model will be deduced and analyzed. Finally, the sensor sensitivity, which is a function of the capacitance variation, was determined.

2 Operational principle Suspended structures have an infinite number of resonance vibration modes (or eigenmodes). The structure deflection magnitude at the resonance frequency is the maximum that can be achieved, thus the resonant sensor mostly uses structures operating at one of their eigenmodes, generally the first vibration mode. Based on this notion, the designed sensor consists of a suspended clamped-clamped beam with comb electrodes defined along its entire length. These electrodes form interdigitated pairs with combs that are anchored to the substrate. When the suspended clamped-clamped beam vibrates in the out-of-plane direction (z-axis), the interdigitated electrodes capacitances change (see Fig. 1). If an alternative current i x , which passes through the suspended beam (along the x-axis), is coupled to a magnetic field By (along the y-axis), a Lorentz force will be generated along the z-direction and subsequently the beam will move out of plane. The Lorentz force expression is given as following: FL = ix By L

(1)

MEMS-based clamped-clamped beam resonator capacitive | 49

where L is the beam length. The beam should be long enough to permit a relatively high number of comb fingers, and eventually a sufficient variation in the capacitance formed by the interdigitated combs. The choice of L is defined by the maximum area offered by the fabrication technology and that can be etched on the substrate. Moreover, the comb width should be minimized in order to reduce the damping effects due to air, while its thickness is also defined by the fabrication process. Two prototypes of this MEMS magnetometer have been fabricated through the SOIMUMPs foundry process (MEMSCAP Inc., USA). The first prototype has a suspended conducting beam thickness equal to 10 μm while the second prototype is equal to 25 μm. The structure geometric parameters that were used for the fabrication of the magnetic field sensor are shown in Tab. 1. In the manufactured layout, three contact pads permit the electrical accesses for measurement; pads A and B allow access to the two ends of the clamped-clamped beam and pad C is connected to the electrodes which are fixed on the substrate and is used for the capacitance measurements.

800

Movable FingerElectrodes

Fixed FingerElectrodes

600 400

y

z x

200 0

C

–200 –400

B

A

Current

Beam –400 –200

0

magnetic field B

200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

Fig. 1. Top view of the MEMS-based magnetic-field sensor.

3 Determination of the resonance frequency The clamped-clamped beam used has a rectangular section shape fixed at its both ends. During the sensor operation, it will be subjected to bending due to the Lorentz force. To maximize the deformation value, the beam will be excited with its resonance frequency. The resonant frequency of suspended structures is usually determined by the Rayleigh method [6]. This method determines the eigenmodes by the ratio between the maximum potential energy and the maximum kinetic energy of the structure [6]. From this definition, the n-eigenmodes frequency formula of a clamped-clamped

50 | Hadj Said et al.

Tab. 1. Used dimensions in the resonant magnetometer sensor design. Part

Beam

Fingers

Sensor parameters

Value

Length (L)

1.969 μm

Width (W)

62.5 μm

Height (h)

10 μm or 25 μm

Length (l)

100 μm

Width (a)

3 μm

Thickness (t)

10 μm or 25 μm

Fingers number (n)

312

Gap between fingers (d)

3 μm

beam is given by the following equation: fn =

R2n 2L2



EI ρA

(2)

where ρ is the density of the structural material, A is the beam section area, E is the Young’s modulus, R n is a constant that depends on the boundary conditions and the natural mode of the beam, its values are given in Tab. 2, and finally I is the area moment of inertia given by the following equation: I=

w h3 12

(3)

Tab. 2. R n -constant values for the first five eigenmodes. Parameter Value

R1

R2

R3

R4

R5

4.738

7.853

10.995

14.131

17.278

Thus after substituting the R1 -value, the expression for the first resonant frequency of the clamped-clamped beam can be given by [12]: 4.7382 EI f1 = (4) ρA 2L2 Within the same objective, a modal analysis of the clamped-clamped beam structure was performed using Comsol Multiphysics in order to determine its resonance frequencies and compare it to the analytical value. Figure 2 shows the first modal

MEMS-based clamped-clamped beam resonator capacitive

| 51

shape of the beam, and Tab. 3 summarizes the first resonant frequency of the 10 μm and 25 μm-thick beam structures obtained by simulations. For symmetry reasons and to save time during the simulation, we modeled only the quarter of the beam (see Fig. 2). The theoretical and simulation results are quite similar, and are summarized in Tab. 3 for the both sensor designed structures.

0

500

40 20 0 –500

Eigenfrequency=15148.737851 Hz

Fig. 2. The beam displacement within the first vibration mode (for the 10 μm-thick beam).

Experimentally, the resonant frequencies are obtained by the laser Doppler velocimetry (Polytec MSA-500), where the beam is actuated by a periodic chirp signal of fixed amplitude which is applied between pads A and B (see Fig. 1). Indeed, the device is placed in a 150 mT static magnetic field generated by a set of two neodymium permanent magnets placed in front of each others in order to obtain a field as homogeneous as possible. The resonance frequencies results exposed in Fig. 3 show a large discrepancy with those found in theoretical and simulation. The difference is caused by the Joule heating effect due to the rms value of the driving current, and the increase of the thermal energy of the beam. Because the beam is clamped at both ends, this increase leads to an expansion of the beam and eventually to its buckling. The localized thermal stress generated by Joule heating plays a major role leading to the resonant frequency modification. In general, when compressive stresses are generated, the structure becomes softer; therefore there is a decrease in the resonant frequency. Otherwise, if the stresses are tensile stresses then the structure becomes more rigid, and the resonant frequency increase. A static analysis using the “Joule heating and the Thermal expansion” modules in Comsol Multiphysics was performed for the two thicknesses of the beam (namely 10 μm and 25 μm). Figure 4 shows the temperature profile in the beam when a current of 50 mA is used. During the simulation, the temperature is fixed at the beam ends to be equal 295 K (∼22°C, ambient

52 | Hadj Said et al. 20.2 Structure 10mm

20 Frequency[kHz]

19.8 19.6 19.4 19.2 19 18.8 20

40

(a) 40.9 40.8 40.7 Frequency[kHz]

60

80

100

120

Current [mArms] Structure 25mm

40.6 40.5 40.4 40.3 40.2 40.1 40 25 (b)

30

35 40 Current [mArms]

45

50

Fig. 3. Measured resonant frequency variation as a function of the supply current for the (a) 10 μm-thick beam (b) 25 μm-thick beam.

temperature) and increasing to 325 K (∼52°C) in the middle for the 10 μm-thick beam and to 313 K (∼40°C) for the 25 μm thick beam. The convective heat transfer coefficient was assumed to be equal to 100 W/m2 /K. In this case, the beam structure becomes more compliant causing the resonant frequency reduction [13]. Figure 3 shows the curve of the resonant frequency shifting as a function of the supply current. Two phenomena can be denoted, for the 25 μm-thick beam we can see the reducing of the resonant frequency because of the inherent compressive stress. However, for the 10 μm-thick beam we can see that the resonant frequency increases when the current is increasing. It is believed that in this case, the non-linear vibration effects become dominant as the Lorentz force increases with the supply current in the beam. On the other hand, the SOIMUMPs fabrication process uses a fine gold layer added on top of the suspended beam. Due to the high thermal expansion coefficient of gold, we can note a greater stress in the latter than in the silicon layer. Indeed, the gold layer tends to expand more but its cohesion with the silicon prevents that. This should

MEMS-based clamped-clamped beam resonator capacitive | 53

325.6 Surface: Temperature (K)

325

320

315

310

305

500

300 0 295

Fig. 4. Temperature variation in the 10 μm-thick beam due to the Joule heating effect.

explain also why the stress in the structure of 10 μm-thick beam is higher than in the other one. During the experiment for the 10 μm-thick beams, we note that when the current increases the curvature of the beam, even in static standby mode, largely increases reaching 3 μm, knowing that the thickness of the gold layer is equal to 520 nm. In this case the nonlinear vibration is presented due to the gold layer and more dominant than the thermal stress effect. Conversely for 25 μm-thick beams structures, this effect is not outstanding since the beam is thicker and the total displacement was in order of nm. To avoid this problem, the 10 μm-thick beams was actuated using a sweeps signal between 17 kHz and 22 kHz, and 22 kHz and 17 kHz respectively shown in Fig. 5. A hysteresis is shown between the two spectra that can be explained by the nonlinear effect of the beam. In fact, Zook et al. [14] found that when the drive voltages increase, the resonant frequency shifts to a higher value, leading to hysteresis depending on the direction of frequency scan. Theoretically and due to the gold layer, some correction adjustment can be introduced to equation (4) resulting:

  w E21 h21 + E22 h22 + E1 h1 E2 h2 + 4h21 + 6h1 h2 + 4h22 EI = (5) 12 (E1 h1 + E2 h2 ) and ρA = ρ1 A1 + ρ2 A2

(6)

54 | Hadj Said et al. 35

1V 17–22 kHz 1V 22–17 kHz

Displacement [nm]

30

X:2.013e+004 Y:2.604e–008

25 X:1.929e+004 Y:1.759e–008

20 15 10 5 0 0

5

10

15 20 25 Frequency[kHz]

30

35

40

Fig. 5. Spectrum of the beam with sweep actuation.

where E1 and h1 are respectively the Young’s modulus and the thickness of the silicon layer, and E2 and h2 are the Young’s modulus and the height of the gold layer, respectively. While, ρ1 and A1 are respectively the density and section of silicon, ρ2 and A2 are the density and the section of the gold layer, respectively. Based on this correction, the resonant frequency results are shown in Tab. 3. We can conclude the same result, is that the gold layer increases the density and induced a resonant frequency higher.

4 Interdigitated combs capacitance evaluation As per design the beam is moving out of plane, the magnetometer is sensitive along the z-axis direction. The theoretical capacitance expression in this configuration is given by:    nε l(t|z|) if |z| < t (7) C(z) =  d  0 if |z| ≥ t where ε is the dielectric constant of the air, n the number of comb capacitance fingers and the others parameters are given in Tab. 1. In order to validate this equation through Comsol Multiphysics©, we have simulated a single finger pair to estimate the total capacitance. Figure 6.a presents the simulated electrical potential distribution between a pair of fingers, in the absence of actuation. While Fig. 6.b presents the capacitance variation as a function of the displacement between the movable and the fixed fingers. Finally, we have to multiply the capacitance found between the two

MEMS-based clamped-clamped beam resonator capacitive

| 55

35

Surface: Electric potential (V)

25um thick beam 10um thick beam

30

1 1 Capacitance[fF]

25

0.8 0.6 0.4

20 15 10

0.2

5

0 –1.4288´10–17

0

(b) –40

(a)

–30

–20

–10

0

10

20

30

40

Displacement[um]

Fig. 6. (a): The electrostatic potential distribution between two fingers simulated by FEM. (b): Capacitance change as a function of the displacement between two fingers.

fingers by the total number of fingers pairs throughout the clamped-clamped beam. Table 3 shows the different results from this simulation.

Tab. 3. Resonance frequency and static total capacitance values, for the two different structures thick. 10 μm-thick structure

25 μm-thick structure

Analytic result

FEM result

Analytic result

FEM result

17.5 –

15.148 2.57

42.98 –

39.465 5.31

First mode Frequency (kHz) Capacitance (pF)

5 Quality factor The quality factor, Q, is defined as the ratio between the energy stored in the system and the energy lost per cycle [15]. If the structure has a high quality factor then a low energy will be needed to keep the resonance at constant amplitude and the electronic circuitry will have a minimum effect on the resonant frequency [16]. Figure 7 presents the displacement of the clamped-clamped beam actuated at 50 mArms as a function of the frequency obtained by laser Doppler vibrometry. The Q-factor can be easily

56 | Hadj Said et al. –340

Displacement[dB]

–360 –380 –400 –420 –440 –460 –480 0

5

10

15

20 25 Frequency[KHz]

30

35

40

Fig. 7. Displacement of the clamped-clamped beam (in dB) vs. the frequency.

Tab. 4. Value of the quality factor derived from the displacement spectrum with an excitation current of 50mA.

50mA

Resonance frequency (kHz)

Wide bandwidth for −3dB (kHz)

19.270

0.38

Quality factor 50.71

calculated from this curve using the following: Q=

f0 ∆f

(8)

where f0 is the resonant frequency of the beam leading to the maximum vibration amplitude, and ∆f is the difference between f1 and f2 frequencies. These frequencies f1 and f2 correspond to vibration amplitudes of −3 dB lower than the maximum (that of f0 ). From Fig. 7, we extract the values needed to determine the quality factor; these parameters are shown in the Tab. 4. The measured Q factor is around 50 for our sensor (for the 10 μm-thick structure).

6 MEMS magnetometer equivalent electric model The parameters of the electric model of the sensors are deduced from measurements of the input and output impedance. Both these quantities are measured using the Agilent 4284A LCR meter on a frequency range between 20 Hz and 1 MHz. The input impedance of the sensor is equivalent to a resistance formed by the metal layer that is deposited on top of the beam. Experimentally, this impedance is measured between

MEMS-based clamped-clamped beam resonator capacitive

| 57

pads A and B (see Fig. 1). In Fig. 8 the input impedance in (Ω) and the corresponding phase in (°) as a function of frequency are plotted for the 10 μm-thick structure. From these curve, the resistance of the Au layer was deduced equal to 5.62 Ω. The output impedance of our structure is also important because it is defined by the total capacitance of the finger electrodes. Experimentally, this impedance is measured between pads A and C (see Fig. 1). From the schematic in Fig. 9 (modulus and phase of output impedance), we can deduce a resistor-capacitor serial circuit, so we measure directly these values (resistor and capacitor serial) using the LCR meter. The values that were obtained from these measurements at different frequencies are summarized in Tab. 5. The magnetometer electric model can be represented as shown in Fig. 10; with R act representing the input impedance, and the impedance C eq , R eq are the equivalent of the RC-dipole which was derived from the output impedance. The beam is actuated between the A and B pads and the capacitive measurement is effectuated between the A and C pads or B and C pads. Finally, the theoretical sensitivity of our sensors can be defined by the following equation  ∆C ∆C ∆z  S= = × B (9) ∆i x ∆z ∆i x  It may be noted that the sensitivity is directly proportional to the current passing through the beam, however increasing this current causes a shift of the resonant frequency, which present a drawback for our resonant sensors. So, we can solve this problem by using a tuning system (electrostatics, thermal, etc.) for the resonant frequency or using other metal layer instead to the gold if the process permits it. To calculate the sensitivity, we can extract its two expression constituent parts from graphs. In fact, the first part can be determined from Fig. 6.b and the second part can be extracted from Fig. 11, which presents the displacement as a function of the actuating rms current. From Fig. 12, we can calculate the total sensors practical sensitivity that is around 10 fF/mA, for the 10 μm-thick structure, and 1 fF/mA for the other one. The sensor sensitivity depends mainly on the chosen geometrical parameters in the SOIMUMPs fabrication process and the quality factor. The sensor performance can be improved by using a vacuum package. Indeed, in vacuum the quality factor increase and the resonant beam will get larger amplitudes by reducing the damping effect.

58 | Hadj Said et al.

Tab. 5. Capacity and resistance values obtained from the output impedance measurements at different frequencies for the 10 μm-thick structure. Frequencies (kHz)

0.5

1

100

1000

Capacity (pF)

6.80

6.76

6.72

6.61

267.55

223.313

2.01

596.970

Resistance (MΩ)

Impedance[Ω]

40 30 20 10 0 101

102

4 103 10 Frequency[KHz]

105

106

102

103 104 Frequency[KHz]

105

106

105

106

Phase[deg]

80 60 40 20 0 101

Fig. 8. Experimental measurement of the input impedance.

Impedance[Ω]

40 30 20 10 0 1 10

102

104 103 Frequency[KHz]

Phase[deg]

80 60 40 20 0 1 10

2

10

10

3

4

10 Frequency[KHz]

Fig. 9. Experimental measurement of the output impedance.

10

5

10

6

MEMS-based clamped-clamped beam resonator capacitive

R_act/2

| 59

R_act/2 C_eq

A

B

R_eq C

Fig. 10. Electric model of the sensor. 4

80

3

displacement[um]

displacement[um]

3.5

2.5 2 1.5 1

70 60 50 40

0.5 0 0 (a)

10

20 30 current[mArms]

40

30 25

50

30

(b)

35 40 current[mArms]

45

50

Fig. 11. Measured displacement of the beam variation as a function of the supply current for: (a) 10 μm-thick beam, and (b) 25 μm-thick beam.

2.6 2.55 2.5 Capacitance[pF]

2.45 2.4 2.35 2.3 2.25 2.2 2.15 2.1 0

10

20 30 Current[mArms]

40

50

Fig. 12. simulated capacitance as function of actuation current for the 10 μm-thick beam.

7 Conclusion In this paper a MEMS-based magnetometer with capacitive detection was presented founded on an out-of-plane Lorentz force excitation. We have simulated the first

60 | Hadj Said et al.

vibration mode of the clamped-clamped beam and it has been compared with the measured value. The sensor resonance frequency was founded around 15.146 kHz with Comsol for the 10 μm-thick beam and 39.465 KHz for the 25 μm-thick one. We noted a difference between the simulated and measured value when the beam was actuated by an alternative current. This difference is a result of the Joule heating effect for the 25 μm-thick beam, as it generates a compressive stress, and the non-linear effect, mostly caused by the presence of the gold layer, for the 10 μm-thick beam. We noticed also a difference between the simulated and the measured capacitance, which was about 2.57 pF (the measured was about 6.7 pF) for the 10 μm-thick structure. This difference is due to the effect of the electrodes and pad effect (see Fig. 1) which has not been considered in the simulated model since we modeled just one pair of fingers, and we multiply the capacitance found by the total number of fingers pairs throughout the clamped-clamped beam. The measured quality factor was about 50, and the electric equivalent model of the 10 μm-thick sensor is deduced from the measurement of the input and output impedance using the LCR meter. It was found that the input impedance is a simple resistance equal to 5.62 Ω and the output resistance is formed by the RC serial circuits with R was about 223.313 MΩ and C was about 6.7 pF. Finally, the sensor sensitivity is found to be about 10 fF/mA (for the 10 μm-thick) and 1fF/mA (for the 25 μm-thick). The sensitivity is significantly affected by the current passing through the beam that causes resonant frequency shifts. This problem can be solved by using tuning electrostatics systems by applying a DC voltage between fingers to adjust the resonant frequency. Works to overcome the characterization problems encountered during our first design; we note the shifting of the resonance frequency and the quality factor reduction are ongoing, and further optimized structures are also under study as adding spring on the ends of the beam. Acknowledgements: The authors would like to thank Mr. Pierre Courtois for having designed and fabricated the sensor in the SOIMUMPs process flow.

Bibliography [1] [2] [3]

[4] [5] [6]

S. Kordic. Integrated silicon magnetic-field sensors. Sensors and Actuators, 10:347–378, 1986. R. S. Popovic, J. A. Flanagan and P. A. Besse. The future of magnetic sensors. Sensors Actuators A, 56:39–55, 1996. R. Gottfried-Gottfried, W. Budde, R. Jahne, B. Sauer, S. Ulbicht and U. Wende. A miniaturized magnetic-field sensor system consisting of a planar fluxgate sensor and a CMOS readout circuitry. Sensors and Actuators, 54:443–447, 1996. M. J. Caruso. Applications of magneto resistive sensors in navigation systems. Sensors and Actuators, SAE SP-1220:15–2, 1997. P. Losantos, C. Cané, D. Flandre and J.-P. Eggermont. Magnetic-field sensor based on a thin-film SOI transistor. Sensors and Actuators A, 67:96–101, 1998. A. L. Herrera-May, L. A. Aguilera-Cortés, P. J. García-Ramírez and E. Manjarrez. Resonant magnetic field sensors based on MEMS technology. J. of Sensors, 39:7785–7813, 2009.

MEMS-based clamped-clamped beam resonator capacitive

[7] [8]

[9]

[10]

[11]

[12] [13] [14]

[15]

[16]

| 61

A. Arfan and D. K. Potter. A new contactless trackball design using Hall effect sensors. Sensors and Actuators A, 147:110–114, April 2008. W. Hernandez. Improving the Response of a Rollover Sensor Placed in a Car under Performance Tests by Using a RLS Lattice Algorithm. Sensors Journal, Molecular Diversity Preservation, Int. (MDPI), 5:613–632, 2005. F. Ayazi and K. Najafi. Design and Fabrication of A High Performance Polysilicon Vibrating Ring Gyroscope. 11th IEEE/ASME Int. Workshop on Micro Electro Mechanical Systems, Heidelberg, Germany, January, 1998. S. D. Senturia. Perspectives on MEMS, Past and Future: The Tortuous Pathway From Bright Ideas to Real. 12th IEEE Int. Conf. on Solid State Sensors, Actuators and Microsystems, Boston, June, 2003. B. Ziaie, T.W. Wu, N. Kocaman, K. Najafi and D.J. Anderson. An Implantable Pressure Sensor Cuff For Tonometric Blood Pressure Measurement. Technical Digest, Solid-State, Sensor and Actuator Workshop, June, 1998. W. Weaver, S. P. Timoshenko and D. H. Young. Vibration Problems in Engineering. 5th Edition, Wiley Publishers, 1990. T. Remtema and L. Lin. Active frequency tuning for micro resonator by localized thermal stressing effects. Sensors and Actuators, 91:326–332, 2001. J. D. Zook, D. W. Burns, H. Guckel, J. J. Sniegowski, R. L. Engelstad and Z. Feng. Resonant micro beam strain transducers. 6th Int. Conf. Solid-State Sensors Actuators (Transducers’91), :529–532, San Francisco, CA, June 1991. F. Ahmad, J. Ojur Dennis, N. H. Hamid, M. Haris and M. Khir. Design and Simulation of Mechanical Behavior of MEMS-based Resonant Magnetic Field Sensor with Piezoresistive output. Int. Conf. on Mechanical and Electrical Technology, September, 2010. M. Elwenspoek and R. J. Wiegerink. Mechanical Microsensors. Springer-Verlag: Berlin, Heidelberg, Germany, 2001.

Biographies Mohamed Hadj Said was born in Monastir, Tunisia in 1987. He received the B.Sc.’10 and MSc’12 degrees both from the Higher Institute of the informatics and mathematics of Monastir (ISIMM, Tunisia). His Master is done in collaboration with the laboratory of Sensors, Microsystems and Actuators of Louvain-la-Neuve (SMALL), Belgium. He is currently a PhD student, in the National Engineering School of Sfax (ENIS), working on the novel design, modeling and simulation of a MEMS microphone based in magnetic detection in CMOS Technology in collaboration with TIMA Laboratory France. Furthermore, he interests in magnetic sensors, surface acoustic wave modeling. Fares Tounsi received the BSc’01 and MSc’03 degrees from the National Engineering School of Sfax (ENIS) in Tunisia, and the PhD’10 in Micro and Nano-electronics from Grenoble Institute of Technology (INPG), France. He is currently an assistant professor in the Institut Supérieur d’Informatique et de Mathématiques in Monastir (ISIMM), Tunisia. He is currently working on the design, modeling and simulation of new CMOS compatible microsystems micromachined sensors. Specifically, he is interested in novel design of microphone, accelerometers and RF switches. In addition, he is now focused on the field of nanotransducers, evaluation of new materials/structures for MEMS and advanced microsystems.

62 | Hadj Said et al. Petros Gkotsis was born in Athens, Greece and received his degree in Physics from the National and Kapodistrian University of Athens in 2002. In 2010 after the PhD degree award from Cranfield University, England he joined Université catholique Louvain at Belgium as a postdoctoral research assistant investigating the reliability of MEMS in harsh environments. His current research interests include the study of functional materials, thin-film characterization, design and modelling of microsystems, effects of radiation and extreme temperatures on the reliability of microelectromechanical systems and the development of processes to incorporate new materials in microfabrication. Mohamed Masmoudi was born in Sfax, Tunisia in 1961. He received the B.Sc.’85 from National Engineering School of Sfax (ENIS) and the Ph.D.’89 in Microelectronics from the Laboratory of Computer Sciences, Robotics and Microelectronics of Montpellier, France. From 1989 to 1994, he was holding an Associate Professor position at National Engineering School of Monastir, Tunisia. Since 1995, he has been a Professor at ENIS where he has been engaged in developing Microelectronics in the engineering program of the university, and where he is also the head of the research group Electronics, Microtechnology and Communication (EMC). He is the author and coauthor of several papers in the Microelectronic field. He has been a reviewer for several journals. Prof. Masmoudi organized several international Conferences and has served on several technical program committees. Laurent A. Francis (MEng ’01, PhD ’06) holds the Microsystems Chair position at the Université catholique de Louvain (UCL, Belgium) as Associate Professor and is the leading member of the Sensors, Microsystems and Actuators Laboratory of Louvain (SMALL). His scientific interests are related to ultra-low power microsensors for biomedical applications or operation in harsh environments, on bio-inspired approaches and on micro- and nano-fabrication technologies including in particular atomic layer deposition. His PhD thesis was related to acoustic/optical biosensors at IMEC, the Interuniversity MicroElectronics Center located in Leuven, Belgium. Between 2000 and 2007 he was with imec as researcher, successively in the Biosensors and RF-MEMS groups. In 2011, he was a visiting professor at the Université de Sherbrooke, Canada. He is regular member of the IEEE. He has authored or co-authored more than sixty publications in international scientific journals, conferences, and book chapters and holds one patent.

G.U. Gamm and L.M. Reindl

Range Extension for Single Hop Wireless Sensor Networks with Wake-up Receivers Abstract: Wireless Wake-up receivers are used whenever a long lifetime and a permanent accessibility is required. The main disadvantage they have is their short range of communication of a few meters. We therefore present in this work an infrastructure for a single hop network that makes use of a powerful main node that can send out wake-up signals with up to +20 dBm. The end point nodes have an integrated wake-up receiver and consume 3.5 μA of current. The wake-up range was measured up to 90 meters in an open air field. Keywords: Wake-Up, WSN, Low Power. Mathematics Subject Classification 2010: 65C05, 62M20, 93E11, 62F15, 86A22

1 Introduction A wireless infrastructure where a host node communicates with a number of client nodes can be found in different scenarios like a smart metering application or the surveillance of soil parameters in farming. Often the information gathered by the client nodes is needed only once a day or even only once a week. If the point of time is not known when the host demands the data from the clients, the clients need to have a radio listening constantly to a channel. A normal low power receiver consumes about 15 mA of current in receive mode. When the client is powered by a standard CR2032 coin cell with 230 mAh the battery would be depleted in less than one day. In this work we use client nodes that have an integrated wake-up receiver and consume only 3.5 μA of current. With a normal CR2032 coin cell a theoretical standby time of more than 8 years is achieved. We achieve the low power consumption in receive mode by using a 125 kHz receiver. To avoid large antennas that can not be integrated on a small PCB we modulate the 125 kHz wake-up signal in the sender on a 868 MHz carrier using OOK (On Off Keying). In the receiving clients we demodulate the incoming signal using a Schottky Diode followed by a low pass filter. Only the envelope signal is then passed to the 125 kHz receiver. This way we combine the low current consumption of receivers working at low frequencies with the small antenna size of receivers working at higher frequencies. In most network topologies the host node comes with a wired power source or at least has an easy changeable battery. We therefore incorporated in the host node an RF-frontend that enables an output power of up to +20 dBm. This

G.U. Gamm and L.M. Reindl: Laboratory for Electrical Instrumentation, Department of Microsystems Engineering - IMTEK, Freiburg, Germany, email: [email protected]. De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 2, 2017, pp. 63–74. DOI 10.1515/9783110470444-005

64 | G.U. Gamm and L.M. Reindl

way the wake-up signal can penetrate walls and can wake-up sleeping clients inside buildings which is especially important for smart metering applications. The remainder of this paper is structured as follows. Section 2 gives an overview on related work. Section 3 presents the realized network topology. Section 4 introduces the client node and shows some measurements. Section 5 introduces the host node and gives some measurements. The final conclusion and a further outlook will be given in section 6.

2 Related Work In [1] Ansari et al. present a TelosB sensor node operating in the 868 MHz ISM band with included address decoding. Decoding is done by a separate microcontroller. The achieved wake-up range is up to 10 meters. In [2] Lun Cuifen et al. present a Zigbee based system for reading out meters wireless. The electronic attached to the meters consists of a CC2430 System on Chip with integrates ZigBee functionality. The system is especially designed for rural areas. The authors connect their devices to the standard power network. Therefore an additional power supply is needed for the device which increases the costs. Another ZigBee based system is presented in [3]. Baoding Zhang and Jialei Liu present a system where a so called data concentrator collects data via Zigbee and pass them via GPRS to a gateway. The paper states that the power consumption is one of the main problems, but there is no solution given. In [4] Gamm et al. give a more detailed description of the wake-up receiver circuit as well as additional measurements. Other examples of wake-up receiver circuits can be found by Gu et al. in [5] and by Van der Doorn et al. in [6]. In [7] Thomas Wendt and Leonhard Reindl describe different methods to wake-up a sleeping sensor node. One of the presented methods is a Frequency Diversity Wake-Up scheme in which also an Atmel wake-up receiver operating at 125 kHz is used. Wendt calculates the lifespan of a sensor node with such a wake-up receiver up to 4–5 years.

3 Network Topology We propose a star network topology. The central node of the network is the host node which can send out coded wake-up signals to trigger selective nodes from their sleep to active mode. Every client node that wakes-up automatically transmits its collected and stored data to the host node. In case of a smart metering application the client nodes

Single hop wireless sensor networks with wake-up receivers

| 65

would be installed close to the metering device. The host node could be a handheld device that is used by a person to collect the metering data while driving by in a car. Fig. 1 shows a diagram of the realized network topology.

-up Wake

Client-Node

Data

Wak e-up Host-Node

Data Client-Node

Fig. 1. Diagram of the realized network topologie.

Impedance Matching

Rectifier

Lowpass

Control

Antenna Switch

Wake-Up Main Transceiver

Microcontroller

Wake-up Receiver

Fig. 2. Blockdiagram of the sensor node. In sleep mode all incoming signals are routed to the upper path. After demodulation and low pass filtering the AS3932 chip detects a valid wake-up signal and switches the client to active mode.

4 Client Node 4.1 Client Hardware Design The main component of the client node is the CC430F5137 [8] System on Chip from Texas Instruments. It includes a low power microcontroller core together with an integrated radio transceiver of type CC1101 in one single die. Coming from the SMA jack towards the center of the board follows the ADG918 [9] antenna switch that is used for selecting the wake-up path or the connection to the radio of the CC430 controller. On top of the antenna switch follows the demodulation circuit consisting of impedance

66 | G.U. Gamm and L.M. Reindl

matching, Schottky HSMS285C diode and low pass filter. The AS3932 [10] is a wake-up receiver working at 125 kHz with included address correlation. When receiving a valid wake-up signal it triggers the microcontroller from sleep to active mode. Fig. 2 shows a block diagram of the client node. For the client node a four layer design on FR4 substrate was created. Additional components are push buttons, JTAG programming interface, LEDs and a 26 MHz crystal. The impedance matching of the main radio was done using a chip balun from Johanson Technologies. The main power supply is a CR2032 coin cell. This type of battery is small and very economic. The backside of the board accommodates the blocking capacitors. A photo of the client node can be seen in Fig. 3.

CC430F5137

ADG918

AS3932

Demodulation

Fig. 3. Photo of a client node manufactured on a four layer FR4 substrate.

4.2 Client Software In normal operating mode the node will be in the sleep mode. All peripherals and components are switched off to save energy except the 125 kHz wake-up receiver. The antenna switch is in such position, that incoming signal will be routed to the demodulation and low pass circuit. If the wake-up receiver chip detects a valid wake-up signal it will interrupt the microcontroller from its sleep mode. The microcontroller then reads out the metering data, turns on the main transceiver and toggles the antenna switch so that the main transceiver is connected with the antenna. Then the metering

Single hop wireless sensor networks with wake-up receivers

|

67

data is sent out using GFSK modulation scheme with included CRC check. The flow chart of the nodes operating system can be seen in Fig. 4.

4.3 Client Measurements Current consumption measurement of the complete node in sleeping mode was done using a Keithley 6514 System electrometer. The current was measured to 3.5 μA of which 2.8 μA consumes the wake-up receiver chip and about 0.7 μA the CC430 controller in low power mode 4. When using a 230 mAh coin cell we can calculate the maximum lifetime of the client node in sleep mode by the equation 1.

Start

Turn on main radio

permanent Send data Incoming Signal?

No

Yes

Toggle Antenna Switch

Interrupt μC μC to sleep mode Toggle Antenna Switch End

Fig. 4. Flowchart of the nodes operating system.

Tmaxidle =

230 mAh = 65714 h 3.5 μA

(1)

This equals to 7.5 years of standby time in which the node can always be woken up by receiving a valid wake-up signal. We can define an interval Tint in which the client node wakes-up once and sends back the stored data. The length of this interval can be seen in Fig. 5 on the x-axis. On the y-axis the resulting lifespan of the node is plotted. The overall lifetime does not change significantly due to the current needed to send

68 | G.U. Gamm and L.M. Reindl

back the stored data. In a longterm scenario the majority of the power is consumed by the current in sleep mode.

8 7

Lifespan Tnode (years)

6 CR-2032 (230mAh)

5 4 3 2 1 0 0

100 200 300 400 500 600 700 800 900 1000 Time Interval Tint (s)

Fig. 5. On the x-axis the interval Tint is plotted. It represents the span of time in which the sleeping node is woken-up once and transmits its stored data back to the host. The resulting lifespan for each interval is plotted on the y-axis.

5 Host Node 5.1 Host Hardware Design The host node is controlled by an CC430F5137 System on Chip that combines microcontroller and transceiver IC in one package. For impedance matching a chip balun from Johanson Technologies is used. To suppress sporadic emissions at unwanted frequencies a B3716 SAW from Epcos has been inserted in the antenna path. Since the wake-up signal has to pass concrete walls to reach the sleeping client nodes a high sending power is of advantage. We therefore included the CC1190 RF front end from Texas Instruments [11] on the host node. It increases the output power up to +20 dBm. For transferring the received data to the PC a FT232 UART to USB chip is used. The host node can be powered using four AA batteries or using the USB connection. A jumper selects the different power supply. To guarantee a stable operation a MIC5318 drop out regulator is used. Further components are a push button, three LEDs, an On/Off Switch and a JTAG programming interface. In Fig. 6 a block diagram of the host node can be seen. Fig. 7 shows a photo of the assembled host node.

Single hop wireless sensor networks with wake-up receivers

69

Quartz

USB-to-UART

Microcontroller + Transceiver

|

Chip Balun

Voltage Regulator

SAW-Filter

RF Frontend

LEDs

Fig. 6. Blockdiagram of the host node.

FT232R-USB

MIC5318

CC430F5137

B3716 SAW

CC1190

Fig. 7. Foto of the assembled host node.

5.2 Host software The software for the host node starts with an initialization routine. After configuring the transceiver a main state machine is started. When receiving a start command via UART or when the push button is pressed the node sends out a coded wake-up signal for client node 1. Afterwards the transceiver is switched to receive mode to listen for the incoming data of node 1. Then a wake-up packet for node 2 is built and the process

70 | G.U. Gamm and L.M. Reindl

is repeated. The received data is passed via UART to a connected computer where it can be stored and visualized. A flowchart of the software can be seen in Fig. 8

5.2.1 Wake-Up signal buildup The whole wake-up signal is built by modulating a 125 kHz square wave signal on an 868 MHz carrier using OOK (On Off Keying). An additional address information for selective wake-up of nodes can be modulated on the 125 kHz signal by again using OOK. The modulation steps are shown in Fig. 9. The OOK modulation of the 125 kHz square signal on the 868 MHz carrier in the CC430F5317 is done by setting the data rate to 250 kbit/s and by sending out constantly the databyte 0xAA. This results in a subsequently turned on and off carrier signal. The resulting envelope then represents the 125 kHz square signal. Figure 10 shows the databyte 0xAA and the resulting carrier signal when sent out with 250 kbit/s.

Start

Wake Node 2

Idle

Listen for Data 2

• • Interrupt

• No

Yes

Send Data via UART

Wake Node 1 Idle Listen for Data 1 End

Fig. 8. Flowchart of the hosts operating system.

Single hop wireless sensor networks with wake-up receivers

|

71

125 kHz Period

868 MHz

Carrier Modulation

Data Modulation 1

0

1

0

1

0

1

1

0

1

0

0

1

Fig. 9. Modulation steps of the wake-up signal.

0xAA

1

0

1

0

1

0

1

0

868 MHz Carrier

8 ms = 125 kBit/s >

>

4 ms = 250 kBit/s

Fig. 10. Sending a 0xAA byte with a data rate of 250 kbit/s results in the modulation of a 125 kHz square wave signal on the 868 MHz carrier.

5.3 Host Measurements To have an idea of the amount of frequencies that are involved in modulating the 125 kHz wake-up signal on the 868 MHz carrier a measurement with an Rhode and Schwarz ZVL Spectrum Analyzer was done. The host node was programmed to send the wake-up signal in an infinite loop without any delay in between. The antenna signal was fed directly to the spectrum analyzer. For safety reasons a 10 dBm attenuator was inserted. The resulting spectrum can be seen in Fig. 11. Due to the attenuator the real amplitude values are 10 dBm higher than shown in the figure.

72 | G.U. Gamm and L.M. Reindl Amp in dBm

10 0 –10 –20 –30

f in MHz 840

860

880

900

Fig. 11. Spectrum of the wake-up signal. The amplitude values seen on the y-axis are measured with the attenuator in line. The real values are 10 dBm higher.

5.4 Wake-up Distance Measurement To determine the maximum range that host and client can be separated a distance measurement has been done. The host node was programmed to send a wake-up signal with included address information in an infinite loop. The sleeping client node was programmed to wake-up at the same address. Both host and client nodes were mounted to poles of one meter height. The host node had a fixed position whereas the client node was moved successively away. Switching from sleep to active mode was indicated in the client node by turning on and off an LED. The maximum range we achieved by accepting a packet error rate (PER) of 5 % and a sending power of

Range in m Friis–Formula 80

60

40 20 Pout in dBm 0

–10

0

10

20

Fig. 12. Wake-up range as a function of output power using Friis transmission equation.

Single hop wireless sensor networks with wake-up receivers

| 73

+20 dBm in the host where 90 meters in an open free field. The theoretical possible wake-up range can be calclulated using Friis transmission equation. Solved for r it looks as follows: PS GS GE c (2) r= 4πf PE Figure 12 shows a plot of the output power of the sender on the x-axis and the resulting wake-up range on the y-axis. The measurement point is represented by a blue dot and is in the theoretical expected range.

6 Conclusion and Outlook In this work we presented a wireless infrastructure consisting of one host node and three client nodes with included wake-up receivers. While in sleep mode the clients consume 3.5 μA of current but can be woken up by a correct wake-up signal. The wake-up signal itself consists of an 125 kHz signal modulated on an 868 MHz carrier that is send out by the host node with a maximum output power of +20 dBm. When woken up, the client nodes switch to their main radio transceiver and send out their collected data. The host node can be powered and controlled by an connected notebook which makes the system usable for mobile applications. Further work can be done to increase the sensitivity of the sleeping nodes since the main radios range is still significant higher than the wake-up range. Acknowledgment: This work has partly been supported by the German Research Foundation (DFG) within the Research Training Group 1103 (Embedded Microsystems).

Bibliography [1]

[2]

[3]

[4]

J. Ansari, D. Pankin and P. Mahonen. Radio-triggered wake-ups with addressing capabilities for extremely low power sensor network applications. IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), :1–5, September 2008. L. Cuifen, Z. Xiaoqin, L. Yanping and L. Ce. The electric meter reading system in rural areas based on wireless micro-computer. Int. Computer Design and Applications (ICCDA) Conf, vol. 1, 2010. B. Zhang and J. Liu. A kind of design schema of wireless smart water meter reading system based on zigbee technology. Int. E-Product E-Service and E-Entertainment (ICEEE) Conf, :1–4, 2010. G. U. Gamm, M. Sippel, M. Kostic and L. M. Reindl. Low power wake-up receiver for wireless sensor nodes. 6th Int. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Conf., :121–126, 2010.

74 | G.U. Gamm and L.M. Reindl

[5]

L. Gu and J. Stankovic. Radio-triggered wake-up for wireless sensor networks. RealTime Systems, vol. 29, no. 2, pp. 157–182, 2005. [6] B. van der Doorn, W. Kavelaars and K. Langendoen. A prototype low-cost wakeup radio for the 868 mhz band. Int. J. Sensor Networks, 5:22–32, 2009. [7] T. Wendt and L. Reindl. Wake-up methods to extend battery life time of wireless sensor nodes. Conf. on Instrumentation and Measurement Technology (IMTC), :1407–1412, 2008. [8] CC430F513x MSP430 SoC with RF Core, Texas Instruments, sLAS554E, :43, 2009. [9] ADG918/ADG919, Analog Devices, 2008. [10] AS3932 3D Low Frequency Wakeup Receiver, austriamicrosystems AG, revision 1.2, 2009. [11] CC1190 850–950 MHz RF Front End, Texas Instruments, sWRS089A, :2, 2009.

Biographies G.U. Gamm did his Diploma in Electrical Engineering and Information Technology at the Karlsruhe Institut of Technology (KIT), Karlsruhe, Germany. Currently he is doing his PhD in Microsystems Engineering at the Institut of Microsystems Technology (IMTEK) in Freiburg, Germany. He is member of the PhD program “Embedded Microsystems” and his work is concerned with wake-up receivers for embedded microsystems.

L.M. Reindl is head of the Laboratory for Electrical Instrumentation at the Department of Microsystems Engineering, of the University of Freiburg since May 2003. He received a Dipl. Phys. degree from the Technical University of Munich, Germany in 1985 and a Dr. Sc. Techn. degree from the University of Technology Vienna, Austria in 1997. From 1999 to 2003, he was a university lecturer for communication and microwave techniques at the Institute of Electrical Information Technology, Clausthal University of Technology. His research interests include wireless sensor and identification systems, and surface acoustic wave devices.

A. Ghorbel, M. Jallouli, L. Amouri and N. Ben Amor

A HW/SW Implementation on FPGA of Absolute Robot Localization Using Webcam Data Abstract: This paper presents an implementation of absolute robot localization algorithm using FPGA technology. The adopted localization method uses webcam tracking images. This technique has been developed and implemented for the motion of the robot from an initial position towards another desired position. Firstly, we have validated the proposed approach on PC platform using C language and OpenCV library. Secondly, to facilitate robot autonomous navigation, a mixed HW/SW implementation was been developed using a high performance version of the NIOS processor coupled with a custom hardware accelerator. Experimental tests on Altera Cyclone III FPGA Starter Kit show an improvement of 85% over purely software execution which proves the effectiveness of this proposed architecture. Keywords: HW/SW implementation, Autonomous navigation, Robot localization, Altera FPGA, Webcam data, Image processing. Mathematics Subject Classification 2010: 65C05, 62M20, 93E11, 62F15, 86A22

1 Introduction Robot navigation is a quickly developing area in the science of robotics. The rapid technology progress of sensor and image gives new opportunities for autonomous robot navigation. Mobile robots are particularly being used as a substitute for humans or to do simple work that is either in closed environment or outside. It is becoming increasingly important to be able to accurately determine the position of a robot in its environment, as well as to manage all the related electronic, mechanical and software issues. To make truly autonomous robots, one of the issues to resolve is robot localization. For self localization, a robot has access to absolute and relative measurements giving it feedback about its driving actions and the situation of the environment around it.

A. Ghorbel, M. Jallouli, N. Ben Amor: University of Sfax, National Engineering School of Sfax, Research Laboratory on Computer and Embedded Systems (CES), Sfax, Tunisia, emails: [email protected], [email protected], [email protected]. L. Amouri: University of Sfax, National Engineering School of Sfax, Research Laboratory on Control and Energy Management (CEM), Sfax, Tunisia, emails: [email protected], [email protected]. De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 2, 2017, pp. 75–92. DOI 10.1515/9783110470444-006

76 | A. Ghorbel et al.

2 State of the art Relative localization consists in evaluating the position and orientation using only inertial sensor data. These data can be displacement information (odometer), speed information (velocity) or acceleration information (accelerometer). Nevertheless, it cannot be used especially for long and winding trajectories [1], since it suffers from several drawbacks. Though the technique is simple, it is prone to error due to imprecision in modeling, noise, drift and slip [2]. Since the position estimation is based on earlier positions, the error in the estimates increases over time. Absolute localization provides position measurements based on observations made from the environment. This position information is independent of previous position estimates [3]. The location is not derived from integrating a sequence of successive measurements, but directly from one measurement. Unlike relative position, the error in the position does not grow unbounded [3]. The major disadvantage of absolute measurements is their dependence on the characteristics of the environment. In order to compensate the drawbacks of the two techniques, substantial improvement is provided by applying Kalman Filtering techniques [4]. These filters can estimate states of noisy systems in noisy environment. Another approach, presented in [5, 6], adapts the position and the orientation of a mobile robot through a weighted Extended Kalman Filter (EKF). These methods require too heavy computing capacity for a mobile robot to perform a task. Other disadvantages are either the short range of used sensors like infrared sensors or the necessity to know the initial position of the robot. Other solution, presented in [7], uses a method which permits the vehicle to correct its drift by direct observation using a unique embedded CCD camera on a mobile robot. In [8], another localization technique is presented. It uses the correspondence between a current local map and the global map previously store in memory. In [9], a method calculates the position of the robot in order to intercept a moving target through visual feedback. The most significant disadvantage of these methods is the necessity to know the starting position of the mobile. In recent years, the most advanced digital technologies were introduced into robotic control applications: DSP and FPGA. In [10], a multi-DSP platform, based on TIs DSPs from the C2000/C5000 families is used for motion control and data processing on the mobile robot F.A.A.K. This technology is particularly efficient for implementation of complex localization technique. The DSP internal parallel structure is suitable for image processing algorithm. The emergence of complex and low power FPGA offers a flexible and realistic approach to design high performance in a faster time and at lower costs. It allows to define user programmable hardware subsystem which can be easily updated. Thus, the integration of FPGA can carry out a lot of computing-intensive and time-critical tasks such as information acquisition and data procession in robot control applications [11].

FPGA Implementation of Absolute Robot Localization | 77

Azhar and Dimond in [12], use FPGAs for the implementation of control and sensor fusion algorithms in the inertial navigation system of a Mobile Inverted Pendulum (MIP) robot. The FPGA technology, despite its little use in robotics, has significant advantages especially important capacity that allows to use a single PFGA to control a robot especially for parallel applications. A hardware implementation of artificial neural network used for visual trajectory control of a mobile robot using FPGA is presented in [13]. Another application, in [14], consists on interfacing all the modules used by the robot to detect obstacles and control the robot speed. These different modules are actuators, sensors, wireless transmissions circuits. A fuzzy logic and an hybrid fuzzy logic controller for robotic navigation have been carried out using FPGA in [15, 16]. The researches have demonstrated that the FPGA is an interesting solution that combines satisfactory performance and cost design for robotic applications. The proposed technique presented in this paper consists on combining sensors measurements with external absolute data coming from a tracking camera, to reduce the encoder position errors and provide the best estimate of the robot position. Since, there are few work on FPGA in robot localization area, we decide to implement this absolute localization algorithm (based on webcam data) on FPGA embedded system. The complete technique will be presented on ongoing publications. The paper is organized as follows. In section 3, we describe the proposed approach for absolute localization. Then, we present the different algorithms used to determine absolute position of the robot in the test platform by exposing progressively validation results. In section 4, we detail the HW/SW implementation on the Altera FPGA embedded system.

3 Description of the absolute localization algorithm The proposed absolute localization algorithm is based on webcam data. The provided images are treated with various image processing algorithms to obtain the position of the mobile robot on a map formed by two axis cartesian coordinate system. As we shown in Fig. 1, the external absolute data are obtained from a camera mounted on the ceiling of the test environment and ensured the absolute localization. The webcam provides a color image which will subsequently be handled by an image processing program to determine firstly, the reference system and secondly the robot position. The robot used in this work is the Mini Khepera II (Fig. 2). It is equipped with a 68331 Motorola processor with 25 MHz of frequency, 8 Infrared proximity and ambient light sensors and a serial port providing communication with the PC (sending and receiving commands). This approach is based on three steps as shown in Fig. 3.

78 | A. Ghorbel et al.

Fig. 1. The experimental environment.

Fig. 2. The mobile robot Khepera II.

Input Grayscaling Sobel Filter Thresholding Erosion Image

Step 2

Hough Transform

Robot initial position

Creation of the cropped rectangle

Current robot position

+ Reference Image

Grayscaling Sobel Filter Thresholding

Hough Transform

– landmark

Step 1 Step 3 Robot real position

Fig. 3. The adopted approach.

Σ

k

FPGA Implementation of Absolute Robot Localization

|

79

The step 1 consists on the localization of the four landmarks and definition of the robot cartesian coordinate system set up against which the position of the robot will be calculated. In the step 2, we calculate the current position of our robot in a sequence of images. In step 3 and last step, we estimate the position of the robot on the platform. This technique has been implemented and tested on PC as a first step, under Visual Studio with C language using OpenCV Library. OpenCV is an open source and free computer vision library. The only OpenCV features used in application are loading and displaying images in graphical windows (The images are captured from a camera and saved in the hard disk of PC). No predefined image processing function was used. This is in order to be able to embed quickly the application and facilitate the hardware accelerator’s design. It is important to mention that when this technique was established, OpenCV can not be cross compiled on the Nios-II processor. The three steps shown previously will be detailed in this section.

3.1 The robot Cartesian Coordinate System The aim of this part is to identify the labels used for the cartesian coordinates system set up against which the position of the robot will be calculated. We extract the pixel coordinates of the reference system from a reference image, which is captured and stored in advance. This image, as shown in Fig. 4, describes the workspace of the mobile robot limited by four landmarks placed on the corners. After extracting coordinates of the landmarks, we can define our reference system shown in Fig. 5.

Fig. 4. The reference image.

Fig. 5. The Cartesian Coordinates System.

80 | A. Ghorbel et al.

Fig. 6. Cropped rectangle: 70×70 pixels.

3.2 Determination of robot position We determinate the robot position in a sequence of images: we extract from each sequence’s frame the robot’s position. In order to decrease the processing time, we apply a minimum bounding box (MBB), embracing the robot, to the binary input image in order to reduce the number of the treated pixels. The proposed algorithm is presented as follows: – In the first step, we have the first color image with the default webcam image size 640×480 pixels. On this image, we calculate the initial robot position in pixels coordinates. – In the second step, we define a crop rectangle (Fig. 6) that depends on previous robot coordinates.

3.3 Robot and landmark localization technique The different algorithms used are the grayscaling, the Sobel filter, the thresholds, erosion and Hough Circle Transform. As shown in Fig. 7, the first step consists on converting the color image to a gray level (Fig. 7b) in which the red, green and blue components have equal intensity in RGB space. Then, the second step consists on transforming, using Sobel filter, the grayscale image into a black image unless at the points where a contour is detected that is marked in white (Fig. 7c). After that, we must reduce in Fig. 7c a large quantity of informations (the white lines) while conserving in Fig. 7d nearly all pertinent informations to separate objects from background. To isolate the robot from the four markers (Fig. 7e), we apply the erosion on the binary image. Finally, the last step is to locate the four landmarks and the robot using the Hough Circle Transform algorithm. After applying this treatment, we have succeed to locate simultaneously, four landmarks (defining the reference system of the robot) (see Fig. 7g) and identify the robot (see Fig. 7f).

FPGA Implementation of Absolute Robot Localization

(a) Input color image

(b) Grayscale image

(c) Edged image

(d) Binary image

(e) Eroded image

(f) Center of robot

(g) Center of landmarks Fig. 7. Results of image processing algorithms.

|

81

82 | A. Ghorbel et al.

The absolute localization system using camera determines the coordinates in a reference linked to the camera image (A, j, i). These coordinates have to be transformed in the robot coordinates system (O, x, y).

3.4 Transformation from image coordinates to real coordinates Based on both the real and image landmarks’coordinates with the gravity center of the robot, we can calculate the robot real position. Hence, we define the left bottom landmarks (j1 , i1 ) as the origin of our system as shown in Fig. 8.

Y i

( j4, i4)

( j3, i3)

y robot

ir

x

Origin O ( j1, i1) A

x robot

jr

( j2, i2) j

Fig. 8. The positioning architecture adopted.

We obtain the real image coordinates in the coordinate system (O, x, y) using equation (1): ⎧ ⎨ x robot = j r − j1 (1) ⎩ y robot = i r − i1 where j r and i r are the position (j, i) of the robot on the image and j1 and i1 are the position (j, i) of the origin. The resulting coordinates are measured using pixel number as unit. To transform them in centimeters, a multiplication by constant coefficients k x and k y is used. These coefficients are calculated on the basis of both the real and the pixels distance between the landmarks along the x-axis and the y-axis.

FPGA Implementation of Absolute Robot Localization |

The coefficients k x and k y are given by: ⎧ Dx ⎪ ⎪ ⎨ Kx = j − j 2 1 ⎪ D y ⎪ ⎩ Ky = i4 − i1

83

(2)

where D x and D y are respectively the real distance in centimeters between the landmarks along the x-axis and the y-axis. By applying the previously described localization technique on a PC (Core 2 duo, 2GHz, 3Gb RAM), the average processing time is about 0.009 seconds which is suffisant to provide continuous robot navigation with maximum velocity. In order to ensure autonomous navigation without an encombrant PC, we decide to implement this technique of localization on an ALTERA FPGA embedded system like shown in Fig. 9.

Camera FPGA

Robot Fig. 9. The hardware implementation.

4 Implementation on Altera FPGA platform In this paper, we give proof of concept of using FPGA technology in our localization technique. We adopt an Altera Cyclone III FPGA Starter Kit. Thus, in this section, we present the steps needed to implement this technique on the NIOS-II processor. Then we detail the various stages to measure the processing time. Finally, we expose different techniques used to optimize and reduce the overall execution time.

84 | A. Ghorbel et al.

4.1 Purely Software Implementation The first step we have to do is the adaptation of the code which has been executed under Visual Studio using OpenCV library. Two possible solutions exist. The first consists on building the embedded Linux operation system on Nios-II and cross compile the OpenCV library. This solution is suitable but impossible to realize with the previous Nios version. Also, this method increases the computational time (already huge as discussed after). So, the adopted solution is to declare images in header file (.h) directly in the source code as described in Fig. 10.

1 2 3 4 5 6

typedef struct { unsigned char B; unsigned char G; unsigned char R; }Pixel; Pixel image[307200]={{0,0,3},{1,2,6},{5,9,10},{9,13,14},{11,1

Fig. 10. Example of header file.

After importing, configuring and executing the program on the FPGA, we need to verify the result. Since our FPGA is not equipped with a VGA port, we have displayed in the NIOS console the RGB components values, we put each in a matrix and we concatenate them in order to obtain a 3 channels matrix, using MATLAB command: MyRGB = cat(3, Red, Green, Blue)

(3)

The images obtained are identical to those shown in Fig. 7 on the PC version. This validates the purely software C version developed for the NIOS-II.

4.2 Time processing measure The implementation of the absolute localization algorithm on the FPGA, under 100 MHz of frequency, leads to an assembly of time execution which is resumed in Tab. 1 and Tab. 2. We can notice that we obtain 118.106 seconds as time processing for the first image (640×480 pixels) used to identify the starting robot position. However, the rest of images (70 × 70 pixels), used for robot tracking, need only 12.316 seconds. To ensure fluid robot navigation, the processing time must be less than 0.48 seconds. Then, we decide to accelerate our system.

FPGA Implementation of Absolute Robot Localization |

85

Tab. 1. Measuring result for the first image. Section Gris and Sobel Binarisation Erosion Hough Transform

% of total time

Time(sec)

Time(clocks)

83.1 0.257 0.788 15.9

98.13403 0.30305 0.93115 18.73743

9813403474 30305130 93115463 1873743278

Total Time :

118.106 seconds

Tab. 2. Measuring result for the remaining images. Section Gris and Sobel Binarisation Erosion Hough Transform Total Time :

% of total time

Time(sec)

Time(clocks)

19 0.297 1.43 79.3

2.33668 0.03658 0.17616 9.76661

233668191 3657597 17616146 976661326

12.3169 seconds

4.3 Techniques for accelerating time processing In order to accelerate the system and reduce the execution time, we use different methods:

4.3.1 Tools available on the Nios II processor –

–

The use of the fast version of the NIOS (NIOS-II/f core) that provides 133 MHz as frequency, a performance over 300 MIPS and six-stages pipeline to achieve maximum MIPS per MHz. The use of the floating-point (FP) custom instruction: like custom peripherals, custom instructions allow us to increase system performance by augmenting the processor with custom hardware directly added to the Nios UAL (Fig. 12). The floating-point custom instructions, optionally available on the Nios II processor, implement single-precision, floating-point arithmetic operations. When the floating-point custom instruction is present in the design, the code will be build using the custom instructions (addition, subtraction, multiplication and division) like shown in Fig. 11.

86 | A. Ghorbel et al.

Multiplication

Addition

Nios-II processor Subtraction

Division Avalon Bus

Performance counter (slave)

Memory (slave)

Fig. 11. Arithmetic operations connects to the Nios II.

Custom Instruction

A

Nios II ALU

Result

B

Fig. 12. Custom Instruction connects to the Nios II ALU.

This FP unit has been used to compute the grayscale equation: Gray = 0.299 × Red + 0.587 × Green + 0.114 × Blue

(4)

The added hardware features lead to an assembly of time execution which is resumed in Tab. 3 and Tab. 4.

FPGA Implementation of Absolute Robot Localization

|

87

Tab. 3. Measuring result for the first image. Section Sobel Binarisation Erosion Hough Transform

% of total time

Time(sec)

Time(clocks)

56.5 0.301 2.18 41

9.89234 0.05265 0.38112 7.17303

989234461 5265333 38111921 745904065

Total Time :

17.4994 seconds

Tab. 4. Measuring result for the remaining images. Section Sobel Binarisation Erosion Hough Transform Total Time :

% of total time

Time(sec)

Time(clocks)

7.48 0.151 0.936 91.4

0.29672 0.00599 0.03712 3.62636

29671767 598544 3712309 362636394

3.9664 seconds

We can notice that, with these custom hardware logic blocks, we obtain 17.499 seconds as time processing for the first image which corresponds to 85% of improvement. The rest of images (70 × 70 pixels) need only 3.966 seconds which nearly corresponds to 70% of improvement.

4.3.2 Creation of hardware accelerator According to Tab. 3, the Sobel function monopolizes 56.5% of total time since it contains complex operations like magnitude equation (5):  |Mag | = (E h )2 + (E v )2 (5) where E h and E v represent respectively the gradient intensity in image along horizontal and vertical directions. So, we design a hardware accelerator that computes equation (5) to HW in order to accelerate execution time for the first image. This accelerator communicates with the Nios-II processor through Avalon bus (Fig. 13). The schematic block of the Sobel HW accelerator is shown in Fig. 14.

88 | A. Ghorbel et al.

Nios II (master)

Memory (slave)

Avalon Bus

Sobel Accelerator Fig. 13. The Sobel hardware accelerator.

Eh[15..0] Input

Multi X2 A Add + B

Eʋ[15..0]

Sqrt R

Q r

Mag [15..0] Output Output

Multi 2

Input

X

Fig. 14. The schematic block of Sobel accelerator.

The visualizing images after adding the Sobel accelerator are identical to those shown in Fig. 7. These results validate the design of Fig. 13. With all these accelerating tools (NIOS-II/f, Floating Point Unit and the Sobel accelerator), we reach 11.052 seconds of execution time for the first image (Tab. 5) which corresponds to 35% of time decrease against previous time. For the rest of the images, we reach 3.788 seconds (Tab. 6).

Tab. 5. Measuring result for the first image. Section Sobel Binarisation Erosion Hough Transform Total Time :

% of total time

Time(sec)

Time(clocks)

29.3 0.476 3.44 66.8

3.23552 0.05263 0.38072 7.38325

323552263 5262942 38071911 738324576

11.0523 seconds

FPGA Implementation of Absolute Robot Localization

|

89

Tab. 6. Measuring result for the remaining images. Section

% of total time

Sobel Binarisation Erosion Hough Transform Total Time :

4.93 0.024 0.228 94.8

Time(sec)

Time(clocks)

0.18687 0.00091 0.00864 3.59213

18686747 91047 863854 359212649

3.78876 seconds

5 Conclusion In this work, we have realized an application that ensures the absolute localization of the robot Khepera II in its workspace using webcam data. We develop a C version of the localization algorithm using OpenCV library. Then we implement it on an ALTERA FPGA embedded system using the NIOS processor. We use specific computation hardware (floating point coprocessor) and a custom hardware accelerator (Sobel) to reduce the overall execution time. The obtained results proved the effectiveness of the proposed HW/SW architecture in accelerating the processing time. However, it still enough to ensure continuous and real time robot navigation. The further improved the obtained values, an implementation of a parallel architecture with a multi-processor FPGA system will be held in future work. Due to the limited capacity of the used FPGA (Cyclone III FPGA Starter kit), other optimizations techniques cannot be used especially multiprocessor techniques. These techniques are particularly efficient especially for application with high inherent parallelism like our navigation application. Direct navigation of the robot using the Cyclone III FPGA Starter kit is not possible due to the lack of DVI input nor VGA output. We plan in ongoing work to realize all the application using the ML507 platform with PowerPC processor which is more performant than NIOS and integrating the camera for images acquisition. Furthermore, the use of the camera is restricted to an internal environment (indoor) to be able to fix it, while in an external environment, it will be harder to do it. So, to overcome this problem, a multi-sensors (GPS, cameras or DGPS) fusion approach is a way to improve environment perception in a real-life scenario would involve a much larger area.

Bibliography [1]

[2]

M. Jallouli, L. Amouri and N. Derbel. An effective localization method for robot navigation through combined encoders positioning and retiming visual control. In J. of Automation, Mobile Robotics and Intelligent Systems, 3(2), 2009. S.I. Roumeliotis, G.S. Sukhatme and G.A. Bekey. Smoother based 3D Attitude Estimation for Mobile Robot Localization. In IEEE Int. Conf. on Robotics and Automation, May 1999.

90 | A. Ghorbel et al.

[3]

[4] [5] [6] [7] [8]

[9] [10] [11]

[12] [13]

[14] [15]

[16]

R. Negenborn. Robot Localization and Kalman Filters On finding your position in a noisy world. Thesis for the degree of Master of Science, Institute of Information and Computing Sciences, Utrecht University. September 1, 2003. M.S. Grewal and A.P. Andrews. Kalman Filtering, Theory and Practice. Prentice Hall, 1993. J.Z. Sasiadek and P. Hartana. Sensor Data Fusion Using Kalman Filter. Int. Conf. of the Int. Society of Information Fusion, 19–25, 2000. F. Kobayashi, F. Arai, T. Fukuda, K. Shimojima, M. Onoda and N. Marui. Sensor Fusion System using recurrent fuzzy inference. J. of Intelligent and Robotic Systems, 23:201–216, 1998. K. ACHOUR and A.O. DJEKOUNE. Localization and guidance with an embarked camera on a mobile robot. Advanced Robotics, 16(1):87–102, 2002. A. Kak, K. Andress, C. Lopez-Abadia, M. Carroll and J. Lewis. Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation. Computing Research Repository J., abs/1304.1513, 2013. L. Freda and G. Oriolo. Vision-based interception of a moving target with a non-holonomic mobile robot. em Robotics and Autonomous Systems J., 55:419–432, February 2007. I. Masár and M. Gerke. DSP-Based Control of Mobile Robots. DSP Education and Research Symp., Birmingham, U.K, November 2004. P. He, M. H. Jin, L. Yang, R. Wei, Y. W. Liu, H. G. Cai, H. Liu, N. Seitz, J. Butterfass and G. Hirzinger. High Performance DSP/FPGA Controller for implementation of HIT/DLR Dexterous Robot Hand. IEEE Int. Conf. on Robotics and Automation, New Orleans, April 2004. M. A. H. B. Azhar and K. R. Dimond. FPGA-based Design of an Evolutionary Controller for Collision-free Robot. ACM/SIGDA 11th Int. Symp. on Field Programmable Gate Arrays, 2003. S. T. Brassai and L. Bako. Visual trajectory control of a mobile robot using FPGA implemented neural network. Pollack Periodica, 4(3):129–142, DOI: 10.1556/Pollack.4.2009.3.12, December 2009. S. Kale and S. S. Shriramwa. FPGA-based Controller for a mobile robot. In Int. J. of Computer Science and Information Security, 3(1):1–5, July 2009. J.M. Ramos-Arreguin, E. Guillen-Garcia, S. Canchola-Magdaleno, J. Pedraza-Ortega, E. Gorrostieta-Hurtado, M.A. Aceves-Fernndez and C.A. Ramos-Arreguin. Fuzzy Logic Hardware Implementation for Pneumatic Control of One DOF Pneumatic Robot. Advances in Soft Computing book. Springer Berlin Heidelberg, :500–511, 2010. N. Zhang, R. Kamdem, E. Ososanya, W. Mahmoud and L. Wenxin. VHDL implementation of the hybrid fuzzy logic controllers with FPGA. Int. Conf. on Intelligent Control and Information Processing (ICICIP), :5–10, August 13–15, 2010.

Biographies Agnès Ghorbel born in April 1987. Since December 2012, she is a Ph.D at Computer and Embedded System Laboratory (CES_lab) in the National Engineering School of Sfax, Tunisia. She received her Master degree (with honors) in New Technologies of Dedicated Computer Systems discipline in 2012. Her research interests are embedded devices, hardware/software co-design, robotic applications based on vision. She has worked extensively on mapping of image processing algorithms onto FPGAs.

FPGA Implementation of Absolute Robot Localization

|

91

Mohamed Jallouli was received his DEA in Automatics from University of Valenciennes, France, in 1986 and PhD in Robotics Engineering from University Paris XII, France, in 1991. In 1987, he then joined French University in education activities for his postdoctoral period. In April 1991, he joined the Tunisian University where he held different positions involved in both education and research activities. He is currently an Associate Professor at Higher Institute of Industrial Systems of Gabes and a Computer & Embedded System laboratory’s member. His current interests include the implementation of intelligent methods (neural network, fuzzy logic and genetic algorithm) in robotic and vision system as well as in multi-sensory data fusion mobile bases. Lobna Amouri received the Ph.D Diploma in Electrical Engineering from both University of Sfax, Tunisia and University of Orleans, France, in 2012. In September 2009 she joined Tunisian University in education and research activities. She is currently an assistant professor of Soft Computing at ENET’COM in Tunisia. She is a member of the laboratory CEM Lab (Control, Energy and Management Laboratory), University of Sfax, Engineering School (ENIS). Assistant Professor Amouri is currently working on Embedded Systems Control involving both adaptive control, image and signal processing. Nader Ben Amor Nader Ben Amor is an Assistant Professor at the National Engineering School of Sfax, Tunisia. He received his PhD in Electrical Engineering from both Sfax University (Tunisia) and Bretagne Sud University (France) in 2005. His research interests are Hardware-Software System on Chip, co-design methodology, self adaptive systems, embedded real time system, real time image processing on FPGA systems, robotics.