Sensors, Circuits and Instrumentation Systems: 2018 9783110592566, 9783110590258

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

Advances in Systems, Signals and Devices

| Edited by Olfa Kanoun, University of Chemnitz, Germany

Volume 10

Sensors, 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 Germany [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 978-3-11-059025-8 e-ISBN (PDF) 978-3-11-059256-6 e-ISBN (EPUB) 978-3-11-059128-6 ISSN 2365-7493 Library of Congress Control Number: 2019931924 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. © 2019 Walter de Gruyter GmbH, Berlin/Boston Typesetting: VTeX UAB, Lithuania Printing and binding: CPI books GmbH, Leck www.degruyter.com

Preface of the Editors The tenth volume of the series “Advances in Systems, Signals and Devices” (ASSD), is a peer reviewed international scientific volume 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. Every issue is edited by a special editorial board including 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 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

De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 1–13. https://doi.org/10.1515/9783110592566-201

Advances in Systems, Signals and Devices Editors in Chief: Systems, Automation and Control Nabil Derbel, Faouzi Derbel, and Olfa Kanoun

Power Systems and Smart Energies Faouzi Derbel, Nabil Derbel, and Olfa Kanoun

Communication, Signal Processing and Information Technology Faouzi Derbel, Nabil Derbel, and Olfa Kanoun

Sensors, Circuits & Instrumentation Systems Olfa Kanoun, Faouzi Derbel, and Nabil Derbel

Prof. Dr.-Eng. Nabil Derbel ENIS, University of Sfax, Tunisia [email protected] Prof. Dr.-Ing. Faouzi Derbel Leipzig Univ. of Applied Sciences, Germany [email protected] Prof. Dr.-Ing. Olfa Kanoun Technische Universität Chemnitz, Germany [email protected]

Editorial Board Members: Systems, Automation and 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 and 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 and 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

Advances in Systems, Signals and Devices Volume 1 N. Derbel (Ed.) Systems, Automation, and Control, 2016 ISBN 978-3-11-044376-9, e-ISBN 978-3-11-044843-6, e-ISBN (EPUB) 978-3-11-044627-2, Set-ISBN 978-3-11-044844-3 Volume 2 O. Kanoun, F. Derbel, N. Derbel (Eds.) Sensors, Circuits and Instrumentation Systems, 2017 ISBN 978-3-11-046819-9, e-ISBN 978-3-11-047044-4, e-ISBN (EPUB) 978-3-11-046849-6, Set-ISBN 978-3-11-047045-1 Volume 3 F. Derbel, N. Derbel, O. Kanoun (Eds.) Power Systems & Smart Energies, 2017 ISBN 978-3-11-044615-9, e-ISBN 978-3-11-044841-2, e-ISBN (EPUB) 978-3-11-044628-9, Set-ISBN 978-3-11-044842-9 Volume 4 F. Derbel, N. Derbel, O. Kanoun (Eds.) Communication, Signal Processing & Information Technology, 2017 ISBN 978-3-11-044616-6, e-ISBN 978-3-11-044839-9, e-ISBN (EPUB) 978-3-11-043618-1, Set-ISBN 978-3-11-044840-5 Volume 5 F. Derbel, N. Derbel, O. Kanoun (Eds.) Systems, Automation, and Control, 2017 ISBN 978-3-11-046821-2, e-ISBN 978-3-11-047046-8, e-ISBN (EPUB) 978-3-11-046850-2, Set-ISBN 978-3-11-047047-5 Volume 6 O. Kanoun, F. Derbel, N. Derbel (Eds.) Sensors, Circuits and Instrumentation Systems, 2018 ISBN 978-3-11-044619-7, e-ISBN 978-3-11-044837-5, e-ISBN (EPUB) 978-3-11-044624-1, Set-ISBN 978-3-11-044838-2 Volume 7 F. Derbel, N. Derbel, O. Kanoun (Eds.) Power Systems & Smart Energies, 2018 ISBN 978-3-11-046820-5, e-ISBN 978-3-11-047052-9, e-ISBN (EPUB) 978-3-11-044628-9, Set-ISBN 978-3-11-047053-6 Volume 8 F. Derbel, N. Derbel, O. Kanoun (Eds.) Communication, Signal Processing & Information Technology, 2018 ISBN 978-3-11-046822-9, e-ISBN 978-3-11-047038-3, e-ISBN (EPUB) 978-3-11-046841-0, Set-ISBN 978-3-11-047039-0

Volume 9 N. Derbel, F. Derbel, O. Kanoun (Eds.) Systems, Automation and Control, 2019 ISBN 978-3-11-059024-1, e-ISBN 978-3-11-059172-9, e-ISBN (EPUB) 978-3-11-059031-9

Contents Preface of the Editors | V Karima Garradhi, Néjib Hassen, and Kamel Besbes Low Voltage Low Power Analog Circuit Design OTA Using Signal Attenuation Technique and Applications | 1 Rim Barioul, Sameh Fakhfakh Ghribi, Houda Derbel, and Olfa Kanoun A Myopathy’s Diagnosis System Based on Surface EMG Signal Acquisition, Analysis and Classification | 23 Björn Bieske, Gerrit Kropp, and Alexander Rolapp Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 41 Elena Sobotta, Robert Wolf, and Frank Ellinger Wideband Power Amplifier with Auto-Transformer Based Output Impedance Transformation Network | 57 Ghada Ben Salah, Karim Abbes, Chokri Abdelmoula, and Mohamed Masmoudi Obstructive Sleep Apnea treatment Methods: A Comparative Study | 77 Wagah F. Mohamad and Munther N. Al-Tikriti Investigation of Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 97

Karima Garradhi, Néjib Hassen, and Kamel Besbes

Low Voltage Low Power Analog Circuit Design OTA Using Signal Attenuation Technique and Applications Abstract: This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45 mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18 μm. The simulation results of this circuit showed a high DC gain of 73.6 dB with a unity frequency of 50.19 MHz and a total harmonic distortion of −60.81 dB at 100 kHz for an input voltage of 1Vpp. In order to realize this circuit, we have implemented in this first step a universal filter, where the frequency can reach the 51.34 MHz. In the second step, we have implemented a floating inductor simulator. Finally, we have used the last inductor to implement a RLC Band-Pass filter whose simulation results are in good agreement with the theoretical calculations. Keywords: Operational Transconductance Amplifier (OTA), Current Mirror, Dynamic Range, Floating Inductor, Universal Filter, RLC Band-Pass Filter MSC 2010: 65C05, 62M20, 93E11

1 Introduction In recent years, much effort has been made in reducing the consumption and supply voltage in CMOS systems. This is mainly due to the increasing use of portable batterypowered systems, and to the reduction of the size of integrated circuits. Operational transconductance amplifier (OTA) is an important building block for various analog circuits and systems. It is widely used in both analog and digital applications such as converters (A/N, N/A), Gm-C filters, continuous time oscillators and gyrator [1, 2]. The OTA converts an input voltage to an output current by means of transconductance [3, 4]. This representation is shown in Fig. 1. Iout = Gm (Vin+ − Vin− ) = Gm Vid

(1)

Karima Garradhi, Néjib Hassen, Micro-Electronics and Instrumentation Laboratory, University of Monastir, Monastir, Tunisia, e-mails: [email protected], [email protected] Kamel Besbes, Micro-Electronics and Instrumentation Laboratory, University of Monastir and Centre for Research on Microelectronics and Nanotechnology, University of Sousse, Tunisia, e-mail: [email protected] De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 1–21. https://doi.org/10.1515/9783110592566-001

2 | K. Garradhi et al.

Figure 1: Ideal OTA: (a) Symbol, (b) Equivalent circuit.

Where Vin+ and Vin− are the differential input voltages and Gm is the transconductance gain expressed as A/V (or Siemens). The linearity of the OTA is an important issue because the linearity of the overall system would be determined by this block. Other characteristics of the OTA would also be provided by low supply voltage, low power, large open loop gain, good response of frequency, high slew rate, large bandwidth and high linearity and wide dynamic range. Conciliating all these requirements is difficult to achieve a conventional OTA circuits since the bias current limits the maximum output current. Hence, a trade-off between slew rate and power consumption exists [5, 6]. To solve this problem a differential attenuator looks like a good factor to achieve these requirements. Moreover, multiple circuit techniques have been proposed in literature to improve the linearity of MOS transconductors. The linearization methods include: Crossing-Coupling of multiple differential pair [7], pseudo differential stages (using transistor in the triode region or in saturation) [8, 9], source degeneration using resistors or MOS transistors [10, 11], adaptive biasing [12], constant drain-source voltages [13] and super class-AB linear operation [14]. In this paper, we present a new approach OTA using a signal attenuation technique to improve input dynamic range which operates at low supply voltage ±0.8V with a reduced power consumption of 0.45 mW. Based on this circuit, we will implement universal filter. Subsequently we will implement a floating inductance simulator. This last was applied in RLC Band-Pass Filter.

2 Low Voltage Low Power OTA 2.1 Description of Circuit Operation 2.1.1 Conventional OTA The conventional OTA is achieved with a differential pair (M1 , M2 ) and three high performance current mirrors with low operating voltage. The current mirrors 1, 2 are the type flipped voltage follower (FVF) current mirror which reaches a low input and a high output impedance are given respectively by the

Low Voltage Low Power Analog Circuit Design | 3

following two expressions (2, 3) [15]. 1 gm11 gm13 ro11 = ro10 ro12 gm10

Rin = Rout

(2) (3)

The current mirror 3 is proposed in [16]. Where the input stage has implemented by flipped voltage follower (FVF) and the output stage has implemented by a “supercascade transistor”. The OTA used super-cascade transistor to achieve a high slew rate, large bandwidth and high open loop gain [17]. This mirror has a very low input and a high output impedance. Their expressions are given by the following equations: Rin = Rout

1

gm17 gm16 ro17 = gm18 ro18 gm19 ro19 gm15 ro15 ro14

(4) (5)

In this configuration, the differential input voltage between Vin+ and Vin− is converted into differential current between I1 and I2 . The pair transistors M1 and M2 are functional in the saturated region. Hence, the differential input voltage is given by the following expression: Vid = Vin+ − Vin− = VGS1 − VGS2 = √

I1 I −√ 2 βn βn

(6)

Therefore, expressions of currents I1 and I2 are given by: 2 βn Vid 1 1 I1 = ISS + √2βn ISS Vid √1 − 2 2 2ISS

(7)

2 βn Vid 1 1 I2 = ISS − √2βn ISS Vid √1 − 2 2 2ISS

(8)

Based on these two expressions (7, 8), the differential output current can be expressed as: Iout = I1 − I2 = √2βn ISS Vid √1 −

2 βn Vid

2ISS βn 3 1 ≃ √2βn ISS Vid − √2βn ISS V 4 ISS id

where βn =

1 2μnCox ( WL )1,2

(9)

is the transconductance parameter of transistors (M1 and M2 ),

Vid = Vin+ − Vin− is the differential input voltage and ISS is the bias current. In equation (9), the higher order terms have been neglected. Therefore, the dI transconductance Gm = dVout and the third order harmonic distortion can be writid ten as: Gm = √2βn ISS

(10)

4 | K. Garradhi et al.

HD3 =

1 √2βn ISS Iβn 4 SS

3 Vid

√2βn ISS Vid

=

βn 2 V 4ISS id

(11)

It is clear from the above equations that the output current of the transconductance amplifier contains a third harmonic term. Therefore, the basic objective of any linearization technique is to minimize this harmonic component.

2.1.2 Proposed OTA Linearity of the circuit in Fig. 2 can be improved by adding the stage attenuation. Therefore, the differential attenuator is one of the most popular linearization techniques reducing the third harmonic distortion which illustrated in Fig. 3.

Figure 2: Conventional OTA.

The attenuator essentially consists by a differential pair (M1a , M2a ) with two load transistors of the PMOS type (M3 , M4 ) operating as a diode [18, 19]. The transistors M3 and M4 operate in the saturation region. Then, the voltages developed across the diode

Low Voltage Low Power Analog Circuit Design

| 5

Figure 3: Proposed OTA.

connected loads can be written as: VG3,4 = VDD − VSD3,4

VSD3,4 = VSD3,4 = VSD3,4−sat + |VTp |

where βn =

1 2μnCox ( WL )3,4

VG3 = VDD − √

I1a − |VTp | βp

(12)

VG4 = VDD − √

I2a − |VTp | βp

(13)

is the transconductance parameter of transistors (M3 and M4 ).

The pair transistors (M1a , M2a ) are functional in the saturated region. Hence, the differential input voltage is given by the following expression: Vida = Vina+ − Vina− = VGS1a − VGS2a = √

I1a I − √ 2a βn βn

The currents I1a and I2a are the two branch currents which can be expressed as: 2 βn Vida 1 1 I1a = ISSa + √2βn ISSa Vida √1 − 2 2 2ISSa

(14)

6 | K. Garradhi et al. 1 1 βn 3 1 ≃ ISSa + √2βn ISSa Vida − V 2 2 8 ISSa ida 1 I2a = ISSa − 2 1 ≃ ISSa − 2

β V2 1 √2βn ISSa Vida √1 − n ida 2 2ISSa 1 βn 3 1 √2βn ISSa Vida + V 2 8 ISSa ida

(15)

(16)

The differential gate voltage of the load transistors can be written as: ΔVg = VG3 − VG4 = √ =√

I1a I − √ 2a βp βp

1 1 βn 3 (I + √2βn ISSa Vida − V ) 2βp SSa 4 ISSa ida

−√

1 1 βn 3 (I − √2βn ISSa Vida + V ) 2βp SSa 4 ISSa ida

(17)

In equation (17), the higher order terms have been neglected so: ΔVg = √ =√ where m =

βp βn

=

√2βn ISSa √2βn ISSa ISSa I (1 + Vida ) − √ SSa (1 − Vida ) 2βp ISSa 2βp ISSa ISSa √2βn ISSa V β Vida = √ n Vida = ida √m 2βp ISSa βp

μp μp ( WL )p3,4 [ ]: μn μn ( W )n1,2 L

(18)

is the attenuation factor.

Now, this differential gate voltage is applied to the input of a conventional OTA. The considered equation (9) for the output current can be expressed as: Iout = I1 − I2 = √2βn ISS ΔVg −

β 1 √2βn ISS n ΔVg3 4 ISS

(19)

Substituting the expression of ΔVg given by (18), the output current can be expressed as: Iout = I1 − I2 =

√2βn ISS β 1 3 √2βn ISS n Vida Vida − √m ISS 4m√m

(20)

By neglecting terms of superior order, the transconductance Gma and the third order harmonic distortion HD3a can be derived as: √2βn ISS √m 1 βn 2 = V 4m ISS ida

Gma = HD3a

(21) (22)

Low Voltage Low Power Analog Circuit Design

| 7

These relations show that the transconductance Gma of the proposed OTA is reduced by a factor √m and the harmonic distortion HD3a is reduced by a factor m. Thus increasing m will simultaneously reduce the harmonic distortion and increase the linear range.

2.2 Dynamic Study of the Circuit The small signal equivalent circuit of stage attenuation is shown in Fig. 4.

Figure 4: The small Signal equivalent circuit of attenuator stage.

The small Signal equivalent circuit for the proposed OTAis shown in Fig. 5. Hence, the differential input voltages Vina+ and Vina− are given by the following expressions: 1 [V − F1 I1a − F3 I2a ] gm1a r01a in+ 1 =− [V − F2 I1a − F4 I2a ] gm2a r02a in−

Vina+ = −

(23)

Vina−

(24)

Based on these expressions given by (23, 24), the differential input voltage can be expressed as: Vina+ − Vina− = −

1 1 [V − F1 I1a − F3 I2a ] + [V − F2 I1a − F4 I2a ] gm1a r01a in+ gm2a r02a in−

where: F1 = ro8 (1 + gm1a ro1a ) + ro1a

F3 = ro8 (1 + gm2a ro1a )

F2 = ro8 (1 + gm2a ro2a ) + ro2a

F4 = ro8 (1 + gm2a ro2a )

(25)

8 | K. Garradhi et al. 1 + gm1a ro3a Vin+ ro3a 1 + gm4a ro4a =− Vin− ro4a

I1a = − I2a

Considering the products gmi ri much greater than 1, the characteristics of transistors M1a and M2a are identical and F1 = F2 , F3 = F4 . Therefore, we find the differential input voltage given by the following expression: Vina+ − Vina− = −

gm3 (V − Vin− ) gm1a in+

(26)

The small Signal equivalent circuit for the proposed OTAis shown in Fig. 5. Hence, the differential input voltages Vin+ and Vin− are given by the following expressions: 1 [(r + r )I + r I + (1 + gm1 ro1 )Vds13 ] gm1 ro1 o7 o1 1 o7 2 1 = [(r + r )I + r I + (1 + gm2 ro2 )Vds13a ] gm2 ro2 o7 o2 2 o7 1

Vin+ =

(27)

Vin−

(28)

Based on these two expressions (27, 28), the differential input voltage can be expressed as: Vin+ − Vin− =

1 [(r + r )I + r I + (1 + gm1 ro1 )Vds13 ] gm1 ro1 o7 o1 1 o7 2 1 − [(r + r )I + r I + (1 + gm2 ro2 )Vds13a ] gm2 ro2 o7 o2 2 o7 1

(29)

where, the voltages drain-source of transistors M13 and M13a are given by the following expressions, respectively: Vds13 = E1 I1

Vds13a = E2 I2 where:

(30) (31)

ro13 (ro9 + ro11 ) (ro9 + ro11 ) + ro13 (1 + gm11 ro11 )(1 + gm13 ro9 ) ro13a (ro9a + ro11a ) E2 = (ro9a + ro11a ) + ro13a (1 + gm11a ro11a )(1 + gm13a ro9a ) E1 =

Substituting these expressions into Vds13 and Vds13a in (27) and using the approximation 1 + gmi ri ≃ gmi ri , characteristics of the transistors M1 and M2 are identical, and E1 = E2 . Consequently, the output current can be expressed as: Iout =

gm1 ro1 [ro9 + ro11 + ro9 (gm11 ro11 )(gm13 ro13 )] (V − Vin− ) gm1 ro1 ro13 + ro1 [ro9 + ro11 + ro9 (gm11 ro11 )(gm13 ro13 )] in+

(32)

The considered equation (26) for the output current can be expressed as: Iout = −

gm1 ro1 [ro9 + ro11 + ro9 (gm11 ro11 )(gm13 ro13 )] gm1a (V − Vin− ) gm3 gm1 ro1 ro13 + ro1 [(ro9 + ro11 + ro9 (gm11 ro11 )(gm13 ro13 ] in+

(33)

Low Voltage Low Power Analog Circuit Design | 9

Figure 5: The small Signal equivalent circuit for the proposed OTA.

3 Simulation Results The performance of the proposed CMOS OTA and Conventional OTA were verified by ELDO based on BSIM3v3 transistor model for the TSMC 0.18 μm CMOS. This circuit is operated at ±0.8V supply voltage with a DC voltage VB1 = 0.1V and capacitive load of 10 pF. Transistors aspect ratios of the two OTA are given in Tab. 1, respectively. The DC transfer characteristic of the proposed OTA is shown in Fig. 6, which is achieved by varying input voltage from −0.8V to +0.8V. It is understood that the proposed OTA has a good linearity in the interval [−0.55V, 0.55V]. From this characteristic, we plot the transconductance Gm, we observed that the transconductance of the OTA proposed is more stable in the interval [−0.55V, 0.55V] and the maximum value of Gm is found equal to 350 μS (Fig. 7).

10 | K. Garradhi et al. Table 1: Aspect ratios of the transistors. Transistors M1a , M2a M3 , M4 M1 , M2 M9 , M9a M8 M11 , M11a , M10 , M10a M12 , M12a , M13 , M13a M7 M14 , M16 M1 8 M1 9 M15 , M17 MI 1, MI 2, MI 3

Conventional OTA W (μm)/L(μm)

Proposed OTA W (μm)/L(μm)

– – 1/0.18 0.27/0.18 – 30/0.18 30/0.18 100/0.18 10/0.18 10/0.18 20/0.18 5/0.18 0.27/0.18

7/0.18 50/0.18 0.8/0.18 0.8/0.18 1/0.18 3/0.18 3/0.18 100/0.18 20/0.18 10/0.18 20/0.18 5/0.18 0.27/0.18

Figure 6: DC Transfer characteristic.

The frequency response of the two OTA is shown in Fig. 8. The proposed OTA reached an open loop gain of 73.6 dB with transition frequency of 50.19 MHz. For OTA conventional, we note a relatively small gain of 44 dB with transition frequency of 4.28 MHz. For a sinusoidal signal at frequency 100 kHz, the harmonic distortion of the two OTA is shown in Fig. 9, which is achieved by varying input voltage from 0.1V to 0.5V. In order to estimate our work towards other works, the defined Figure-of-Merit (FoM) which involve the transconductance value, speed of the circuit, linearity, power consumption and input swing range is expressed as follows [20]: FoM = 10 log

gm Vid |THDdB |fo Power

(34)

Low Voltage Low Power Analog Circuit Design | 11

Figure 7: The simulated Transconductance.

Figure 8: Open loop frequency response of the OTA.

Figure 9: Simulated THD at 100 KHz.

12 | K. Garradhi et al. Table 2: Performance Comparison with Previously Reported Work. Performance design Technology CMOS (μm) Supply voltage (V) Power consumption (mW) Transconductance (μS) Capacitive load (pF) DC gain (dB) Phase margin (degrees) GBW (MHz) Linear range (V) PSRR+ (dc) (dB) PSRR− (dc) (dB) CMRR (dc) (dB) THD (dB) Positive slew rate (V/μs) Negative slew rate (V/μs)-175 Input Noise Density (nV/√Hz) Figure of Merit

Conventional OTA

Proposed OTA

0.18 μm ±0.8 0.39 320 10 44 89.29 4.28 [−0.1, 0.3] 39.22 38.31 16.04 −21.93 429.31 −438.90 36.15 78.86

0.18 ±0.8 0.45 350 10 73.6 84.5 50.19 ±0.55 65.88 63.33 86.53 −60.81 695.48 18.71 28.4 91.15

OTA [6] 0.18 ±0.8 0.325 700 10 81.53 86.09 12.45 ±0.5 65.59 68.16 67.51 −39.86 24.09 – 23.07 83.26

OTA [18]

OTA [19]

0.18 1.8 0.45 110 – – – 5 ±0.5 – – – −61 – – 28 85.71

0.18 1.8 0.45 90 0.2 – – 15 ±0.6 −58 −58 −60 −70 – – 80 81

OTA [20] 0.18 0.9 0.0588 38.8 1 34.8 – 11 ±0.6 82.7 47.8 139.8 −55.4 – 148.3 79.5

In Tab. 2, we summarize the simulated performances of the proposed OTA and the conventional OTA along with some of the recent works.

4 Applications 4.1 Universal Filter 4.1.1 Theoretical Results The range of the transconductance should be increased to achieve a good tuning range of the filter, so the filter performance is largely determined by the Gm value of the OTA. The topology adopted by voltage mode universal filter with multi inputs and signal output is shown in Fig. 10. This designed a voltage mode universal filter uses two proposed OTA which are controllable by three voltages. By controlling the inputs to the three input voltages terminal, a low pass, high pass and band pass filters can be implemented. The relationship between the output voltage and the three control voltages (VA , VB and VC ) can be expressed as: I01 = gm1 (V1+ − V1− ) = gm1 (VA − VB )

(35)

Low Voltage Low Power Analog Circuit Design | 13

Figure 10: Basic Circuit of a Universal Filter.

I02 = gm2 (V2+ − V2− ) = gm2 ( I03 = sC2 (V0 − VC )

gm1 [(VA − V0 ) + VB ] − V0 ) sC1

(36) (37)

We suppose that equations (36, 37) are equal, the differential output voltage can be expressed as: V0 =

s2 C1 C2 VC + sgm2 C1 VB + gm1 gm2 VA s2 C1 C2 + sgm2 C1 + gm1 gm2

(38)

Thus, the circuit can be used as different filters by setting the values of VA , VB and VC as shown below: – Where, VA = Vin and VB = VC = 0, then, we obtain a low pass filter (LP): V0 gm1 gm2 = Vin s2 C1 C2 + sgm2 C1 + gm1 gm2 –

Where VC = Vin and VA = VB = 0, then, we obtain a high pass filter (HP): V0 s2 C1 C2 = 2 Vin s C1 C2 + sgm2 C1 + gm1 gm2

–

(41)

Where VA = VC = Vin and VB = 0, then, we obtain a reject pass filter (BP). V0 s2 C1 C2 + gm1 gm2 = 2 Vin s C1 C2 + sgm2 C1 + gm1 gm2

–

(40)

Where VB = Vin and VA = VC = 0, then, we obtain a band pass filter (BP): V0 sgm2 C1 VB = 2 Vin s C1 C2 + sgm2 C1 + gm1 gm2

–

(39)

(42)

Where VA = VC = VB = Vin then, we obtain a all pass filter (BP): V0 s2 C C + sgm2 C1 + gm1 gm2 =1 = 2 1 2 Vin s C1 C2 + sgm2 C1 + gm1 gm2

(43)

14 | K. Garradhi et al. –

Where, the pole frequency ω0 and the pole quality factor Q0 of these transfer functions can be given as follows: ω0 = √

gm1 gm2 C , Q0 = √ 2 C1 C2 C1

(44)

4.1.2 Simulation Results of Filter The proposed filter operates at a low supply voltage of ±0.8V with a reduced power consumption of 0.9 mW. After having used C1 = C2 = 1 pF, we have obtained a center frequency of 51.34 MHz (Fig. 11). The theoretical calculation, allows us to obtain a centre frequency of 55.7 MHz, wherever we use C1 = C2 = 1 pF and gm1 = gm2 = 350 μS. The simulation results are in good agreement with the theoretical calculations.

Figure 11: Frequency responses of universal filter with varied controlled inputs.

4.2 Implementing an Inductance Floating inductance simulator is one of the important roles in analog electronic circuit design. Its applications can be found in active filters, oscillators, and system response compensation [21, 22]. In this section, we present the creation of the floating inductance, based on OTAs circuit.

4.2.1 Floating Inductor Scheme of a floating active inductor using four OTA is shown in Fig. 12.

Low Voltage Low Power Analog Circuit Design | 15

Figure 12: Realization of floating inductance using four OTA.

The voltage across active floating inductance is: VAB = VA − VB

(45)

The expressions of output currents across OTA1 and OTA3 are given by: I1 = −gm1 VA

I2 = −gm3 VB

(46) (47)

The output voltage V of OTA1 and OTA3 is connected with the grounded capacitor C1 is: V=

I1 + I3 sC1

(48)

This voltage V becomes an input voltage for OTA2 and OTA4 which gives output currents, which are: I2 = gm2 V

I4 = −gm4 V

(49) (50)

For superior performance, the transconductance of the OTA1, OTA2, OTA3 and OTA4 are identical (gm1 = gm2 = gm3 = gm4 = gm ). After substituting V from (48) in (49) and (50), we get: I2 =

2 gm (VB − VA ) g 2 (V − VB ) , I4 = m A sC1 sC1

(51)

Then IA = −I2 and IB = −I4 , from (51) IA = −IB . Therefore its equivalent impedance ZAB is expressed as: ZAB =

V − VB sC1 VA − VB =− A = 2 = sL IA IB gm

(52)

16 | K. Garradhi et al. Consequently, the inductance can be expressed as: L=

C1 2 gm

(53)

Combining equations (21) and (53), we infer that the inductance can be electronically tuned by varying the external bias current ISS . 4.2.2 Simulations Results The floating inductance has an operating frequency zone and L values vary with capacity C1 . At each change in capacity C1 , we have found the area of operation frequency with an accuracy of ±5 degrees of phase (Tab. 3). Table 3: The values of inductors according to the capacity C1 . Floating inductance C1 (pF)

Frequency (MHz)

L (nH)

2 3 4 5

55.6 41.34 34.1 31.2

3.5 5.5 7.1 8.8

4.3 RLC Band-Pass Filter The proposed floating inductance simulator in Fig. 12 was applied in RLC Band-Pass filter as shown in Fig. 13. The resistor RL in Fig. 13 can use the following OTA circuit form Fig. 14 and C is a capacity. 4.3.1 Theoretical Result The prototype of a RLC Band-Pass filter shows in Fig. 13.

Figure 13: RLC band-pass filter.

Low Voltage Low Power Analog Circuit Design | 17

The transfer function of the RLC filter is given by equation 54: H(s) =

Vout RCs = Vin 1 + RCs + LCs2

(54)

Consequently, the pole frequency ω0 and the pole quality factor Q0 of this transfer function can be given as follows: ω0 =

1 L 1 , Q0 = √ √LC R C

(55)

Resistor can use the following OTA circuit form, and is shown in Fig. 14.

Figure 14: The Form of OTA to resistance.

The equivalent resistance is given by the inverse of transconductance and can be calculated as follows: R=

1

gm5

(56)

Based on Fig. 13, it can obtain the form of the OTA circuit. It is shown in Fig. 15.

Figure 15: Proposed floating inductance in RLC pass band filter.

18 | K. Garradhi et al. By substituting the values of L and R, and gm1 = gm2 = gm3 = gm4 = gm5 = gm , Therefore, we find the transfer function is given by the following expression: H(s) ==

1+

1 Cs gm

1 Cs gm

+

CC1 2 s gm

(57)

Consequently, the new expressions of the pole frequency ω0 and the pole quality factor Q0 of this transfer functions can be given as follows: ω0 =

gm C , Q0 = √ 1 √LC C

(58)

4.3.2 Simulation Results of the Filter The workability of the RLC pass band filter has been demonstrated by ELDO based on BSIM3v3 transistor model for the TSMC 0.18 μm CMOS. After having used C1 = 5 pF and C = 0.5 pF. We have obtained a center frequency of 34.4 MHz (Fig. 16). The theoretical calculation, allows us to obtain a centre frequency of 35.27 MHz, wherever we use C1 = 5 pF and C = 0.5 pf and gm = 350 μS. Simulation results of the BP filter are in good agreement with the theoretical calculations.

Figure 16: Simulated frequency responses of the BP filter by varying capacity C.

Theoretically, it is possible to control the quality facture Q0 and to vary the frequency f0 by only varying the capacity C and fixed C1 to 5 pF. Simulation results are presented in Fig. 16.

Low Voltage Low Power Analog Circuit Design | 19

5 Conclusion This paper discusses the approach of low voltage and low power OTA design using a signal attenuator technique to improve the linearity of the conventional differential pair balanced transconductance amplifier, which offers a large differential input voltage capability and a wide gm adjustment range. The resulting topology achieves a good input range of 1.1Vpp with a high DC gain of 73.6 dB in ±0.8V supply voltage. The GBW is 50.19 MHz and the total harmonic distortion. THD is about −60.81 dB for 1Vpp input signal at 100 kHz. Based on this circuit, we have implemented first a universal filter and obtained a center frequency of 51.34 MHz. In the second, we have implemented the floating inductance operating in high frequency and variable depending on capacity C1 . We have used the inductance to implement a RLC Band-Pass Filter. The frequency can reach the 34.4 MHz.

Bibliography [1]

Y. Venkateswarlu. Design of the 40 MHz Double Differential-Pair CMOS OTA with −60 dB IM3. Int. Journal of Engineering Research & Technology, vol. 2, September 2013. [2] Y.Zheng. Operational Transconductance Amplifiers For Gigahertz Applications. Department of Electrical and Computer Engineering University in Canada, Thesis, 2008. [3] R. L. Geiger and E. Sánchez-Sinencio. Active Filter Design Using Operational Transconductance Amplifiers: A Tutorial. IEEE Circuits and Devices Magazine, 1:20–32, March 1985. [4] G. Kapur and S. Mittal. Analog Field Programmable CMOS Operational Transconductance Amplifier OTA. IEEE Department of Electronics & Communication National Institute of Technology, 4673–5250, 2013. [5] Juan A. Galan, and Antonio J. López-Martín. Super Class-AB OTAs with Adaptive Biasing and Dynamic Output Current Scaling. IEEE Trans. on Circuits and System, 54:1549–8328, March 2007. [6] H. Bdiri-Gabboui, N. Hassen and K. Besbes. Low Voltage High Gain Linear Class AB CMOS OTA with DC Level Input Stage. World Academy of Science, Engineering & Technology, 5:735–741, Aug 2011. [7] J. Chen, E. Sanchez and J. Silva Martinez. Frequency Dependent Harmonic Distortion Analysis of a Linearized Cross Coupled CMOS OTA and its Application to OTA-C Filters. IEEE Transaction on Circuit and Systems, 53:499–510, March 2006. [8] A. Nader Mohieldin. A Fully Balanced Pseudo-Differential OTA With Common-Mode Feedforward and Inherent Common-Mode Feedback Detector. IEEE journal of solid-state circuits, 38:0018–9200, April 2003. [9] B. Calvo, S. Celma, J. Ramirez-Angulo and M. T. Sanz. Low-Voltage Linearly Tunable CMOS Transconductor With Common-Mode Feedforward. IEEE Trans. on Circuits And Systems, 55:1549–8328, April 2008. [10] Ko chi kuo. A linear mos transconductor using source degeneration and adaptive biasing. IEEE Trans. on Circuits and Systems-ii: analog and digital signal processing, 48:1057–7130, October 2001. [11] F. A. P. Barúqui and A. Petraglia. Linearly Tunable CMOS OTA with Constant Dynamic Range Using Source Degenerated Current Mirrors. IEEE Trans. on Circuits and Systems II, 53:791–801, 2006. [12] B. Ghanavati. A 1.5V CMOS Transconductor using Adaptive Biasing. Int. Conf. on Electrical, 168–171, April 2012.

20 | K. Garradhi et al.

[13] P. K. Chu. Transconductor Advances in Solid State Circuits Technologies. Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung, pp. 429–446, April 2010. [14] S. Baswa, A. J. Lopez-Martin, J. Ramirez-Angulo and R. Gonzalez Carvajal. Low voltage micro-power super class AB CMOS OTA. Electronics letters, 40:216–217, 2004. [15] N. Hassen, H. Bdiri-Gabbouj and K. Besbes. Low-voltage high-performance current mirrors: Application to linear voltage-to-current converte. Int. Journal of Circuit Theory and Applications, 39, January 2011. [16] A. Torralba, R. G. Carvajal, J. Ramirez-Angulo and F. Munoz. Output stage for low supply voltage high performance CMOS current mirrors. Electronics letters, 38:1528–1529, 2002. [17] Manoj K. Taleja, and Manoj Kumar. Bias Current Effect on Gain of a CMOS. IEEE Int. Conf. on Advanced Computing & Communication Technologies, pp. 396–397, 2011. [18] S. Kar and S. Sen. Linearity improvement of source degenerated transconductance Amplifiers. Analog Integrated Circuits & Signal Processing, 399–407, 2013. [19] S. Kar and S. Sen. A highly linear CMOS transconductance amplifier in 180 nm process technology. Analog Integrated Circuits and Signal Processing, 2011. [20] S. Abbasalizadeh, S. Sheikhaei, and B. Forouzandeh. A 0.9 V Supply OTA in 0.18 µm CMOS Technology and Its Application in Realizing a Tunable Low-Pass Gm −C Filter for Wireless Sensor Networks. Scientific research on Circuits and Systems, 2013. [21] A. Thanachayanont and A. Payne. CMOS floating active inductor and its application to bandpass filter and oscillator designs. IEE Proc. Circuits Device Systems, 147:42–48, 2000. [22] F. Hirohito, K. Atsuo, and K. Kenzo, Realization of Negative Inductance Using Variable Active-Passive Reactance (VAPAR), IEEE Trans. Power Elecronics, 12:589–596, 1997.

Biographies Karima Garradhi was born in Nabul, Tunisia, in 1988. She received the applied license from the Faculty of Sciences of Monastir in 2011, the M. S. degree from at the same University at the Microelectronic and Instrumentation Laboratory in 2013. Actually, she is preparing the Ph. D degree. She is interested to the implementation of low voltage low power integrated circuit design.

Néjib Hassen was born in 1961 in Moknine, Tunisia. He received the B. S. degree in EEA from the University of Aix-Marseille I, France in 1990, the M. S. degree in Electronics in 1991 and the Ph. D. degree in 1995 from the University Louis Pasteur of Strasbourg, France. From 1991 to 1996, he has worked as a researcher in CCD digital camera design. He implemented IRDS new technique radiuses CCD noise at CRN of GOA in Strasbourg. In 1995, he joined the Faculty of Sciences of Monastir as an In 1995, he joined the Faculty of Sciences of Monastir as an Assistant Professor of physics and electronics Since 1997, he has worked as researcher in mixed-signals neural networks. Currently, he is professor of microelectronics and electronics to ISIMM University of Monastir. He is focusing on the implementation low voltage – low power mixed and analog circuits.

Low Voltage Low Power Analog Circuit Design | 21

Kamel Besbes, Professor on Microelectronics, received M. S. degree from the Ecole Centrale de Lyon-France in 1986, the PhD degree from INSA Lyon, France in 1989 and the State Doctorate Degree from Tunis University in 1995. In 1989, he joined Monastir University. He established teaching and research laboratories initiatives in microelectronics since 1990. Research efforts are focused on microelectronics devices, microsystems, embedded systems, Instrumentation for detection, navigation and space programs. He has more than 200, published and presented papers at workshops and conferences. He participated to committees of several workshops and conferences. He is a full Professor since 2002 and he was the Vice-Dean (2000–2005), the Dean of Sciences Faculty of Monastir (2008–2011). He was elected member of University of Monastir council (2005–2014) and member of Higher Education and Scientific Research Reform National Council (2012–2014) and several national strategic committees and H2020 Space Tunisia-NCP. He is now the head of the Microelectronics and Instrumentation Lab (since 2003) in the University of Monastir and the General Director of the Centre for Research on Microelectronics and Nanotechnology in Sousse Technopark (since 2014).

Rim Barioul, Sameh Fakhfakh Ghribi, Houda Derbel, and Olfa Kanoun

A Myopathy’s Diagnosis System Based on Surface EMG Signal Acquisition, Analysis and Classification Abstract: EMG signals in their raw form are very rich in information, but in order to be valuable in the process of myopathy’s diagnostic, they have first to be amplified then analog to digital converted and filtered. In this paper we investigate the possibilities to realize an EMG measurement system with low cost hardware components and improve the performance of the implementation by signal processing. The measurement system has a minimal design consisting of an amplifier interface conform to typical myopathy data bases and an Arduino-based acquisition system. When electrodes are placed on the skin in the right position, the developed device generates signal form and values, which are similar to those downloaded from EMG database. For data acquisition, signal analysis and classification, an intelligent LabView based data processing software was implemented, which uses also a clinical standard database for normal and myopathic EMG signals. For classification, significant signal features were selected, which are: the Shannon entropy, variance, mean absolute value and signal positive peak amplitude. The investigation shows, that these features are sufficient to discriminate between normal subjects and patients with myopathy. A PCA guided K-means clustering classifier was established and a classification accuracy of 91.7 %. Keywords: Myopathy, PCA guided K-means clustering, Surface EMG MSC 2010: 65C05, 62M20, 93E11, 62F15, 86A22

Rim Barioul, Chair of Measurement and Sensor Technology MST, Technische Universität, Faculty of Electrical Engineering and Information Technology, Chemnitz, Germany; and CEMLab Research Laboratory, University of Sfax, National School of Engineers of Sfax, Sfax, Tunisia; and Digital Research Centre of Sfax, Technopole Sfax, Sfax, Tunisia, e-mail: [email protected] Sameh Fakhfakh Ghribi, CEMLab Research Laboratory, University of Sfax, National School of Engineers of Sfax, Sfax, Tunisia; and Digital Research Centre of Sfax, Technopole Sfax, Sfax, Tunisia, e-mail: [email protected] Houda Derbel, CEMLab Research Laboratory, University of Sfax, National School of Engineers of Sfax, Sfax, Tunisia, e-mail: [email protected] Olfa Kanoun, Chair of Measurement and Sensor Technology MST, Technische Universität, Faculty of Electrical Engineering and Information Technology, Chemnitz, Germany, e-mail: [email protected] De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 23–39. https://doi.org/10.1515/9783110592566-002

24 | R. Barioul et al.

1 Introduction Myopathy (MYO) is a Neuromuscular disorder’s group affecting primarily fibers of skeletal muscles with symptoms including muscle’s dysfunction and fiber’s degeneration. It could be inherited or acquired and it have a progressive clinical course that can appears directly after birth or later after a normal infancy looking period [1]. Although, the most useful ways to have a correct and sure myopathy’s diagnostic are based on the recorded electrical activities of muscles analysis and classification [2]. The recorded electrical activity of muscles is called the electromyogram signal (EMG) [3]. As different noise sources including other biological signals, motion artifacts or even environmental noise can corrupt the EMG signal, its raw form is useless for the diagnosis of myopathy [4]. Thus, filtering is required to transform the original raw signal to a more useful form [5]. Moreover, extracted significant informations from filtered EMG signal could be used for the classification of Neuromuscular pathologies such as myopathy [4]. Performances of myopathy recognition system is based on the investigation of features to distinguish correctly between normal and myopathic. Many parameters can be used for the EMG signals analysis [6], [7], [8], [9], [10], [11] such as maximum Amplitude, spike duration at maximum amplitude, number of peaks, Shannon entropy, wavelet analysis, power spectrum analysis, signal variance... Principal Component Analysis (PCA) technique is manipulated on various engineering and science fields and shows a great potential in data reduction, heart beat detection, classification, signal segmentation and feature extraction, etc [12], [13], [14], [15]. Moreover, K-mean clustering is a well-known method based on the use of centroids to represent classes by limiting the within-cluster errors. The close relation between PCA and K-means clustering methods was explored to propose the PCA guided K-means Clustering used for the classification step on this paper [16], [17]. This paper is arranged as follows: A little introduction of EMG signals, Myopathy, EMG’s feature extraction necessity and PCA guided K-means Clustering classifier were presented in section 1, then signal acquisition step was detailed in section 2. After that, an intelligent LabView based data processing software for Myopathy diagnosis was reported in section 3. Before concluding, a collection of classification results via the investigation of the usability of the PCA guided K-mean Clustering for myopathy detection and a discussion were presented.

2 EMG Acquisition The acquisition of EMG signal is challenging because of Noise and complexity. In this section, we investigate the possibilities to realize a measurement system with minimal hardware design, by using e. g. just an Arduino, and to optimize the performance by

Diagnosis Based on Surface EMG Signal | 25

use of digital signal processing. The question to be answered is about the ability of the system to generalize and differentiating between healthy and myopathic signal. To acquire EMG signals, many previous research works such as [18], [19], [20], [21], [22] were based on the hardware side of the process. They designed very complicated systems to amplify and filter EMG signals based on complex electronic circuit which reduce the reproducibility and increase the cost of these systems. So to reduce the cost and improve the reproducibility of the circuit, a software based EMG filtering is used on this work with a reduced design of an electronic acquisition card (figure 1).

Figure 1: EMG acquisition system on this work.

Surface electrodes were chosen because they are painless and can be easily handled and manipulated by general public [23]. On the designed board level the detected signal is amplified. As well, it is conducted to a host via an Arduino board. Then, with numerical filters, noise is eliminated to keep the useful signal. Finally, this signal is stored to be later explored with the progress of the work.

2.1 Amplification Electromyographically signals are noisy and have a very small amplitude so generally, EMG signals are captured by electrodes and then treated by differential amplifiers [24]. Thus, an instrumentation amplifier with a high CMRR must be used to amplify this signal. Table 1 illustrates the electrical features of four of the most chosen instrumentation amplifier for bio-potential amplifying phase: AD524, AD620, INA128 and INA129.

26 | R. Barioul et al. Table 1: The mean features of the most used instrumentation amplifiers for EMG amplification. Features Supply Range CMRR (min) Standard GAIN GAIN Equation

AD524 [25]

AD620 [20]

INA128 [26]

INA129 [21]

6 V to 18 V 120 dB 1, 10, 100 or 1000 40.0Ω +1 Rg

2.3 V to 18 V 100 dB – 49.4Ω + 1 Rg

2.25 V to 18 V 120 dB – 50.0Ω + 1 Rg

2.25 V to 18 V 120 dB – 49.4Ω + 1 Rg

One can observe that the AD524, the INA128 and the INA129 have higher performances with a CMRR value of 120 dB. Moreover, the use of an amplifier require the addition of a collection of capacitors and resistors surrounding it to set the gain value. However, with the AD524 the operation is simplified by the use of standard gain given by the amplifier’s design. So the AD524 seams to be the best for a simple, low cost and high performance acquisition board.

2.2 Interfacing with Computer In this stage, the analog amplified EMG signal is digitized and driven to a personal computer via an Arduino board to ensure the real time analog to digital conversion. Through the serial communication, the numeric EMG signal is viewed and analyzed with LabVIEW. As the shown signal is a very noised one, a wavelet based filter processed on the MATLAB Node of the LabVIEW software was applied with this signal to minimize noises before the analysis step.

2.3 Wavelet Based Filter Since, the EMG signal as all physiological signals is very noised, it has to be filtered to give accurate features. One of the most used filtering method is that based on wavelet transform (WT). In fact, it confirmed in last years it’s productivity in investigating and denoising non stationary and biomedical signals such EMG signal [4], [27], [28], [29]. WT transforms the signal within it’s time-frequency domain. There are two sorts of wavelet transforms: the Discrete wavelet transform (DWT) and the Continuous wavelet transform (CWT). Both types consume a low time for signal processing however, the efficiency of the DWT have been approved in the analysis of non stationary signals though it yields a high-dimensional feature vector. The equation of DWT was given on [1] by the following equation: ∞

∑ x(t) = ∑

∞

k

∑ d(k, l) 2 2 ϕ(2−k t − 1)

k=−∞ l=−∞

with d(k, l) is a sampling of W(a, b) at discrete points k and l; a = 2k and b = 2kl.

(1)

Diagnosis Based on Surface EMG Signal | 27

Many wavelet functions can be chosen as the base of the used wavelet filter, the Daubechies wavelet was confirmed as the best choice to for EMG signals in previous studies [30], [31] so this wavelet family was chosen to be applied in this work. One can define the Daubechies wavelet transforms by calculating differences and averages via scalar products with wavelets and scaling coefficients. A large number of Daubechies transforms could be considered. Moreover, the fourth Daubechies transform (Db4) has been proposed as the best wavelet function among this family for surface EMG processing in literature [32] because of it’s lowest root mean squared error, this is why we will use it in this work [33], [34], [35]. In [34], the dB4 is defined as follows: k−1 k−1 k−1 k−1 + α4 V2∗m+2 + α3 V2∗m+1 + α2 V2∗m Vmk = α1 V2∗m−1

Wmk

=

k−1 β1 V2∗m−1

+

k−1 β2 V2∗m

+

k−1 β3 V2∗m+1

+

k−1 β4 V2∗m+2

(2) (3)

with Vmk are the scaling signals of the k level of the Db4 wavelet function and Wmk are the Db4 wavelet function of the k level wavelet. Following conditions should be fulfilled, which are the averaging condition (equation 4), the energy conservation condition (equation 5), the orthogonality condition (equation 6) and the linearity condition (equation 7): α1 + α2 + α3 + α4 = √2 α12

+

α22

+

α32

+

α42

=1

β1 = α4 , β2 = −α3 , β3 = α2 , β4 = −α1

0 ∗ β1 + 1 ∗ β2 + 2 ∗ β3 + 3 ∗ β4 = 0.

(4) (5) (6) (7)

2.4 EMG Acquired Signals In this stage, only an acquisition board based on the instrumentation amplifier AD524 was directly branched to the Arduino board. The gain was fixed on 1000. Different noises sources are affecting this signal so it is very perturbed. The signal’s quality was improved by adding a rectifier and an integrator circuit to the original acquisition circuit. The Arduino board was used to digitize and transfer this signal to LabVIEW’s MATLAB Node via the serial communication. The perturbation of this signal by the sampling steps is still persisting. Hence, the denoising stage is very pivotal. For EMG signals, the Db4 is one of the most processed wavelet based filter, so it was handled with LabVIEW for the created system on this work and some EMG signals was recorded. As a verification of a good signal acquisition, signals from normal people were recorded with the developed simple system and compared with normal clinical signals from the standard EMG database used later for the signal analysis and classification

28 | R. Barioul et al. stages in this work. To make this comparison valid we used the same sampling rate, the same amplification gain but with programmed filtering values. Two proprieties were considered on this validation stage: The signal form and the positive pick amplitude. According to figure 2 we can confirm the close representation of Output of the EMG final acquisition circuit (figure 2.a) to normal EMG signal from the clinical database (figure 2.b). This result is proved by the similitude of amplitude rang between the two sets of signals shown in table 2. Thus, the evaluation of the correct acquisition and the close representation of the original EMG signal is validated.

Figure 2: EMG signal form of normal people.

Table 2: Positive Pick Amplitude Comparison. Database Signals Rang

Acquired Signals Rang

0.575–0.85 mV

0.6–0.85 mV

3 LabVIEW Based Myopathy Recognition At the first step, the EMG signal’s acquisition was done. On the next section a myopathy recognition interface is developed to differentiate automatically normal and myopathic signals from the standard clinical EMG signal’s database [36]. It’s a LabVIEW based interface which processes feature extraction and signals classification.

Diagnosis Based on Surface EMG Signal | 29

3.1 Feature Extraction The analysis of EMG signals before their classification is a very critical task. Above all, learning a system to classify signal to normal or myopathic requires the knowledge of features of the two categories. Thus many analysis quantities are calculated on signals from the selected database and the LabVIEW programmed interface for the EMG signal’s analysis was created. There exist many signal features that can be computed, some of them were chosen (figure 3): the positive maximum of the EMG signal, the Shannon entropy calculated by equation 8 [6], the mean absolute value (MAV) (see equation 9 [37]) and the variance (VAR) (see equation 10 [10]). N

Entropy = − ∑(P(X = xi ) log P(X = xi )) i=1

MAV = VAR =

1 L 󵄨󵄨 󵄨󵄨 ∑󵄨x 󵄨 L i=1󵄨 i 󵄨

1 N 2 ∑[x(t)] N − 1 t=1

(8) (9) (10)

Figure 3: An EMG signal graph with a normal and a myopathic signal.

3.2 Database Details For the myopathy learning system, we use 40 signals from 5 normal persons and 5 myopathic patients from the chosen clinical database [36]. In the aim of differencing myopathic from normal signals, the first compared signal’s parameter is the positive amplitude of EMG signals peak from the two groups. The myopathic EMG signal’s showing a lower range of positive amplitude compared to normal EMG signal’s positive amplitude range (see table 3).

30 | R. Barioul et al. Table 3: Positive peak amplitude. Normal Amplitude Range

Myopathic Amplitude Range

0.595–0.850 mV

0.410–0.570 mV

Table 4: Mean Absolute Values. Normal group N1 N2 N3 N4 N5

MAV 0,00712 0,00717 0,00737 0,00717 0,00747

Myopathic group M1 M2 M3 M4 M5

MAV 0,00568 0,00576 0,00563 0,00580 0,00565

Next the MAV was calculated from four tests for each subject on the tow groups. Some of results are presented on table 4. The programmed interface can shows also the Shannon entropy for every analyzed signals. Few of calculated entropy values from signals of normal people and patients with myopathy were collected on table 5. Additionally examples of calculated variance values were cited on table 6. Presentations of all signal’s MAV, VAR and Shannon Entropy values were shown by: the Shannon entropy as a function of the MAV (see figure 4.a), the MAV as a function of the VAR (see figure 4.b) and the VAR as a function of the Shannon entropy (see figure 4.c). Figure 4 proves that the two groups are distinguishable and the MAV, the VAR and the Shannon Entropy could be used as a base of an automatic classifier to separate them. Table 5: Shannon Entropy values. Normal group N1 N2 N3 N4 N5

Entropy values 1,43116 1,43541 1,50787 1,43308 1,45385

Myopathic group M1 M2 M3 M4 M5

Entropy values 0,72458 0,71607 0,72302 0,72177 0,72511

Table 6: Variance values. Normal group N1 N2 N3 N4 N5

VAR values 0,00390 0,00385 0,00428 0,00386 0,00418

Myopathic group M1 M2 M3 M4 M5

VAR values 0,00203 0,00191 0,00204 0,00201 0,00205

Diagnosis Based on Surface EMG Signal | 31

Figure 4: Features presentations for all investigated signals.

4 EMG Classification The final step on this paper is the development of a PCA guided K-means clustering classifier as a tool for myopathy diagnosis by surface EMG signal’s classification.

4.1 PCA Guided K-Means Clustering The regular K-means method is a prototype clustering technique with the use of group’s centroids as representative prototypes of data. Centroids are determined by the minimization of the objective function (equation 11) where Y = (y1 , .., yn ) is the data matrix, ck = ∑i∈Gk is the centroid of the cluster Gk and K is the number of clusters [17], [38]. K

Fk = ∑ ∑ (yi − ck )2 . k=1 i∈Gk

(11)

32 | R. Barioul et al. On PCA, Y = (y1 , .., yn ) is the original data where yi is a variable and n is the number of observation, M = (m1 , .., mn ) with mi = yi − y is the centered data matrix where y = ∑i (yi /n) and C = Y T Y is the covariance matrix of the centered data. C is presenting principal component coefficients, returned as a p-by-p matrix where p is the number of variables used to create Y. Each column of C contains coefficients for one principal component and they are in the order of descending component variance. S = (s1 , .., sn ) is the matrix projection of M on C where rows are corresponding to observations (signals), and columns to principle components. It is to note that on PCA the first few principal component PC are presenting the most of the variation of the original data [16], [39]. The PCA guided K-means clustering method was introduced on [16] as a new technique that applying the PCA directly to the estimation of cluster indicator in K-means clustering applications. This method is based on the redefinition of the previous objective function to create deterministic means for the K-means regular technique by a PCA guided way. So, the new objective function is presented by the equation 12 where nk is the number of points on the cluster Gk and the continuous solution for new groups indicator vectors Qk are the k − 1 principal components of the data matrix Y [16], [17]. n

K

1 ∑ (yiT yj ). n k i,j∈G k=1

Fk = ∑(yi )2 − ∑ i=1

(12)

k

In this work, two classes: Normal (NOR) and Myopathic (MYO) are considered, that is why the 2-way clustering indicated on [16] is implicated to define them as MYO = [i |PC1(i) ≤ 0 ] and NOR = [i |PC1(i) > 0 ] where PC1 is the first principal component, i ∈ (1, . . . , n) and n is the number of rows (observations) on the data matrix.

4.2 Validation Parameters For validating the classification results the most calculated metrics are the classification accuracy (equation 13, it is the percentage of correctly classified signals), the sensitivity (equation 14, it shows the amount of subject affected with myopathy and have been declared as myopathic during the test) and the specificity (equation 15, it shows the amount of normal people declared as myopathic during the test) of the classifier (see table 7) [40], [12]. PT + NT NT + PT + PF + NF PT Sensitivity = PT + NF PT Specificity = PT + PF

Accuracy =

(13) (14) (15)

Diagnosis Based on Surface EMG Signal | 33 Table 7: Validation Symbols Identification. Real Class

Predicted Class

Validation Symbol

Normal Normal Myopathic Myopathic

Normal Myopathic Myopathic Normal

NT NF PT PF

A collection of standard, well known and cheap electrical components have been used to create a design of a based on simple circuit EMG acquisition board. Then a signal analysis interface was programmed on LabVIEW and used to have parameters that can be used to distinguish normal from myopathic signals during the classification process. Finally a PCA guided K-means clustering classifier was processed on LabVIEW to identify the class of each treated EMG signal.

4.3 Learning Step For the learning step of the classifier, 14 normals and 14 myopathic signals were used to create the learning database. Figure 5.a shows extracted features (MAV and VAR values were multiplied by 100). Figure 5.b presents the final classes obtained on the learning step with a classification accuracy of 100 % for the learning database.

4.4 Testing Step For the testing step, 6 normals and 6 myopathic signals were used to create the testing database. Figure 6.a shows the extracted features (MAV and VAR values were multiplied by 100). The figure 6.b presents the final classes obtained on the testing step with a classification accuracy of 91.7 %, a sensitivity on myopathy detection of 85,5 % and a specificity on detection myopathy of 100 % for the testing database.

5 Conclusion In this paper, first a high performance and low cost EMG acquisition board was designed then an intelligent LabVIEW based interface was developed which objective is to make decision on EMG signals: with myopathy (MYO) or not (NOR). Thus, the acquired signal was denoised with a digital wavelet based filter. The closeness of the EMG digitized signal and the real one is evaluated and confirmed by comparing it to signals from a clinical standard EMG database. By measuring some parameters as amplitude and frequency for a normal signal, we conclude that the de-

34 | R. Barioul et al.

Figure 5: Learning Step.

signed board shows a similar signal to typical normal EMG signal. When the system was validated, a set of normal EMG signals have been recorded. Since the system is still on the experimental phase it can’t be used on patient with myopathy yet so a clinical standard EMG database was used to define distinguishing parameters between normal and myopathic EMG signals. Many features such as the root mean square, the short Fourier transform, the signal’s positive pic (maximum positive amplitude), the Shannon Entropy, the MAV and the variance were calculated from 5 normal people and 5 patients with myopathy on this paper. Results shows that both groups can be

Diagnosis Based on Surface EMG Signal | 35

Figure 6: Classification Step.

easily distinguished using last four parameters so they can be used on the classification for myopathy’s diagnosis based on EMG signals. After that a PCA based classifier was applied with previously extracted features and a classification accuracy of 91.7 % was calculated with a specificity on myopathy detection of 100 %.

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[21] D. R. Sanchez, L. Velasquez, L. H. Camargo, Designe of EMG Wireless Surface EMG 6 Channels, Universidad Distrital Francisco José de Caldas, 2012. [22] A. A. Al-Imari, K. A. Rashid, N. H. Al-Egaidy, Design and Implementation of a Surface Electromyogram System for Sport Field Application, 3rd IEEE International Workshop on System-on-Chip for Real-Time Applications, July 2003. [23] J. D. Coud, Conception dún Electromyogramme, Projet de Conception en Ingenierie. Université du Qubec à Chicoutimi, 2010. [24] S. A. Fattah, Md. A. Iqbal, M. A. Jumana, A. B. M. Sayeed Ud Doulah, Identifying the Motor Neuron Disease in EMG Signal Using Time and Frequency Domain Features with Comparison, Signal and Image Processing, International Journal (SIPIJ). VOL. 3, ISSUE 2, April 2012. [25] R. Barioul, S. Fakhfakh Ghribi, O. Kanoun, A Low Cost Signal Acquisition Board Design For Myopathy’s EMG Database Construction, 13th International Multi-Conference on Systems, Signals and Devices, SSD 2016, pp 274–279, 18 May 2016. [26] A. Ruvalcaba, A. Altamirano, C. Toledo, R. Munoz, A. Vera, L. Leija, Multichannel EMG Acquisition System for Arm and Forearm Signal Detection, CINVESTAV-IPN, Mexico D.F., Mexico, 2014. [27] C. Zhan, L. F. Yeung, Z. Yang, A Wavelet-Based Adaptive Filter for Removing ECG Interference in EMGdi Signals, Journal of Electromyography and Kinesiology, VOL. 20, pp 542–549, 2010. [28] V. Tscharner, B. Eskofier, P. Federolf, Removal of The Electrocardiogram Signal from Surface EMG Recordings Using Non-Linearly Scaled Wavelets, Journal of Electromyography and Kinesiology, VOL. 21, pp 683–688, Mars 2011. [29] L. Smital, M. Vitek, J. Kozumplik, I. Provaznik, Adaptive Wavelet Wiener Filtering of ECG Signals, IEEE Transactions on Biomedical Engineering. VOL. 60, ISSUE 2, February 2013. [30] M. S. Hussain, M. B. I. Reaz, F. Mohd-Yasin and M. I. Ibrahimy, Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction, Expert Systems. VOL. 26, ISSUE 1, pp 35–48, 2009. [31] Manel Limem and Mohamed Ali Hamdi, Uterine Electromyography signals denoising using discrete wavelet transform, International Conference on Advances in Biomedical Engineering (ICABME). pp 101–103, 2015. [32] V. Karan, Wavelet Transform-Based Classification of Electromyogram Signals Using an Anova Technique , Neurophysiology. VOL. 47, ISSUE 4, pp 302–309, 2015. [33] E. Gokgoz, A. Subasi, Comparison of Decision Tree Algorithms for EMG Signal Classification Using DWT, Biomedical Signal Processing and Control. VOL. 18, pp 138–144, 2015. [34] J. S. Walker, A Primer on Wavelets and Their Scientific Applications Second Edition, Taylor and Francis Group LLC. 2008. [35] G. R. Scolaro, F. M. Azevedo, C. F. Boos, R. Walz, Practical Applications in Biomedical Engineering, InTech. January 2013. [36] M. Nikolic, Detailed Analysis of Clinical Electromyography Signals EMG Decomposition, Findings and Firing Pattern Analysis in Controls and Patients with Myopathy and Amytrophic Lateral Sclerosis, PhD Thesis, Faculty of Health Science, University of Copenhagen, [The data are available as dataset N2001 at http://www.emglab.net] 2001. [37] M. Kim, T. Kim, D. S. Kim, W. K. Chung, Curved Microneedle Array-Based SEMG Electrode for Robust Long-Term Measurements and High Selectivity, Sensors. VOL. 15, ISSUE 7, pp 16265–16280, 2015. [38] T. Velmurugan, Performance Based Analysis between K-Means and Fuzzy C-Means Clustering Algorithms for Connection Oriented Telecommunication Data, Applied Soft Computing, VOL. 19, pp 134–146, June 2014. [39] I. T. Jolliffe, Principal Component Analysis, 2nd ed., Springer, 2002.

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Biographies Rim Barioul obtained her engineering degree (2015) in industrial informatics from the National School of Electronics and Telecommunications of Sfax (ENET’com), Tunisia. She is currently a PhD student at Chemnitz University of Technology, Germany, and National Engineering School of Sfax, Tunisia. Her research is centered on Electromyographic signals, ElectricalImpedance Myography, Hand gesture recognition and classification algorithms.

Sameh Fakhfakh Ghribi was born in Tunisia in 1979. She received her Electrical Engineering diploma in 2003, the Master Degree in Electrical Engineering in 2004, and the Phd Degree in Electrical Engineering in 2011 from the National Engineering School of Sfax – Tunisia. Now she is an assistant professor in the Higher Institute of Biotechnology of Sfax, a member of Control & Energy Management Laboratory in Sfax University and an associate researcher in Digital Research Centre of Sfax. She works on signal processing and analysis. Her research works are applied on pattern recognition. Since 2015, she works in the field of biomedical instrumentation for the development of medical simulators. She was awarded in 2017 for the design and the realization of a heart and lung sound simulator. Houda Derbel was born on Sfax on 1964. She received (i) the Maitrise on Applied Physics on 1987 from the Ecole Nationale de l’Enseignement Technique, Tunis-Tunisia, (ii) the Diplôme d’Etudes Approfondies on 1988 and the PhD on Energetics on 1990, both from the University of Paul Sabatier – Toulouse, France, and (iii) the Habilitation Degree on 2007, from The Faculty of Sciences of Sfax-Tunisia. Since 2011, she was a Professor in Physics at the Faculty of Sciences of Sfax, University of Sfax, Tunisia. Her interest concerns renewable energies, and applied systems.

Diagnosis Based on Surface EMG Signal | 39

Olfa Kanoun is a full professor for measurement and sensor technology at Chemnitz University of Technology, Germany. Her main research fields are: Impedance spectroscopy, energy harvesting and micro and nano sensors. She received her PhD degree from the institute for measurement and automation at the University of Bundeswehr Munich. She was awarded in 2001 by the German Commission of Professors for Measurement Technology (AHMT. e.V). As senior IEEE member, she volunteers for the Instrumentation and Measurement Society and for IEEE. In 2004 she founded an IEEE IM Chapter and in 2014 she initiated a student branch at TU Chemnitz. She serves as co-chair of the Technical Committee on nanotechnology in instrumentation and measurement (TC 34). In 2001 she was co-founder of the international multi-conference on systems, signals, and devices (SSD) and in 2008 she initiated the annual International Workshop on Impedance Spectroscopy (IWIS). She is author or co-author of 7 books, 52 papers in international journals with peer review, 110 papers in proceedings of international conferences and 6 journal special issues. She is member of the editorial board of TechnischesMessen (De Gruyter) and associate editor of the journal on Digital Signals and Smart Systems (IJDSSS, Inderscience).

Björn Bieske, Gerrit Kropp, and Alexander Rolapp

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters Abstract: Energy harvesting is one possibility to power up small sensor devices using ultra low power technologies. The work of this paper is based on electrostatic energy harvesters using a variable capacitor as a charge pump to convert mechanical into electrical energy [1–3, 6]. These capacitors can be implemented as discrete devices or as micro electromechanical systems (MEMS) integrated on a chip. The aim of this construction is to show a method to characterize the harvested energy of the intrinsic harvester. Due to low currents in the range of microamperes it is difficult to measure exactly without influence on the harvesting process. A new improved topology is used to ensure the autonomous operation of the harvester circuit. This topology allows for measuring makes it possible to measure the converted energy more accurately, even if there are resistive losses in the variable capacitor. Thus we can obtain comparable results on the efficiency of the harvester itself. The amount of harvested energy can be determined easily by processing the measured values. Finally this new measurement setup was implemented as a stand alone measurement device [5]. To demonstrate the new method different types of harvesters were characterized. The electrical efficiency of the harvester output was discussed and a strategy for its optimization was developed. Keywords: electrostatic energy harvester, harvested energy, capacitive harvester, characterization, topologies, efficiency

1 Introduction Electrostatic harvesters transform mechanical energy of rotation, vibration or oscillation into electrical energy. Transducers based on a variable capacitance are used. The variable capacitance is realized by changing the overlap or the distance of electrodes. These variable capacitors can be implemented as discrete devices or as micro electromechanical systems (MEMS) integrated on a chip in a CMOS process. The variable capacitance is the main part of the harvester and works like a mechanical charge pump [1, 10, 11]. The charge will stay constant while lowering the capacitance when no current is flowing during the movement of electrodes. As a result the voltage is rising and more energy is stored in the capacitor. Q=C⋅U

or U =

C Q

Björn Bieske, Gerrit Kropp, Alexander Rolapp, IMMS GmbH, Ilmenau, Germany, e-mails: [email protected], [email protected], [email protected] De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 41–55. https://doi.org/10.1515/9783110592566-003

(1)

42 | B. Bieske et al.

Figure 1: Block diagram of an electrostatic harvester.

Figure 2: Three used QV cycles for vibration energy conversion with a capacitive transducer [6]. © 2013 IEEE. Reprinted, with permission, from [6].

Several principles and topologies of electrostatic harvesters have been published in several papers [1, 2, 7–10] The Q-V cycle has been simulated and analyzed using special tools. The aim was to get a figure of the converted energy depending on Q-V cycle [1, 3–6]. The statement “the area inside this cycle is the converted energy” may not be completely true in any case. The main characteristic parameter of the harvester is the pumped charge vs. time as the characteristic current of the harvester but not the Q-V trace (Fig. 2). The harvested energy also depends on the values of external components like the storage capacitor (see chapter 4). One main question is: How efficient can the harvested energy be used driving a load? Common to all versions of harvesting solutions is the closed autonomous system using an initial pre-charge. Nothing of the initial charge should be “lost” and drained to ground. We will go more into detail when discussing efficiency and optimization of harvesting and the maximization of the power dissipated on a load circuit. The main focus of this paper is to develop a methodology for electrical characterization of the different kinds of variable capacitors achieving comparable results. This leads to the possibility to optimize the harvesting process based on accurate, repeatable and comparable measurements. We will keep the external circuitry as simple as possible. The challenge is to measure currents and voltages without influencing the harvesting process. Losses in parts are mostly not considered in simulations but occur during measurements in practice. Since the main parts of the harvester have very high impedance there should be no connection to AC power supply. Thus we can also ensure that no additional energy is absorbed from AC power lines via ground loops or capacitive coupling disturbing the measurement results. The complete measurement equipment will be battery powered to meet the condition of an autonomous system. Based on a standard topology with resistive fly back as the first version we developed a new topology in two variants overcoming some disadvantages and simplifying the calculation of harvested energy. The main harvesting

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 43

process and its efficiency is nearly the same for both new developed topologies. But in some details the variants differ concerning topology to achieve important boundary conditions for correct measurements. The source of incident mechanical energy and the efficiency of conversion of mechanical to electrical energy will not be discussed here. We reduce the complexity of the problem to the electrical parameters and efficiency of electrical output power of the harvesters to a load under defined conditions. We use fixed values for external components to get comparable measured data.

2 Standard Topologies A. Harvester using resistive fly-back The basic elements of an electrostatic harvester are: – An energy reservoir Cres which is initially pre-charged – A variable harvesting capacitor Cvar operating as a charge pump – A storage capacitor Cstore collecting the harvested charges/harvested energy – Two switches in between them to control the harvesting process, in Fig. 3 realized as diodes – A fly-back element recharging the energy reservoir Cres .

Figure 3: Harvester with charge pump and resistive fly-back [4, 9]. © 2014 IEEE. Reprinted, with permission, from [4].

A resistor Rload is used as fly-back element to recharge the reservoir (Fig. 3). This resistor is also used for measurement purposes. We can measure the current through Rload and the voltage across this resistor, which is the differential voltage between the capacitors Cstore and Cres . Furthermore, we can calculate one of these values according to Ohm’s law. The resistor Rload can be a part of our measuring device. In addition there is no need for very high impedance (R1 = 10 MΩ is used) (Fig. 5). Initially simulations for standard topology were done using LT-Spice (Fig. 4 and 5). The voltages waveforms at the capacitors and current through Rload are shown in Fig. 4. The voltage

44 | B. Bieske et al.

Figure 4: Standard topology: Voltages and current curves to stady state: Vres drops, harvested and load current is 100 nA.

Figure 5: Schematic: Standard topology in LT-Spice.

V(store) across Cstore increases and the voltage V(res) across Cres decreases according to the ratio of these two capacitances (see “param” in Fig. 5). Based on figures for current and voltage we can calculate the power of the harvester delivered continuously to Rload after steady state has reached (Fig. 4). P = (V(store) − V(res)) ⋅ I(R1) = 1 V ⋅ 100 nA = 100 nW

(2)

The steady state (99 %) is reached after a time about 5τ of constant harvesting operation when the harvested energy is equal to the energy dissipated in the load resistor. Initial values for the simulations and measurements are R1 = 10 MΩ and Cstore = 1 μF to be used in (3) to calculate the measurement time (Fig. 4). 5τ = 5 ⋅ Rload ⋅ Cstore = 5 ⋅ 10 MΩ ⋅ 1 μF = 50 s

(3)

Since the switches are implemented using diodes the harvesting process is selfsynchronizing between the mechanical movements and the electrical circuitry concerning the voltage waveforms at the three capacitors. External controlled switches

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 45

would demand additional energy for their control hardware, thus we prefer the topology using diodes. Additionally, we omit the connection to external circuits or power supplies, which could influence the measurement results due to the very high impedance system. This simple solution was chosen in opposition to the statements in [5], that “such topology is not usable in practical applications”. By using this simple topology, we ensure the autonomous operation of the harvesting system and the measurements are taken under the same repeatable conditions as in a real application. B. Harvester using inductive fly-back A slightly different standard topology is using an inductive fly-back to recharge the reservoir Cres . In this case Rload is connected to ground in parallel to Cres [2, 5, 9]. Due to the controlled switch Sw and the inductance L (Fig. 6) charges are pumped up from ground back to the reservoir Cres to compensate a loss of charges to ground. Since the switch control needs a separate driving circuit requiring additional energy, there is no guarantee that no energy will be coupled into the harvesting circuit adding errors to the measured values. For that reason this approach will not be further evaluated for our measurement purposes.

Figure 6: Harvester using inductive fly-back and switching converter [1, 5, 9]. © 2007 IEEE. Reprinted, with permission, from [1].

3 Discussion of Measurement Problems The topologies shown have some disadvantages complicating the measurement process, its repeatability and the calculation of the amount of harvested energy and power. – Since charges are transferred via the charge pump from the reservoir Cres to Cstore the voltage across Cres is changing during the harvesting process depending on the ratio between the capacitance values of Cres and Cstore . The source of the initial voltage “Vinitial ” has to be disconnected from Cres after pre-charging the reservoir to avoid unwanted currents. – Due to the diodes the storage capacitor Cstore is already pre-charged to nearly “Vinitial ” at the beginning of the harvesting process. The subsequent calculation of harvested energy is more difficult due to this pre-charged storage capacitor.

46 | B. Bieske et al. –

If the harvesting capacitor Cvar has resistive losses, the leakage current will not only discharge the reservoir, but also flow through the measurement resistor Rload (Fig. 3). This leakage current is adding severe errors to the measurement results. In this case the topology is not usable for measurements and applications [12].

Energy and power are often mixed up. To clarify these two terms the following should be considered: – The harvested energy can be calculated according to (4) using the measured voltages at the storage capacitor Cstore without any load resistor Rload and any current flow. The energy depends on the capacitance of Cstore : E= –

1 ⋅ C ⋅ U2 2

(4)

The electrical power is calculated by voltage and current measurements using a load resistor Rload dissipating the harvested energy: P =I ⋅U

(5)

Therefore, the converted power of any energy harvester also depends on the used load resistor. The value of the load resistor should match the characteristics of the energy harvester (see chapter 6). The converted power can be measured in the steady state of the harvesting process only. When the harvester has reached its steady state operating point the harvested energy is equal to the dissipated energy in Rload . The constant power conversion requires a constant time invariant input of mechanical energy and movement to obtain a constant value for the electrical power based on voltage or current measurements at Rload . So it is more difficult to measure and to compare the converted power than the harvested energy. To measure the harvested energy, intermittent operating scenarios are possible. The harvested energy can be accumulated step by step over a long time. The transferred energy per cycle depends mainly on the minimum and maximum capacitance of the variable harvesting capacitor and the ratio between these two values. The energy is also depending on the value of Cstore . When the voltage across the storage capacitor is rising, saturation effects occur and less energy can be pumped up per cycle. The desired voltage in a certain time can be determined by the value of Cstore . In any case the external components have to be defined exactly for comparable measurements. Nevertheless, steady-state-measurements provide the advantage of avoiding unwanted saturation effects.

4 Improved New Topology To avoid the problems mentioned above a new improved topology for the harvesting circuit was developed (Fig. 7). The main difference to standard topology (Fig. 5) is that

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 47

Figure 7: New topology of energy harvesting circuit with floating Cstore .

Figure 8: Improved topology: Voltages and current curves to steady state: Vres keeps constant at 50 V , harvested current is 100 nA.

Cstore has no connection to ground anymore but is stacked up on Cres . There is no influence on the harvested energy under the same mechanical conditions. The electrical efficiency of the harvesting process is the same as when using the standard topology. We will get the same values for the harvested current, energy and transferred power using the standard or new improved topology (Fig. 4 and 8). Besides the improved topology offers important advantages to obtain more accurate measurement results: – Since the current charging the storage capacitor Cstore is also flowing back in the reservoir capacitor Cres the charge of Cres and therefore the voltage across Cres is kept nearly constant during the whole harvesting process (Fig. 9). The initial voltage source can stay connected with no influence on harvesting process. So possible resistive losses within the harvesting capacitor Cvar can be compensated by currents delivered by the initial voltage source and can be measured separately. – The storage capacitor is discharged at the beginning of the harvesting process: Vstore is equal to Vres , (Vstore − Vres = U ⋅ Cstore = 0 V). Thus only the harvested energy is stored in Cstore . The calculation of the harvested energy can be easily done according to (4). – Discharging Cstore completely does not discharge the reservoir Cres . There is no loss of charges in the autonomous system due to resistive loads across Cstore .

48 | B. Bieske et al.

Figure 9: Schematic: Improved new topology in LT-Spice.

– –

–

There are no limitations of the values and ratio between Cres and Cstore like using standard topology. If the harvesting capacitor Cvar has resistive losses the leakage current will not affect the measured current and amount of harvested energy. Of course the reservoir Cres will be discharged if there is no compensation current generated by the source of initial voltage. Harvested and leakage current can be separated from each other now. Both resulting energies can be measured separately and then subtracted to get the balance for the whole harvesting process.

The ratio between maximum and minimum capacitance of the variable harvester caC pacitor Cvar−max is the main parameter to estimate the performance of the harvesting var−min circuit. Using this ratio we can determine theoretical limit for the maximum harvested voltage (Fig. 10) during long term energy accumulation depending on the initial precharge voltage Vinitial . Vmax =

Cvar−max ⋅ Vinitial = A ⋅ Vinitial Cvar−min

(6)

Figure 10: New topology (C-ratio = 20): Maximum voltage limit using long term energy accumulation: Vres = 50 V , leakage current is < 1 nA.

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 49

We calculated some parameters using simple rounded figures for a first idealized estimation. For testing in the lab a macroscopic harvester with a variable rotary capacitor of Cvar−max = 2 nF and Cvar−min = 100 pF was used. This harvester with a ratio of C A = Cvar−max = 20 delivers energy enough to evaluate and characterize the harvesting var−min circuits under any initial conditions (initial voltage, cycles etc.). The storage capacity has to be a fixed value to get comparable results. To calculate the expected maximum limits we assume the parameters: – Cvar−max = 2 nF, Cvar−min = 100 pF, A = 20 – Cstore = 1 μF, Cres. = 1 μF – pre-charge of reservoir Cres Vinitial = 50 V – 1 cycle per second fc. = s1 = 1 Hz The output of this harvester may be estimated by: Q = Cvar−max ⋅ Vinitial = 2 nF ⋅ 50 V = 100 nAs

Q Iharvest_avg ≈ = Q ⋅ fc = 100 nAs ⋅ 1 Hz = 100 nA t Q 100 nAs Uharvest ≈ = = 100 mV Cstore 1 μF 1 1 Eharvest ≈ ⋅ Cstore ⋅ U 2 = ⋅ 1 μF ⋅ (0.1 V)2 = 5 nWs 2 2

(7) (8) (9) (10)

The harvested voltage and current flowing trough Rload are rising linearly and the energy and power are increasing with the square of precharge voltage Vinitial and the cycle frequency fc . Furthermore, voltage and energy are depending on the value of the storage capacitance Cstore . The maximum theoretical voltage Vmax is simulated in Fig. 10 and calculated in (11): Vmax = A ⋅ Vinitial = 20 ⋅ 50 V = 1000 V

(11)

In practice all values will be lower due to leakage and losses. We expect a maximum voltage of 400 V.

5 Measurement Results The measurement results were obtained using a harvester with a rotary variable caC pacitor of Cvar−max = 2 nF and Cvar−min = 100 pF. The ratio of Cvar−max is 20. In long term var−min energy accumulation mode with no load a maximum voltage of 350 V was measured. The storage capacitance is Cstore = 1 μF. The limiting factors are the rising leakage currents in switches and diodes at higher voltages and the capacitive losses in the diodes. These mentioned voltages are the absolute maximum ratings. In most cases lower voltC ages can be harvested due to less efficiency and lower ratio A of Cvar−max in integrated var−min transducers.

50 | B. Bieske et al. Table 1: Measured harvested voltages. Parameter

Measurement case

Vinitial in V number of cycles Vharvest in V Energy Eh in uWs

10 160 3,3 5,4

25 160 8,2 33,6

50 160 16,3 133,0

50 80 8,3 34,4

50 10 1 0,5

50 1 0,1 0,005

The measurement values shown in Table 1 using the harvester demonstrator in the lab show that a voltage of about 0.1 V per cycle is harvested when the reservoir Cres is precharged using a source Vinitial = 50 V and Cstore = 1 μF. This result matches with the theoretical calculations in (8) and simulation results of I = 100 nA obtained using LT-Spice (Fig. 4 and 8). At the first linear part of the voltage curve the harvested voltage is linear rising depending on V_initial and the cycle count.

6 Efficiency and Optimization The electrostatic harvester is generating a characteristic current per cycle. This current depends on the initial voltage Vinitial and the maximum and minimum capacitance of Cvar . The output parameters of the harvesting circuit (voltage, energy and power) depending on this typical harvester current but also on the values of the external components Cstore and Rload . The power dissipated by the load is calculated as P =U ⋅I 2

P = I ⋅ Rload

(12) (13)

Since the current I is given by the harvester type as the amount of moved charges per time the load resistance should be maximized for maximum output power. But this load resistance can not be changed easily because it is given by the load circuitry. Since the harvester acts as a current source its internal equivalent resistance (Ri ) can be estimated to a very high value of 1 GΩ, based on our demonstrator generating typical harvester current of 100 nA at an output voltage level of 100 V. If the external load resistance (RL ) is in the range of about 10 MΩ there is a large mismatch between RL and Ri resulting in a very low power efficiency of about 1 % only (Fig. 11). The purpose of this work is to create a measurement methodology to measure the harvested energy and the usable power of electrostatic energy harvesters. Based on those measurements a strategy to optimize the efficiency can be derived. The harvester’s current is the main parameter for its internal resistance and the harvested energy at a certain initial voltage. To increase the efficiency of the harvesting process the main questions are: – How can we increase the harvester current depending on the values of the harvesting variable capacitor, cycle frequency and initial voltage?

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 51

Figure 11: Power efficiency vs. load to source impedance ratio.

– –

How can we match source and load impedance to get a higher efficiency? What devices can be used to store the harvested energy?

The current can be increased directly by a higher cycle frequency or by a higher capacitance of the harvester due to a higher amount of moved charges per time. A higher current lowers the source resistance resulting in a higher efficiency as described above. A higher initial voltage also increases the characteristic harvester current but does not lower the source resistance due to higher source voltage. To clarify the relation between voltage, energy and power we will look at an example in practice: A load of 10 MΩ should be powered up to a voltage of 5 V in minimum to ensure a proper work for a sensor (Fig. 12). How the harvester’s components should be dimensioned? The table (Tab. 2) shows different scenarios for Cstore and the resulting values for harvested voltage and energy. We see that the value for Cstore = 1 μF is the best choice allowing the longest operation time based on a voltage of 10 V across the capacitor. But there is less energy stored in Cstore compared to a configuration using Cstore = 10 nF charged to 1000 V.

Figure 12: Voltage vs. time for different capacitors Cstore .

52 | B. Bieske et al. Table 2: Voltage, energy and capacitance vs. discharge time. Cstore

Vharvest

Eharvest

t =R∗C

Vload > 5 V

10 nF 100 nF 1 μF 10 μF 100 μF

1000 V 100 V 10 V 1V 0, 1 V

5 mWs 500 μWs 50 μWs 5 μWs 0, 5 μWs

0, 1 s 1s 10 s 100 s 1000 s

0, 5 s 3s 7s – –

So we have to take into account that high voltages and energies are not the parameters to be optimized for reliable stand alone operations of low power and low voltage devices only. The voltage level has to match he conditions required by the load circuit. Capacitors discharge their voltage exponentially in contrast to batteries or accumulators. Otherwise capacitors have very low self discharge. Another approach for load matching is a buck converter designed for high efficiency especially at low currents used to convert high voltages into currents delivered to the low power and low voltage load. Such a converter will act as impedance matching between harvester source and load. The stored energy will be usable at a lower voltage level at high efficiency.

7 Implementation of Measurement Device The implementation of the stand alone measurement device “Harvest Meter 5.0” is presented in Fig. 13. The harvesting topologies are shown on the front panel. The measurement equipment is intuitively to operate. The measurement mode can be switched between the old and the new version. So the electrical efficiency can be measured under defined conditions and is comparable between different harvester types. Different measured parameters can be displayed by two panel meters: – Harvested voltage across Rload in Volts – Current from reservoir Cres to Harvester Cvar in nA

Figure 13: Measurement device “Harvest Meter 5.0” for testing harvester.

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 53

– – –

Voltage of reservoir Cres in Volts Current from source of initial Voltage to reservoir Cres Current (average) through load resistance Rload in nA

All voltage measurements are taken using an input resistance of 1 GΩ. All current measurements are taken using serial resistance of 100 kΩ. Therefore we can estimate an error of about 1 % due to the panel meter using a 10 MΩ load resistance. A shielding alumina case is used to prevent electrostatic and electromagnetic interferences.

8 Conclusion & Outlook A new methodology based on an improved topology for more accurate measurements used for characterization and test of electrostatic energy harvesters was developed. The different types and topologies of harvesters can be characterized by a set of parameters like characteristic current and output voltage. The main parameter is the charge pump current harvested per cycle. It is based on C the pre-charge voltage Vinitial at Cres and the ratio of Cvar−max . This current and the cycle var−min frequency fc provide the harvester current for a certain time interval. This accumulated current results in an amount of energy stored after the harvesting time in a defined storage capacitor Cstore . All other parameters (energy, power, voltage) are depending on the external circuit and properties of the load. A measurement device was developed to measure not only the harvested energy and power but also to measure separately the leakage currents in the harvester’s variable capacitors Cvar . In summary the energy balance of the harvesting process can be calculated. The system is autonomous without any energy entry from the environment to ensure accurate measurements. To increase the efficiency of the harvester a matching of source and load impedance is required. The matching can be achieved by a higher harvester current or a buck converter running highly efficient at lowest currents. Further works will optimize the circuitry to maximize the harvested energy. Especially the use of micro batteries is an interesting alternative to capacitors. The relation between Q-V cycles, external components and optimizing the harvestable energy have to be simulated and analyzed more in detail.

Bibliography [1]

D. Galayko, R. Pizarro, P. Basset, A. M. Paracha. AMS modeling of controlled switch for design optimization of capacitive vibration energy harvester. Behavioral Modeling and Simulation Workshop. IEEE Int., BMAS 2007, 115–120, San Jose, 2007.

54 | B. Bieske et al.

[2]

H. R. Florentino, R. C. S. Freire, Alan V. S. Sá, C. Florentino, D. Galayko. Electrostatic vibration energy harvester with piezoelectric start-up generator. IEEE Int. Symp. of Circuits and Systems (ISCAS), 1343–1346, 2011. [3] E. O’Riordan, E. Blokhina and O. Feely. Modelling and Analysis of Vibration Energy. IEEE Int. Symp. of Circuits and Systems (ISCAS), 1247–1250, 2014. [4] Elena Blokhina, Eoghan O’Riordan, Orla Feely, Dimitri Galayko. Nonlinearities in electrostatic vibration energy harvesters: A review using the example of a charge pump conditioning circuit. IEEE Int. Symp. of Circuits and Systems (ISCAS), 2604–2607, 2014. [5] D. Galayko, A. Dudka, A. Karami, E. O’Riordan, E. Blokhina, O. Feely, P. Basset. Capacitive Energy Conversion With Circuits Implementing a Rectangular Charge-Voltage Cycle–Part 1: Analysis of the Electrical Domain. IEEE Trans. on Circuits and Systems I, 62(11):2652–2663, 2015. [6] D. Galayko, E. Blokhina. Nonlinear Effects in Electrostatic Vibration Energy Harvesters: Current Progress and Perspectives. IEEE Int. Symp. of Circuits and Systems (ISCAS), 19–23, Beijing, China, 2013. [7] S. Meninger, J. Mur-Miranda, R. Amirtharajah, A. Chandrakasan, J. Lang. Vibration-to-electric energy conversion. IEEE Trans. on Very Large Scale Integration Systems, 9(1):64–76, 2001. [8] A. C. M. de Queiroz. Electrostatic vibrational energy harvesting using a variation of Bennet’s doubler. 53rd Midwest Symp. on Circuits and Systems, 404–407, Seattle, USA, 2010. [9] B. C. Yen and J. H. Lang. A variable-capacitance vibration-to-electric energy harvester. IEEE Trans. on Circuits and Systems I: Regular Papers, 53:288–295, 2006. [10] E. O. Torres and G. A. Rincón-Mora. Energy-Harvesting System-in-Package (SiP) Microsystem. ASCE Journal of Energy Engineering, 134(4):121–129, 2008. [11] G. J. Sheu, S. M. Yang, and T. Lee. Development of a Low-Frequency Electrostatic Comb-Drive Energy Harvester Compatible to SoC Design by CMOS Process Sensors and Actuators A, Physical, Vol.: 167, No.: 1, S. 70–76, 2011. [12] R. Hahn, Y. Yang, U. Maaß, L. Georgi, J. Bauer, K.-D. Lang. Variable capacitor energy harvesting based on polymer dielectric and composite electrode. 4th Int. Symp. on Energy Challenges & Materials – working on small scales, 11–13, Aberdeen, Scotland UK, 2015.

Biographies Björn Bieske (DL5ANT) has studied information techniques at the Technical University Ilmenau. Since 1991 he was working there in the department RF techniques in several projects. In 1996 he joined the IMMS GmbH and is responsible for the fields of RF systems and RF measurement techniques.

Characterization and Optimizing the Efficiency of Electrostatic Energy Harvesters | 55

Alexander Rolapp is currently a Scientific Assistant at the Institute for Microelectronic and Mechatronic Systems in Ilmenau, Germany. He earned his academic degree in Computer Science Engineering at the Technical University of Ilmenau in 2007. In his thesis work he developed a precise multichannel measurement system for analog characterization and test of application specific integrated circuits. Since 2007 as a measurement engineer, he is contributing to recent microelectronic developments in funded research projects.

Gerrit Kropp has studied electrical engineering and information technology with main focus on high frequency technology at the Technical University Ilmenau and graduated in 2010. He joined the IMMS GmbH in 2010 and works as hardware designer and measurement engineer.

Elena Sobotta, Robert Wolf, and Frank Ellinger

Wideband Power Amplifier with Auto-Transformer Based Output Impedance Transformation Network Abstract: A broadband, linear power amplifier with an auto-transformer based output impedance transformation network for DVB-T (digital video broadcasting-terrestrial) is presented. The transformation network includes an auto-transformer combining a broadband, high efficient design with a small sized layout. Investigations on conventional reactive networks determine the improved characteristic. The complex autotransformer design together with an amplifier stage can be faced by a structural approach. To maintain the high bandwidth of the power amplifier, a totem-pole driver is realized at the input. The power amplifier was implemented in a 0.25 μm SiGe BiCMOS process and consumes 375 mW at a supply voltage of 3 V. The high relative bandwidth of 120 % is verified by measurements. Further it exhibits an output referred 1 dB compression point of 24.4 dBm with an efficiency of 38 % at the working frequency of 700 MHz. Additionally system measurements for the DVB-T standard are performed and reveal a minimum of 5 dB back-off to match to the error vector magnitude requirements of the standard. Keywords: power amplifier (PA), impedance transformation network, transformer, auto-transformer, load line, SiGe BiCMOS, digital video broadcasting-terrestrial (DVB-T), totem-pole driver

1 Introduction The demands on linear power amplifiers (PA) like high linearity, efficiency, output power and bandwidth in modern communication systems are contradictory. Especially the requirements on linearity and efficiency define the well-known tradeoff. Additionally for fully integrated solutions in current semiconductor technologies with limited breakdown voltages, the quality factor of the passive components restricts the output power and the bandwidth for sub- and low-GHz range. Therefore many ap-

Acknowledgement: The research leading to these results has received funding by the Federal Ministry of Education and Research (BMBF) in the project DEAL. Elena Sobotta, Robert Wolf, Frank Ellinger, TU Dresden, Chair for Circuit Design and Network Theory, Dresden, Germany, e-mails: [email protected], [email protected], [email protected] De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 57–76. https://doi.org/10.1515/9783110592566-004

58 | E. Sobotta et al. proaches implement off-chip reactive elements with higher quality factor for output impedance transformation networks. However, this results in a higher complexity and size of the power amplifiers. One possibility to enlarge the bandwidth is applying dual band matching techniques but these directly degrade the efficiency and significantly enlarge the required chip area. To overcome those problems, a structural change of the impedance transformation network at the output of the PA is required. Using transformers for wide-band impedance transformation and for power combining is a well-known approach but conventional integrated transformers suffer especially in silicon based technologies in the mentioned frequency range from high losses and big chip area [1–7]. In the work of [8], the efficiency could be increased in the low output power region. Another possibility to reduce the losses is the use of active transformer structures for on-chip impedance transformation [9, 10] or parallel-segmented transformer [11]. However, the drawback of these approaches is that the size of the transformer still dominates the chip area. Thus, we propose an auto-transformer based PA with significantly less losses for the same impedance transformation ratio [12]. To fully exploit the benefits of this approach, we use an active driver and resistive matching at the input of the PA. In [13], the design of an integrated HBT power amplifier with an auto-transformer as output impedance transformation network is shown. However, this work does not show a structured design process, which is mandatory to obtain the maximum efficiency. In the following sections, we investigate on reactive output impedance transformation networks to overcome the challenge of broadband and high efficient impedance transformation networks. Then the maximum efficiency with lossy reactive elements is plotted and the results will be compared with the auto-transformer based transformation networks. Then we face the challenging design of an auto-transformer with an amplifier stage. First, a model to calculate the large signal bandwidth is derived, which is necessary for broadband applications. Afterwards, we discuss the design of the driver circuit and the benefits of a totem-pole driver in comparison to the conventionally applied common-collector stage. The functionality is proved by on-waver and PCB based measurement results showing the expected high large signal bandwidth.

2 Analysis of Impedance Transformation Networks The influence of output impedance transformation networks on large and small signal behavior especially the efficiency and the bandwidth is very high. For this reason, the bandwidth and the efficiency of transformation networks are investigated and an alternative auto-transformer based network is described.

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2.1 Conventional Impedance Transformation Network Commonly used reactive output impedance transformation networks are shown in Fig. 1. These networks are suitable for transforming load impedances smaller than the system impedance to the system impedance. For the optimum load impedance of the most designed transistor fields, this kind of impedance transformation ratio is required. The inductor LB compensates the parasitic capacitance of the transistor field. By the elements LM and CM and at the working frequency, the system impedance is transformed to the real part of the load impedance. The transformation at the working frequency to the optimum load with these networks provides one degree of freedom. It can be used for example for dual band networks or for efficiency optimizations.

Figure 1: (a) LCL impedance transformation network. (b) LLC impedance transformation network.

In [14] the approach with dual band matching describes how the inductance LB and the capacitance can be used for impedance transformation at two frequencies. Therefore, no additional element is required and bandwidth of the network is enlarged. Another possibility to use the degree of freedom provided by the impedance transformation networks is to optimize the efficiency. The efficiency is calculated for LCL and the LLC impedance transformation networks and the results of these investigations are depicted in Fig. 2. The losses for biasing are not considered. Therefore an efficiency of 100 % can be achieved for networks without inductors in the transformation network. The inductors in the transformation network are modeled with a quality factor of 10. For the impedance transformation ratio of the dual band amplifier [14], the maximum efficiency is marked and achieves 77 % for the LCL network and 75 % for the LLC network, respectively. This means that at least 20 % of the output power is consumed by the impedance transformation network. These results demand for further optimizations or a complete different structural approach.

2.2 Auto-Transformer Based Impedance Transformation Network Instead of output impedance transformation network with reactive elements a transformer can be implemented. The main advantage of a transformer is that the imped-

60 | E. Sobotta et al.

Figure 2: Contour diagram of the maximum efficiency ηmax of the transformation from the reference ⋆ impedance to the impedance Zin with a quality factor of the inductors of 10; contour lines in steps of 10 % from light grey: 0 % to black: 100 %. (a) LCL network, lossy. (b) LLC network, lossy.

ance transformation is done by magnetic coupling which is frequency independent. This characteristic provides inherently a large bandwidth. Therefore the impedance transformation is not valid for only one or two frequency points like for narrow band LC networks. However conventional transformers are nevertheless bulky and consume a lot of chip area. This issue can be solved by using an auto-transformer like shown in Fig. 3. The conventional LC network can be replaced by a single auto-transformer. This approach has many additional advantages. Firstly, the two bulky coils of the LCL or LLC impedance transformation networks are replaced by a single inductive component. Therefore, the required chip area is reduced and the efficiency improved. Secondly, the parasitic elements of a transformer are inductive which allows the compensation by capacitors. The parasitic capacitance of the transistor can be used for this purpose. For a LC network this compensation is mostly done by the bias inductor LB . This includes also the third advantage. The supply for auto-transformer based networks can also be fed through the transformer so there is no need for additional bias chokes.

Figure 3: Circuit architecture for an output impedance transformation network via auto-transformer.

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| 61

The only disadvantage of an auto-transformer is the shared winding between the first and secondary side and thereby both sides are DC coupled. But, this behavior is not critical for this application. However, the design process of a transformer is challenging and includes many iterative steps to match the transformer to a suitable transistor field. In the next section a structured design process for auto-transformer based power amplifiers is presented.

3 Design For the structured design, the optimum load impedance of the transistor field has to be determined. This can be done by the load-pull analysis or by the load line theory. In this design, the load line is calculated and the optimum load impedance has to fit conjugate-complex wise to the impedance of the auto-transformer. The autotransformer is designed for this impedance and is optimized for high efficiency. For large signal simulation a model of the auto-transformer is introduced. To maintain the high bandwidth a suitable input driver is required. The design of the totem-pole driver is described. This section concludes with the circuit description of the whole power amplifier.

3.1 Load Line Design To achieve maximum uncompressed output power, the load at the intrinsic transistor has to be resistive and has to fit to the operating point [15]. Since the load at the intrinsic transistor differs significantly from the load physically connected to the collector of the transistor, load-pull simulations are often applied. An analytical solution for this problem was published in [16]. There was shown that by a linear model the calculated optimal load is practically the same than the load obtained by load pull simulations. Unfortunately, the published approach uses some assumptions and simplifications which are not universally applicable. Based on this idea we like to show an explicit but numerical solution for the same problem without any simplifications. Thus, the optimal load is guaranteed even for strong repercussion, and for feedback or stacked amplifiers. Starting point of the calculation is the pure amplifier core in the configuration that allows the simulation of the Y-parameters, which is depicted in Fig. 4. Thereby, the well-known Y-parameters can be determined: [

I1 Y ] [ 11 I2 Y21

Y12 V ][ 1 ] Y22 V2

(1)

In addition to this, two more transfer functions are required. They give the relation between the port voltages to the voltage across the inner transfer current source and

62 | E. Sobotta et al.

Figure 4: Simulation setup for the determination of the transfer functions.

its current and are given: VTF = [Av,TF1 Av,TF2 ] [ ITF = [YTF1 ATF2 ] [

V1 ] V2

V1 ] V2

(2) (3)

These transfer functions can also be simulated by the same setup. In case that the current of the transfer current source is not accessible in the transistor model, it can be calculated by the control voltage VBE or VGS and the transconductance gm of the transistor. The load of the transfer current source is then given by: ZL,TF =

VTF = RL,TF −ITF

(4)

and is chosen to be resistive with a value depending on the operating point. Putting (3) into (4) results in: V1 = −

Av,TF2 + RL,TF YTF2 V Av,TF1 + RL,TF YTF2 2

(5)

Using this result and the first row of (1) the requested external load admittance can be calculated to be: YLT =

1

ZL,T

=−

Av,TF2 + RL,TF YTF2 I2 = Y − Y22 V2 Av,TF1 + RL,TF YTF2 21

(6)

Thus, the load line at the inner transistor is implemented. Large signal simulations for several test cases including a wide variety of technologies and operating frequencies proved the validity for large signal excitation. The remaining parameter is the value of RL,TF which can be well estimated by the operating point. By small signal simulations and by these equations, the differential, external load admittance YL,T of this chip is equivalent to a 33 Ω resistor in parallel with a 19.8 nH inductor. The inductance can be compensated by an effective capacitance of 2.6 pF. In case of designing the interface between the transistor field and the transformer, RL,TF is chosen such that the external load admittance YL,T fits conjugate-complex wise to the transformer, which will be derived in the next section, and the operating point is chosen accordingly.

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3.2 Auto-Transformer Design The design and the optimization of LC-based impedance transformation networks can be done by textbook calculations. Thereby, the quality factor of the coil is maximized and there is only a slight difference whether the circuit is differential or single-ended. In contrast to this, the design and optimization of a differential auto-transformer are much more complicated and less flexible. The first problem is to find a suitable geometry. For integrated solutions usually a planar topology is applied due to the high resistance of vias, and due to the high sheet resistance of the lower metals and their higher coupling to the substrate in comparison to the upper metals. Only very few integrated auto-transformer or auto-transformer like geometries are published. The published ones either exhibit complicated and lossy crossings [17] or apply two singleended structures for a differential circuit [18]. We propose a geometry that only crosses adjacent windings every eighth of a winding. Hence, only two metals are used for the whole geometry and the feeding lines just have to contact windings at the outside. This geometry is shown in Fig. 5. This example includes 3.5 windings for each phase whereas 2 windings are shared between the primary and the secondary side. This way the contacts fit well to the layout of the whole chip.

Figure 5: Geometry of the integrated, differential auto-transformer.

To decrease the DC resistance for all connections besides the crossings, the two used metals are connected in parallel. Thereby, not only the effective resistance of the metal is decreased but also the impact of the vias. Although the capacitive coupling between the lines and the substrate is increased, the gain by less resistance is more significant for the applied technology. In the same way a wide variety of different numbers of windings can be implemented. For some of them, the regular arrangement of crossings might has to be broken by leaving some crossings out. But crossing only neighbored windings can very often be maintained. The next problem is how to evaluate the simulation results gained by an electromagnetic field (EM) simulator for a given geometry. The EM simulator only exports

64 | E. Sobotta et al. S-parameter, which indicates not the efficiency or the optimum source and load reflection coefficients. After calculating these values, the geometry can be optimized. Since this part of the circuit is passive and linear, the n-port theory can be used to calculate the efficiency. Thus, the maximum efficiency of the transformer is achieved by a conjugate-complex match at each port and is given by the maximum available gain Gmax . Since the transformer shall be optimized for differential excitation at the primary and the secondary side, the EM simulation results of the 5-port can be reduced to the differential S-parameters forming a 2-port which allows the calculation of the maximum efficiency Gmax by: Gmax =

|SDD21 | (K − √K 2 − 1) |SDD12 |

(7)

where K is the Rollett’s Factor. The optimum source and load reflection coefficients ΓS,Opt and ΓL,Opt can be calculated by solving a quadratic equation, which results in: Γ⋆S,Opt = ΓL,Opt =

1 (−b ± √b2 − 4|a|2 ) 2a Γ⋆S,Opt − SDD11

(8) (9)

Γ⋆S,Opt SDD22 − ΔS

with: ΔS = SDD11 SDD22 − SDD12 SDD21 a=

⋆ −SDD11

(10)

⋆

+ ΔS SDD22 2

2

2

b = 1 + |SDD11 | − |SDD22 | − |ΔS|

(11) (12)

Therewith, it is possible to calculate very quickly the maximum efficiency of the transformer and optimize the geometry. For the shown geometry, the maximum efficiency at 700 MHz is 86.1 % and can be calculated according to (7) with the values of the simulated S-parameters. The value of the efficiency is obtained by a differential source and load admittance of (47.6 + 6.6j)mS and (15.9 + 12.8j)mS, respectively. For the load, this is equivalent to a 63 Ω resistor in parallel with a capacitor of 2.9 pF. Unfortunately, this seems not to fit well to the differential system impedance of 100 Ω but, also for this case the efficiency can be calculated assuming a conjugate-complex matched source. This results in the power gain Gp . The compensation capacitor in parallel to the load is an additional degree of freedom in order to maximize the efficiency. Thereby, we obtain for this example a maximum efficiency of 85.1 %, which is just a slight degradation and which means that this geometry is still applicable for a power amplifier. The demanded source impedance is then equivalent to a resistor of 32 Ω in parallel with a capacitor of 1.2 pF. Unfortunately, the transistor field, which is discussed in the last section, shows

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a parasitic capacitance of 2.6 pF. Thus, the compensation in parallel to the load has to be decreased to maximize the transducer gain GT of the transformer, which then results in the optimal operation for the given boundaries. For this example this results in the numbers listed in Tab. 1. Table 1: Analysis Steps of the Transformer. Efficiency ηAT 86.1 % 85.1 % 84.7 Ω

Source (R parallel C) R 21 Ω 32 Ω 33 Ω

C 1.5 pF 1.2 pF 2.6 pF

Load (R parallel C) R 63 Ω 100 Ω 100 Ω

C 2.9 pF 2.6 pF 2.1 pF

Remark

Conjugate match Load R set and C optimized Source C set and load C optimized

Although this transformer is not operated optimal this way, the achieved simulation results are better than those of a transformer with an optimum load resistance of 100 Ω. This is because of lower DC losses which cannot be taken into account by the n-port theory. For comparison, a conventional PA in this technology with the same impedance transformation ratio and with optimized coils with a quality factor of 10 exhibits an efficiency of the impedance transformation of ηLC = 73 %. The efficiency is defined as the ratio between input and output power: Pout = η Pin

(13)

The power loss describes the difference between input and output power and yields: Ploss = Pin − Pout = (1 − η)Pin

(14)

with ηLC = 73 %, and ηAT = 85 % and the assumption that both networks are fed by the same input power, the ratio of the power losses can be calculated to: Ploss,AT 1 − ηAT = = 55 % Ploss,LC 1 − ηLC

(15)

Thus, the auto-transformer based PA shows 55 % less losses compared to a PA with LC network.

3.3 Auto-Transformer Modeling In order to perform reliable large signal simulations, the auto-transformer must be carefully modeled. In our case, the tools, which are provided by Cadence and

66 | E. Sobotta et al. Sonnet EM, fail to generate a stable model. Thus, the transformer has to be modeled manually. Often an equivalent T-network is used for transformers. Since the designed transformer is a 5-port a single T-network is not sufficient. The usage of one T-network for each phase does not describe the transformer correctly since those networks are uncoupled. A very effective way to model this differential transformer is by modeling the common mode and the differential mode independently. The obtained networks are then combined by ideal baluns. This is illustrated in Fig. 6. This equivalent circuit has one degree of freedom for each T-network, which can be used to ensure positive values for each component.

Figure 6: Model of the differential auto-transformer.

3.4 Driver Circuit Design A main limitation of the usability of PAs comes from narrow-band input matching. To avoid this and to exploit the high bandwidth of the auto-transformer, an active driver circuit with resistive matching is beneficial. An often applied topology for such a driver circuit is the common-collector stage. Although the required voltage swing at the input of the main stage is very low, the high capacitive component of the input of the main stage requires a very high operating point current for linear operation. For this PA the equivalent capacitance of one part of the main stage is 20 pF, which results in an operating point current of 10 mA for each phase. This already noticeably degrades the efficiency of the whole PA.

Wideband Power Amplifier with Auto-Transformer

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To circumvent this problem, a totem-pole stage is applied. This kind of stage was already investigated for resistive loads in [19]. This theory can be extended to reactive loads. The benefit is that the achievable current swing of a totem-pole stage is twice the current swing of a common-collector stage, or the other way around the operating point current can be cut by half. In addition, the totem-pole stage has the ability to expand its average collector current and even to switch to class B operation for increasing drive. This means that the operating point current can be chosen even lower. The topology of the driver is shown in Fig. 7.

Figure 7: Circuit architecture of the totem-pole stage.

The currents of the two transistors of the totem-pole stage have to add up constructively. According to small-signal theory, the emitter current of the upper transistor and the collector current of the lower transistor should be equal in phase and amplitude for maximum undistorted driving ability. This is ensured by a dummy load network with twice the impedance of the actual load. Nevertheless, better large signal performance is achieved by a lower impedance ratio. For this application it is sufficient to implement the dummy load network by a parallel connection of a resistor and a capacitor as it can be seen in the full schematic in Fig. 8.

3.5 Circuit Description The schematic including all blocks discussed previously is shown in Fig. 8. Although the circuit is differential, only one phase is illustrated for clarity reasons. In order to reduce parasitics and chip area, some capacitors are connected from one phase to the other instead of being connected to ground. Thereby, the required capacitance is just one half. For the main stage, a cascode structure is used to ensure stability and to decrease the impact of the supply on the transfer characteristics allowing the use of a modulated supply.

68 | E. Sobotta et al.

Figure 8: Schematic of the whole differential circuit and the bias circuits.

When idle the main stage takes 120 mA from a 3 V supply and the driver requires just 6 mA from a 1.3 V supply. For this operating point the output power is above 24 dBm. The chip was implemented in IHP’s 250 nm SiGe BiCMOS technology. A picture of the chip and the wire-bonded chip can be seen in Fig. 9. The chip size is 1.1 mm by

Figure 9: Photograph (a) of the die and (b) of Chip-on-Board (COB) mounted chip.

Wideband Power Amplifier with Auto-Transformer

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1 mm. The area occupied by the transformer is the same as the area required for one of the two coils of the conventional PA used for comparison in section 3.2. Hence, the overall chip size is reduced by 38 %. For monitoring, the chip includes a temperature sensor in the lower left corner resulting in two more pads than apparent from the schematic. The chip temperature can be measured with an accuracy of ±2 K and ±5 K with and without offset calibration, respectively. This allows checking the thermal resistance introduced by the mounting which is 25 K/W on-waver and 50 K/W on PCB.

4 Measurement Results The circuit was measured on-waver as well as on PCB. S-parameter and compression measurements were performed by an R&S ZVA67. For intermodulation and for distortion measurements, OFDM signals were generated by the R&S SMBV100A and measured by the R&S FSV7. In the diagrams, the simulation result, the on-waver and the PCB measurement results are shown with dotted, solid, and dashed lines, respectively. The degradation of the PCB-based results can completely be explained by the losses and the limited bandwidth of the applied balun. In Fig. 10, the measured current consumption over the available generator power shows an expanded characteristic in the higher power region.

Figure 10: Current consumption versus available generator power.

The measurement results of the differential S-parameters are shown in Fig. 11. The parameter SDD12 is well below −55 dB and is therefore not shown in the graph. The parameter SDD11 was simulated to be below −20 dB and worsened only by process variations of the resistor used for the input matching. Although, the point of maximum gain is slightly shifted towards lower frequencies, the measurement results fit well the expectations. The circuit is unconditional stable in differential mode and common mode.

70 | E. Sobotta et al.

Figure 11: Differential S-parameters versus frequency; simulation and on-waver measurement results with dotted and solid line, respectively.

Figure 12: Output power versus available power of the generator at 650 MHz.

Figure 13: Power added efficiency versus available power of the generator at 650 MHz.

The compression point measurement was performed at the center of the operating band. The output power Pout versus the available power from the generator PG,av is shown in Fig. 12. The corresponding power added efficiency PAE is shown in Fig. 13.

Wideband Power Amplifier with Auto-Transformer |

71

Compared to a classical class-A PA, the back-off behavior of this PA is much better achieving the same good linearity. This results from the strongly expanding average collector current which is thereby adaptive to the input signal. To prove the good large signal bandwidth the compression point measurements were performed for several frequencies. This result is shown in Fig. 14. The 3 dB bandwidth is 800 MHz around 650 MHz, which is a relative bandwidth of 120 %. In the same way, the PAE shown in Fig. 15 for several back-offs, proves this large bandwidth. Considering the impedance transformation ratio of the output, the achieved bandwidth is excellent.

Figure 14: Output power at the 1 dB compression point versus frequency.

Figure 15: Power added efficiency versus frequency for 0 dB to 9 dB back-off; on-waver, and PCB measurement results solid, and dashed, respectively.

Finally, the distortion of OFDM signals was investigated. The test signal was a 2k QAM16 modulated DVB-T signal. The error vector magnitude EVM of this signal without any predistortion is displayed in Fig. 16. It can be seen that 5 dB back-off is already suffi-

72 | E. Sobotta et al.

Figure 16: Error vector magnitude versus available generator power normalized to the available generator power at the 1 dB compression point.

Figure 17: Measured constellation diagram with a QAM16 modulated OFDM signal with 8 MHz bandwidth and 2048 sub carriers.

cient for this signal to get a EVM below 5 %, which results in a measured efficiency of 19 %. The corresponding constellation diagram for several back-off regions is depicted in Fig. 17. By a back-off of more than 6 dB, the requirements of the QAM16 modulation are satisfied. Tab. 2 shows linear state of the art power amplifiers with enhanced bandwidth. The bandwidth enhancements are implemented with transformers or other methods. Only the work of [17] realizes also an auto-transformer but with balun. In [14] the dual band impedance network for wideband behavior is described. Compared with the other works this power amplifier exhibits a very high relative bandwidth of 120 % at high output power and efficiency.

Wideband Power Amplifier with Auto-Transformer

| 73

Table 2: State of the art linear power amplifier with enhanced bandwidth. Ref

Tech

fc MHz

Rel. BW %

GT dB

Pout dBm

η %

ITR F. Ω/Ω int.

Size Comment mm2

–

n. s. (Si)

915

22⋆

24

24

44

1.8⋆ No

[4]

90 nm CMOS

930

48 + +

28

27.7

19⋆

4 yes

3.3 Integrated, distributed transformer with 4-way-power combiner

[1]

90 nm CMOS

5800

n. s.

n. s.

20.5

16

4 yes

0.81 Integrated, distributed transformer with 4-way-power combiner

[2]

InGaP GaAs HBT

2400

20+ 29.3

28.3

40

4 yes

1.17 Integrated conventional transformer

[17] SiGe BiCMOS

3500

n. s. 13.0

24.6

33

4 yes

1.65 Integrated auto-transformer

[20] SiGe BiCMOS

2000

40 23.8

26.2

34

1 yes

1.0 Stacked power amplifier

[14] SiGe BiCMOS

700

100 22.1

23.6

33

3.8 yes

1.6 PA-LC: Dual band impedance network

This SiGe BiCMOS

650

120 32.3

24.4

38

3.1 yes

1.1 Integrated auto-transformer

n. s. Commercial, conventional power amplifier

fc : center frequency Pout = Pout,−1 dB : output power at 1 dB compression point ITR: impedance transformation ratio Rel. BW: relative bandwidth η: efficiency at 1 dB compression point +: determined via small signal measurement F. int.: Fully integrated GT : Transducer Gain ++: determined by the saturated output power instead of the output power in the 1 dB compression point ⋆ : estimated

5 Conclusion First investigations show that a reactive impedance transformation network consisting of L and C show a maximum efficiency below 80 %. On the other hand an auto-

74 | E. Sobotta et al. transformer is proved to be a very good possibility to achieve a very efficient and broadband impedance transformation. For sub- and low-GHz range, it exhibits significantly lower losses, parasitic, and area consumption than a conventional transformer. The arising challenges can be faced by the demonstrated theory allowing a structured de-

sign process. Thus, fully integrated power amplifiers based on this approach can be built and be proved by the design of a power and area efficient PA. To gain the full

bandwidth the use of a totem-pole driver is proposed. All characteristics of the PA were

carefully measured including compression point measurement for several frequencies and distortion measurements proving a good linearity and efficiency.

Bibliography [1]

P. Haldi, D. Chowdhury, P. Reynaert, L. Gang and A. M. Niknejad. A 5.8 GHz 1 V Linear Power Amplifier Using a Novel On-Chip Transformer Power Combiner in Standard 90 nm CMOS. IEEE Journal of Solid-State Circuits, 43:1054–1063, 2008. [2] S. Hoseok, P. Changkun, L. D. Ho, P. Min and H. Songcheol. A 2.4-GHz HBT power amplifier using an on-chip transformer as an output matching network. IEEE MTT-S Int. Microwave Symposium Digest, 875–878, 2008. [3] D. Gruner and G. Boeck. 6 GHz SiGe power amplifier with on-chip transformer combining. SBMO/IEEE MTT-S Int. Conf. on Microwave and Optoelectronics (IMOC), 790–794, 2007. [4] B. Francois and P. Reynaert. A Fully Integrated Watt-Level Linear 900-MHz CMOS RF Power Amplifier for LTE-Applications. IEEE Trans. on Microwave Theory and Techniques, 60:1878–1885, 2012. [5] H. Ahn, S. Baek, H. Ryu, I. Nam and O. Lee. A Highly Efficient WLAN CMOS PA with Two-Winding and Single-Winding Combined Transformer. IEEE Radio Frequency Integrated Circuits Symposium (RFIC), 310–313, 2016. [6] M. Yang, J. Xia and A. Zhu. A 1.8-2.3 GHz Broadband Doherty Power Amplifier with a Minimized Impedance Transformation Ratio. Asia-Pacific Microwave Conference (APMC), 1–3, 2015. [7] W. Simburger, H.-D. Wohlmuth, P. Weger and A. Heinz. A Monolithic Transformer Coupled 5-W Silicon Power Amplifier with 59 % PAE at 0.9 GHz. IEEE Journal of Solid-State Circuits, 34:1881–1892, 1999. [8] C. Park, J. Han, H. Kim and S. Hong. A 1.8-GHz CMOS Power Amplifier Using a Dual-Primary Transformer With Improved Efficiency in the Low Power Region. IEEE Trans. on Microwave Theory and Techniques, 56:782–792, 2008. [9] I. Aoki, S. Kee, D. Rutledge and A. Hajimiri. Distributed Active Transformer-A new power-combining and impedance-transformation technique. IEEE Trans. on Microwave Theory and Techniques, 50:316–331, 2002. [10] I. Aoki, S. D. Kee, D. B. Rutledge and A. Hajimiri. Fully Integrated CMOS Power Amplifier Design Using the Distributed Active-Transformer Architecture. IEEE Journal of Solid-State Circuits, 37:371–383, 2002. [11] O. Lee, K. H. An, C. Lee and J. Laskar. A Parallel-Segmented Monolithic Step-Up Transformer. IEEE Microwave and Wireless Components Letters, 21:468–470, 2011. [12] E. Sobotta, R.Wolf and F.Ellinger. Auto-Transformer-Based Power Amplifier with Totem-Pole Driver. 14th Int. Multi-Conf. on Systems, Signals & Devices (SSD), 2017.

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[13] H. Ahn, S.-E. Choi, H. Ryu, S. Baek, I. Nam and O. Lee. 2.3-GHz HBT Power Amplifier With Parallel-Segmented On-Chip Autotransformer. IEEE Microwave and Wireless Components Letters, 27(12):1–3, 2017. [14] R. Wolf, N. Joram, S. Schumann and F. Ellinger. Dual-band impedance transformation networks for integrated power amplifiers. Int. Journal of Microwave and Wireless Technologie, 8(1):1–7, 2014. [15] S. C. Cripps. RF Power Amplifiers for Wireless Communications. 2nd ed. Norwood: Artech House, 2006. [16] S. Hauptmann and F. Ellinger. Optimized Transistor Output Power – Extending Cripps’ Loadline Method to Cascode Stages. IEEE Trans. on Microwave Theory and Technique, 59:2011–2023, 2011. [17] V. A. Solomko and P. Weger. A Fully Integrated 3.3–3.8-GHz Power Amplifier With Autotransformer Balun. IEEE Trans. on Microwave Theory and Techniques, 57:2160–2172, 2009. [18] C. Knochenhauer. Analog Frontends for Optical Communications up to 80 GBit/s. 1st. ed. Göttingen: Optimus Verlag, 2011. [19] M. Wickert, R. Wolf and F. Ellinger. Analysis of totem-pole drivers in SiGe for RF and wideband applications. Int. Journal of Microwave and Wireless Technologies, 4(2):1–9, 2011. [20] D. Fritsche, R. Wolf and F. Ellinger. Analysis and Design of a Stacked Power Amplifier With Very High Bandwidth. IEEE Trans. on Microwave Theory and Techniques, 60:3223–3231, 2012.

Biographies Elena Sobotta was born in Waiblingen, Germany, in 1987. She received the Bachelor degree (B. Eng.) in electrical engineering in cooperation with Agilent Technologies from the Duale Hochschule Baden-Württemberg in Stuttgart, Germany and her Master degree (M. Sc.) from the Technische Universität Dresden, Germany in 2009 and in 2013, respectively. In 2010 she has been Product Manager for RF inductors and LTCC components at Würth Elektronik eiSos in Waldenburg, Germany. Since 2013, she is working toward the Ph. D. degree at the Technische Universität Dresden, Germany. Her main research interests are analog circuits especially the design of multi-standard transceivers. Robert Wolf was born in Karl-Marx-Stadt (nowadays called Chemnitz), Germany, in 1984. He received the Dipl. Ing. degree in electrical engineering and Ph. D. degree from the Technische Universität Dresden (TUD), Dresden, Germany, in 2009 and in 2016, respectively.. His main research interests include system analysis and the design of integrated control systems for efficiency enhancement of RF power amplifiers.

76 | E. Sobotta et al.

Frank Ellinger was born in Friedrichshafen, Germany, in 1972. He received the Diploma degree in electrical engineering from the University of Ulm, Ulm, Germany, in 1996, the MBA and Ph. D. degree in electrical engineering, and Habilitation degree from ETH Zürich (ETHZ), Zürich, Switzerland, in 2001 and 2004, respectively. Since August 2006, he has been a Full Professor and Head of the Chair for Circuit Design and Network Theory, Technische Universität Dresden (TUD), Dresden, Germany. From 2001 to 2006, he was Head of the RFIC Design Group, Electronics Laboratory and Project Leader of the IBM/ETHZ Competence Center for Advanced Silicon Electronics hosted at IBM Research, Rüschlikon, Switzerland. He authored the lecture book Radio Frequency Integrated Circuits and Technologies (Springer, 2008). Prof. Ellinger is an elected IEEE Microwave Theory and Techniques Society (MMT-S) Distinguished Microwave Lecturer (2009–2011). He was the recipient of several awards including the IEEE Outstanding Young Engineer Award, the ETH Medal, the Denzler Award, Rhode & Schwarz/Agilent/ Gerotron EEEf-COM Innovation Award (twice), and a Young Ph. D. Award of ETHZ.

Ghada Ben Salah, Karim Abbes, Chokri Abdelmoula, and Mohamed Masmoudi

Obstructive Sleep Apnea treatment Methods: A Comparative Study Abstract: This paper deals with Obstructive Sleep Apnea (OSA) which is a common disease characterized by partial or complete interruption of airflow during sleep owing to upper airway occlusion. Untreated OSA contributes to serious comorbidities; thus they are promising invasive and non-invasive treatments. Continuous Positive Airway Pressure (CPAP) is the most common and effective treatment for OSA, however it was rejected by patients after a short period of time because of its discomfort. Therefore, an alternative therapy for this population was required. Functional electrical stimulation FES of Hypoglossal nerve (HGN) has been investigated by multiple groups. Here we have compared between the mechanisms and clinical studies of published implants. A new method for detecting the onset of OSA based on esophageal pressure Pes parameter was demonstrated (threshold −13.5 cm H2 O). According to the stimulation of HGN, it necessitated values range between 33–38 Hz for decline pharyngeal narrowing. Keywords: Obstructive Sleep Apnea (OSA), Continuous Positive Airway Pressure (CPAP) Functional Electrical Stimulation (FES), Hypoglossal Nerve (HGN), Esophageal Pressure (Pes), Implantable Pulse Generator (IPG)

1 Introduction Sleep apnea is a serious sleep disorder that occurs when a person’s breathing is interrupted during sleep. There are two types of Sleep Apnea Syndrome (SAS): Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA). CSA was a much rarer type of SAS; Scientists estimated that less than 20 % of all patients are suffering from this disease [1]. It could appear if the brain failed to send signals to the respiratory muscles to initiate breathing. This was caused by neurological disorder such as a Cerebrovascular accident (CVA) and congestive heart failure (CHF).

Acknowledgement: The authors wish to thank Prof Mohamed Abdelmoula Chief of the department division of surgery Maxillo-facial CHU HABIB BOURGUIBA-Sfax and Dr Mohamed Turki pneumonologist, Founding President of the Tunisian Society of Sleep Medicine for their guidance and discussions. Ghada Ben Salah, Karim Abbes, Chokri Abdelmoula, Mohamed Masmoudi, METS Research Group, National Engineers School of Sfax, Sfax, Tunisia, e-mails: [email protected], [email protected], [email protected], [email protected] De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 77–96. https://doi.org/10.1515/9783110592566-005

78 | G. Ben Salah et al. However OSA was the most frequent type of the sleep apnea, making up 84 % of the syndrome diagnoses [2]. According to the World Health Organization, it affected over 100 million adults worldwide [3]. This study interested with the OSA. It was characterized by the upper airway obstruction that happened when the soft tissue in the back of the throat collapses during sleep. It was delineated by repetitive episodes of discontinued breathing. These episodes are called “Apneas” which lasted approximately 10 seconds. The severity of this disease was measured by the Apnea/Hypopnea Index (also called IAH) which will be evaluated through a sleep test. This index represented the number of periods of apnea or hypopnea per hour of sleep [4]. Mild sleep apnea has been defined as an AHI of 5 to 15 events per hour, moderate sleep apnea as an AHI of more than 15 to 30 events per hour, and severe as an AHI of more than 30 events per hour. There are various risk factors favoring the occurrence of OSA during the last 30 years. Obesity was the most important risk factor since it increased the risk of being apneas around 7 times [5], its role in the pharyngeal obstruction has been reported to various potential mechanisms such as changing in upper airway morphology and instability between respiratory system functions and central respiratory control which reduced the pulmonary functional residual capacity (FRC) associated with high oxygen demand [6]. Neck circumference of more than 43.2 cm (17 inches) in men and 40.6 cm (16 inches) in women was considered as craniofacial facial factor which increased the risk of OSA [7]. Enlarged tonsils and adenoids could be another risk factor [8]. Regarding age, people over 65 years of age were threatened by this sleep disorder 2 or 3 times more than young population [9]. Moreover the hormonal changes related to sex influenced the development of the mucous membrane and the pharyngeal muscles. The muscle response to testosterone secretion was much greater than that of estrogen. Thus the incidence of sleep apnea was generally recognized to be higher in men than in women [10]. Evidence was accumulating that the excess of alcohol consumption and smoking might be a significant risk factor for OSA, as well as people with family history (genetic factor) [11]. OSA could lead to serious complications, ranging from excessive daytime sleepiness (EDS) to raised risk of death. It had a strong correlation with illness, mainly related to the heart and circulation such as High Blood Pressure, Stroke, Arrhythmia and Congestive Heart Failure, etc. [12]. OSA caused not only physical disorders but also physiological and mental troubles. In fact the risk for short term and long term memory loss increases with rising severity of sleep apnea. Also OSA might be responsible for depressive symptoms. The prevalence of depression is higher in patients with OSA as compared to the general population (800 out of 100,000 individuals) [13]. This paper is organized as follows. Section 2 presents the OSA treatments; section 3 describes the physiology and neuro-anatomy of the tongue; section 4 explains the functional electrical stimulation (FES) of the hypoglossal nerve. Section 5 presents the comparative study of implantable devices. The new approach methodology is shown in section 6 for remedying the weaknesses of existing solutions. In section 7

Obstructive Sleep Apnea treatment Methods: A Comparative Study | 79

we present the stimulations requirements. Conclusions and future works are presented in section 8.

2 Obstructive Sleep Apnea Treatments Many promising treatments for OSA which depended in part on the severity of the pathology have been used. They could be divided into two major categories, invasive and non-invasive

2.1 Non-Invasive Treatment 2.1.1 Lifestyle Changes 2.1.1.1 Positional Therapy Body position could affect the severity of OSA with at least twice as patients who laid on their back as in those who sleeped on their side. This might be due to the effects of gravity, which brought about the throat to collapse when a person sleeper on his back. To avoid this problem, the patient should try rolling over onto the side. For patients with mild sleep apnea, they could use a special pillow that helped to stretch the neck. 2.1.1.2 Weight Loss Overweight was a purely anatomical factor in OSA. People who had high Body Mass Index BMI should try a weight-reducing program in order to increase their upper airway’s diameter. So, their sleep will be improved and automatically the daytime sleepiness will be reduced. 2.1.1.3 Smoking Alcohol and Drugs Patients who suffer from OSA should undergo lifestyle changes to reduce the IAH. Among these changes: Quitting smoking, abstaining from alcohol within 4 hours before sleep and avoiding sedatives and sleeping medications.

2.1.2 Dental Devices Oral appliances or dental devices were recommended as an alternative treatment for patients with mild to moderate OSA. Mandibles advancement device (MAD) or also called Jaw advancement device (JAD), which was similar to a sport mouth keeper, forced lower jaw forward and down slightly in order to maintain the upper airway

80 | G. Ben Salah et al. open. This type of dental device was preferred in mild apneic snorers rather than moderate apneas snorers. On the other hand, Tongue Retraining Device (TRD) provided another therapeutic option by holding the tongue in place to prevent the airway from collapsing [14]. Side effects related to dental devises included pain, tooth discomfort and excessive salivation. Over the long term they led to changing in the position of the teeth or jaw.

2.1.3 Continuous Positive Airway Pressure CPAP Continuous Positive Airway Pressure (CPAP) presented the most adequate therapy for moderate to severe apneic snorers.. It was a face or nasal mask worn by the subject at least 6 to 7 hours. The mask, connected to a pump, delivered a steady stream of air into the nasal passages in order to prevent the tissues from collapsing during sleep. The standard CPAP supplied a fixed, constant air flow. However, there are other modes of performance. Bi-level positive airway pressure (BPAP) systems provided 2 different pressures, an elevated one for inhalation and a lowered one for exhalation. BPAP was associated with patients who suffer from significant obesity, respiratory insufficiency and gastric perturbations [15]. Auto titrating positive airway pressure (APAP) systems automatically reacted to variations in the sleeper’s breathing patterns by adapting and changing the air pressure flow. APAP allowed saving technician time compared to the manual titration [16]. Studies had shown that CPAP effectively treated OSA by reducing AHI at least 50 % [17]. However it required a high flow in order to obtain the positive airway pressure, this flow could impose a very hard workload on the nasal mucosa which caused the breakdown of the mucosal capacity. In this case many problems occurred like sneezing attacks, aqueous rhinitis and mucosal congestion. The long term CPAP use could have other negative effects related to skin problems as irritation of the eyes and the occurrence of scars and wounds on the patient’s face. That’s why 30–50 % of patients with severe sleep apnea who used CPAP treatment, abandoned it in the first year of therapy [18].

2.2 Invasive Treatment 2.2.1 Uvulopalatopharyngoplasty UPPP Uvulopalatopharyngoplasty UPPP was a surgical procedure performed for patients with OSA. This sleep surgery increased the width of the airway by removing soft tissue on the back of the throat, including one or more of the following: the uvula, soft palate, tonsils, adenoids, and/or pharynx. The aim of this procedure was stopping part of the muscle action in order to enhance the capacity of the upper airway to remain open. Success rates demonstrated that despite UPPP initial success, it had restricted

Obstructive Sleep Apnea treatment Methods: A Comparative Study | 81

effectiveness in healing OSA. In fact, the apnea hypopnea index (AHI) deteriorated with time from 50 % to a final absolute value less than 20 %. Studies accomplished by Sher and colleagues [19] in 1996 reported data announcing that this sleep surgery had only a 40 % success rate in reaching cure in all subjects used UPPP treatment. Moreover, analysis conducted by Senior and colleagues [20] not only confirmed this percentage, but actually described patients who worsened over the long term. While medical studies proposed that surgery is best adapted to patients with abnormalities in the soft palate, results were poor if the disease implied other areas or the full palate. UPPP not only was considered among the most painful treatment, but also had relatively serious complications. It contributed impaired function in the soft palate and muscles of the throat (called velopharyngeal insufficiency), which could make it hard to maintain liquids out of the airway. Also, it led to swallowing problems because of regurgitation of fluids through the nose or mouth and the presence of mucus in the throat. In general, UPPP was a therapy which took several weeks to achieve recovery [21].

2.2.2 Laser-Assisted Uvulopalatoplasty LAUP Laser-Assisted Uvulopalatoplasty LAUP presented a variation on UPPP in which a laser was effected to eliminate parts or all of the uvula at the back of the mouth. The removal of tissue in LAUP surgery was less than UPPP. The effectiveness of LAUP on OSA was controversial. In terms of objective results, the postoperative apnea hypopnea index (AHI) was distinctly improved in some studies from 29 to 19 [22] or 25 to 15,3 [23]. Others found less improvement as Ferguson who demonstrated that the shortterm cure rate varied from 24 to 48.4 % [24]. This surgery caused potentially serious problems such as pain, throat dryness in 16 to 42 % of the cases and a persistent pharyngeal globus sensation in 10 to 25 % of cases [25].

2.2.3 Radiofrequency Ablation RFA The radiofrequency corresponded to the emission of radio waves with a frequency varying from 3 Hz to 30 Ghz. This wide band of waves was subdivided into subcategories. The radiofrequency ablation RFA was developed from 1996 in its application on the soft palate. The RFA consisted of emitting an electrical current, its wavelength (low frequencies, medium frequencies and high frequencies) changed according to the generators used and the desired effect. The lesion size was inversely proportional to the length of the wave [26].

82 | G. Ben Salah et al. Few studies had been published the effectiveness of the RFA in the therapy of OSA. Brown et al. reported a relative ineffectiveness of 2 to 3 radiofrequency sessions for patients with mild to severe OSA since IAH decreased significantly from 31.2 to 25.3. Only 2 out of 12 patients had IAH reduced more than 50 % with an index lower than 20 [27]. For patients with Mild OSA, Blumen et al. showed a significant decrease in HAI from 19 to 9.8 with 64 % of patients with an AHI less than 10 [28]. RFA was not a painful procedure compared to UPPP and LAUP [29]; however it engendered the appearance of an edema. Its volume varied in accordance with the level of energy delivered from the generator used. In the long term, either erosion or ulceration may appear with maximum velar perforation. The frequency of lesions occurrence varied between 0 to 50 % [30].

2.2.4 Cautery Assisted Palatal Stiffening Operation CAPSO Procedure Cautery Assisted Palatal Stiffening Operation CAPSO was a recently procedure which was based on midline strip of soft palate mucosa removal. 206 patients underwent CAPSO over a 19-month period; the success rate was initially 92 % and decreased to 77 % after 12 months then to 68 % at the end of the trial [31]. As complications, CAPSO was very painful technique which requires hospitalization. Furthermore the taste sensation altered. These non-invasive and invasive modes of treatment had been limited in their efficiency. Therefore there is a need for developing an alternative therapy to OSA treatment. Electrical stimulation of the hypoglossal nerve (XII) has been investigated by multiple groups as an attractive method of therapy to prevent the collapse of the airway. Thus we need to know the physiology and the neuro anatomy of the human tongue in order to appreciate the mechanism of such therapy.

3 Physiology and Neuroanatomy of the Tongue The human tongue is an interesting muscular organ in the mouth that occupies most of the oral cavity and oropharynx. It is vital for chewing and swallowing as well as speaking. The tongue has 4 intrinsic and 4 extrinsic muscles. All the muscles are pair except the superior longitudinal muscle. The four intrinsic muscles change the tongue’s shape which ensures the speech function. However the four other extrinsic muscles alter the tongue’s position. Their principal functions are specified in Tab. 1. The hypoglossal nerve is the twelfth and last cranial nerve and provides the tongue with motor control. It innervates all the extrinsic and intrinsic muscles of the tongue, except for the palatoglossus muscle which is innervated by the vagus nerve as shown in Fig. 1 [32].

Obstructive Sleep Apnea treatment Methods: A Comparative Study | 83 Table 1: Musculature of the tongue. Muscle

Type

Role

Genioglossus Hyoglossus Styloglossus Palatoglossus Superior longitudinal Inferior longitudinal Transverse Vertical

Extrinsic Extrinsic Extrinsic Extrinsic Intrinsic Intrinsic Intrinsic Intrinsic

Depresses and protrudes tongue Depresses and retracts tongue Retracts tongue Elevates posterior tongue Elevates tip and sides of tongue; shortens tongue Curls tip inferiorly; shortens tongue Narrows and lengthens tongue Flattens and broadens tongue

Figure 1: Hypoglossal nerve and the innervation of the intrinsic and extrinsic muscles of the tongue. Red arrows show retrusor muscles, and green arrow shows protrusor muscle.

The hypoglossal nerve or the XIIth cranial nerve is mainly a pure motor nerve. If it was impacted by infirmity or feebleness, the upper airway lost its lingual and pharyngeal tone. The pharyngeal cross-sectional diameter is closed more than 10 s and the rate of oxygen fall in the blood. So this is the case of OSA disease [33].

4 Functional Electrical Stimulation of the Hypoglossal Nerve Functional Electric stimulation FES started at the beginning of the 18th century with the discovery of muscular contraction thanks to an electric current in the famous experiment of the stimulation of the frogs’ legs muscles [34]. This discovery opened the door of new researches for therapeutic objectives. Beyond the technical advances and

84 | G. Ben Salah et al. electronic miniaturization, FES appeared today as an extremely promising research focus for solving many pathologies related to nervous system deficiencies. The fields of applications of this technique were broader including: Parkinson’s disease, paraplegia, quadriplegia, hemiplegic, deep deafness, etc. FES consisted in applying an electric pulse current or voltage which allowed to inhibit, modulate or provoke a nervous message to the nerve structures involved (nerves, muscle motor plate, neuronal group...). For example, in the case of Parkinson’s disease Deep Brain Stimulation DBS was used to inactivate nerve messages responsible for uncontrolled tremors without destroying the brain. In this paper we are interested to the Hypoglossal nerve stimulation HGN in order to elevating the tongue and prevent the collapse of the upper airway for patients who suffer from OSA.

4.1 Animal Studies Miki et al. carried out the first animal experiments in this field to investigate the effects of electrical stimulation of the genioglossus on upper airway resistance in anesthetized dogs. As a result a reduction in upper airway resistance was observed with progressively increasing stimulation frequencies up to 50 Hz [35]. Schwartz et al. conducted the effect of bilateral supramaximal hypoglossal nerve (HGN) stimulation on upper airway mechanics. In this case, the maximal inspiratory airflow (VImax) increased gradually with increasing stimulation frequency. The improvement in VImax could be assigned to lower upper airway collapsibility [36]. Eisele et al. examined how upper airway mechanics were modified by differential electrode placement along the HGN. Results showed that any decrease in upper airway collapsibility by HGN stimulation was dependent on the activation of the genioglossus and electrode placement on the proximal or distal segment of the HGN [37]. Bishara et al. approved the importance of electrode placement and stimulation of specific upper airway muscles in anesthetized dogs. In fact Selective stimulation of the genioglossus with intramuscular fine wire electrodes was more efficient in decreasing upper airway resistance and eliminating upper airway obstruction than stimulation of other upper airway muscles [38]. Bailey et al. have analyzed selective HGN versus whole HGN stimulation in anesthetized rats to examine the differential effects of activation of protrusor muscles compared to coactivation of protrusor and retrusor muscles, respectively [39]. Yoo et al. compared the influence of selective and non selective HGN stimulation in anesthetized beagles. During inspiration, whole nerve stimulation and selective stimulation with co activation of the genioglossus caused an important improvement of airway collapsibility. During expiration, whole nerve stimulation resulted a considerably greater increase in upper airway caliber than selective stimulation of the genioglossus muscle. As a conclusion both nonselective HGN stimulation and selective stimulation with co activation of tongue protrusor muscle (genioglossus) ameliorate upper airway stability in beagles [40].

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Animal studies performed in dogs, rats and beagles demonstrated that electrical Stimulation of the Hypoglossal Nerve (HGNS) or the genioglossus muscle contributed the improvement upper airway collapsibility.

4.2 Human Experiences Guilleminault et al. made the first trials to ameliorate the upper airway patency in humans by transcutaneous sub mental and intraoral electrical stimulation of upper airway muscles [41]. However the results were regarded worthless. About 10 years later, Miki et al. reported their experience with genioglossus muscular stimulation. A decrease in AHI index from 53.8 ± 7.0 to 27.3 ± 5.7 was observed [42]. Smith et al. also aborted to acquire a considerable improvement in airway patency when stimulation was effected through the submandibular region. On the other hand, trials acquired tongue protrusion and contra lateral deviation (coherent with genioglossus stimulation) during wakefulness when fine wire electrodes were infused into the genioglossus [43]. Animal and human experiences had proved that electrical stimulation is able to enlarge the upper airway and ameliorate the breathing disorders. These favorable results impelled the development of implantable hypoglossal nerve stimulating systems which were described in the following section.

5 Comparative Study of Implantable Devices 5.1 Inspire System The Inspire I system from Medtronic (Minneapolis, Minn., USA) consisted of a pressure sensor, a programmable implanted pulse generator IPG and stimulating electrode as mentioned in Fig. 2 [44]. The respiratory pressure sensor, that was fixed through a hole drilled in the middle of the manubrium sternale, was used to detect intrathoracic inspiratory pressure. It was connected with IPG by the means of subcutaneous lead. This IPG was implanted in the right upper chest of the subject (in the region of the pectoralis muscles). From the pressure sensor input, the IPG forecasted the beginning of inspiration, transferring stimulation pulses between the end of expiration and the onset of the next expiratory phase of each respiratory cycle. These Stimulation pulses were sent to the HGN through molded silicone rubber electrode cuff which prohibited unwanted current spread. The electrode cuff was positioned unilaterally on a peripheral branch of HGN that innervated the genioglossus muscle and connected via a stimulation lead with the programmable IPG. The investigators used an external programming device in order to adjust stimulation parameters (stimulation amplitude, pulse width, stimulation frequency) and then defining the optimal stimulus param-

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Figure 2: Schematic representation of the inspire I for hypoglossal nerve stimulation in OSA patients.

eters. The patient received a remote control unit that let him/her active the system during sleep. The initial clinical results was a 6 months study performed in 8 patients, beneficial results were obtained in 7 subjects whom AHI decreased from 38.9 to 10. Stimulation wasn’t painful and doesn’t contribute arousal from sleep. This first promising clinical trial was followed by a decade without any apparent further development. However the trials on inspire continued to obtain Inspire II the second generation of a system previously investigated. The Inspire II trial was announced after a short period. It was a 6 months study divided into 2 parts; the criteria of the first part were wider in term of inclusion and exclusion than the criteria of the second part. 20 implanted patients had to have an AHI more than 25 events per hour in part 1; 14 patients indicated no reduction in the AHI whereas 6 patients showed more than 50 % decrease in AHI and a final index lower than 20 events per hour after the 6 months of experiments. On the other hand, 9 implanted patients had to have an AHI between 20 and 50 events per hour in part 2.8 were evaluated at 6 months and 7 indicated the expected outcome results [45]. The Inspire (STAR trial) study was completed in March 2014 [46]. It included 126 patients who had AHI between 20 and 50 events per hour. After 1 year the AHI decreased after from 32 to 15 events per hour and from 29 to 9 events per hour. 66 % of the participant achieved a final AHI 50 % lower than their initial value and with absolute values below 20 events per hour.

5.2 Apnex System Apnex and Inspire I were two comparable medical systems which had the same principles. In fact, Apnex system included a double impedance sensor, an IPG and a

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Figure 3: Schematic representation of the Apnex implant.

cuff of electrode as shown in Fig. 3. The sensor situated over the lower ribs of both hemithoraces was responsible of the identification of subject’s inspiration. The cuff of electrode confined the electric pulses delivered by the IPG and emitted to the medial branch of HGN in order to perform unilateral aspiratory stimulation. The Apnex system clinical trial was realized in Australia [47]. The investigators selected 37 patients, 21 of them were implanted (14 males) and followed up for 6 months. These subjects had to have a specific degree of severity (between 20 and 100 apneahypopnea events per hour of sleep), and an age between 21 and 70 years. At 3 to 6 months stimulation, effectiveness of experiments was observed because of the decrease of AHI more than 50 % and the improvement in sleep quality. Later the large scale phase clinical trial demonstrated the failure of results (The AHI reduced from 45 to 25) which caused the breaking of the Apnex system.

5.3 Imthera System The aura6000TM system (ImThera Medical Inc., San Diego, CA, USA) functioned like a pacemaker for the tongue, cycling through stimulating different muscles of the tongue to enlarge upper airway diameter during sleep. The system included of 2 implantable components, an Implantable Pulse Generator IPG, positioned under the skin near the collarbone and a multi-contact electrode having an array of six contacts placed around the main trunk of HGN, connected to the IPG via a subcutaneously tunneled lead wire as illustrated in 4. The IPG was a small implanted stimulator containing the battery and stimulation system (Hardware and software). The IPG battery had to be recharged daily by an external Remote Control Charger RCC and charging coil placed over the stimulator with a pair of magnets. The same RCC was used to turn the device on and off during sleep. The Imthera system was characterized by the absence of a sensing lead to control respiration or intrathoracic movements. Eliminating sensing circuitry sim-

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Figure 4: (a) Schematic representation of Imthera system. (b) Chest radiograph of the Imthera implant.

plified the surgical procedure and decreased the power consumption. Furthermore, the stimulation was delivered during both inspiration and expiration (continue). Since the cuff of electrode divides the hypoglossal nerve into six regions, the HGN were activated independently. This method is less painful and tiring for patients. The duty cycle of Imthera system depended on the number of active contacts. With a minimum of 2 active contacts, the duty cycle was 50 % as the system stimulates first one contact, the muscles activated by the second recover before re-activation. This value fell to 33 % If 3 contacts are acceptable then the duty cycle reduced to 25 % in case of 4 active contacts [48]. The initial clinical results was a 1 year study performed in 14 patients, one of them was not implanted because of technical defects of the implant. These patients had to be aged between 25 to 70 years and the AHI more than 20 events per hour. The AHI fell by more than 50 % (from 45.2 to 21), with 10 patients but, 3 patients did not reach this point.

5.4 Nyxoah System Nyxoah GenioTM System developed by a Belgian-Israeli firm, has developed a medical system for people with moderate to severe OSA. It concluded a neurostimulator implanted the chin using a minimally surgery procedure and a disposable patch positioned every night by the patient on the skin under the chin as mentioned in Fig. 5. This patch was connected to a small electronic chip that had its own battery and wirelessly activated the implanted neurostimulator. It sent electromagnetic pulses which were converted into current by the micro-stimulator [49]. Subjects will be consented, en-

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Figure 5: Schematic representation of Nyxoah Genio TM system.

rolled and eligibility criteria checked. These criteria included the age (21 to 75 Years), Obstructive apnoea-hypopnea index AHI (20–60 events/hour) and Body mass index (≤ 32 kg/m2 ). 1 month after implantation, the system will be activated and the subject will be evaluated at 1 month, 2, 3, 4 and 6 month(s) after implantation. However the initial clinical trials are not published yet. It is expected to be launched in the UK in September 2018. Table 2 presents the comparative study between all existing solutions nowadays for the pathology of the OSA.

6 New Approach Methodology The goal of this study was to investigate the limitations of published systems for remedying its weakness in the treatment of OSA. All the devices described in section 5 contained sensors based on the measuring of the intrathoracic pressure in order to detect the beginning of an apnea event because of its important role in controlling respiratory efforts. Analysis confirmed that increased negative measurement of esophageal pressure (Pes) was a definite method for the detection of OSA. During apnea event, the decline in pharyngeal muscular activity accelerated the upper airway occlusion and led to the increase of negative (Pes) values. The threshold-value of esophageal pressure Pes was estimated to −13.5 cmH2 O. The patients with Pes values negatively above threshold were considered apneic snorers. The trace below presented the (Pes) raise in 4 apnea events as illustrated in Fig. 6. The highest peak of negative (Pes) was expressed as (Pes) Nadir (cmH2 O). It was identified from zero to the maximum value of (Pes). (Pes) Duration means the time from the minimal negative pressure to the next Min.

90 | G. Ben Salah et al. Table 2: Comparative study between published implants. Common points Different points

Same act: Protractoring the tongue by unilateral stimulation of hypoglossan nerve (in InspireI, Inspire II, Inspire Star Trial, Apnex, Imthera, Nyxoah) Medical implant classification Totally implantable system Partially implantable system InspireI InspireII, Apnex Imthera Nyxoah Inspire (Star Trial) Number of electrodes 1 6 InspireI InspireII, Apnex Nyxoah Imthera Inspire (Star Trial) Detection of OSA System with sensor System without sensor InspireI InspireII, Apnex Nyxoah Imthera Inspire (Star Trial) Type of stimulation Discontinue Continue (only at inspiration) (inspiration + expiration) InspireI InspireII, Apnex Nyxoah Imthera Inspire (Star Trial)

Figure 6: Parameters of the esophageal pressure (Pes).

The values of AHI and Pes were correlated significantly as demonstrated in Tab. 3 which shows a comparative study of these parameters among [50] Severe, Moderate and Mild OSA. Table 3: Comparison of the pes max among mild, moderate and severe obstructive sleep apnea osa.

Age (Years) AHI Pes Nadir (cmH2 O)

Severe OSA

Moderate OSAS

Mild OSAS

54.8 ± 3.8 71.3 ± 3.7 35.6 ± 6

46.0 ± 4.1 34.4 ± 2.8 31.3 ± 2.7

51.7 ± 3 12.2 ± 0.6 28.4 ± 2.6

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Our new approach consists on the development of a programmable IPG based on the data received from the esophageal pressure sensor to active the implant only at the apnea event (not during the whole sleep). To avoid connecting device to daily rechargeable battery, our system would be powered by an inductive coupled links by the use of power patch.

7 Stimulation Requirements 7.1 Parameters Influencing Stimulation The parameters of the stimulation wave including stimulation frequency, stimulation amplitude, pulse duration had to be well adjusted to avoid the harmful effects on the patient such as arousals and discomfort. 7.1.1 Stimulation Frequency Stimulation frequencies between 50–100 Hz were required to obtain maximal airway diameter opening. At frequencies of 30 Hz or greater, a decrease of pharyngeal occlusion was observed. Schwartz et al. demonstrated that the use of frequencies between 33–38 Hz was sufficient to realize the purpose [51]. 7.1.2 Stimulation Amplitude Guilleminault et al. demonstrated the use of stimulation amplitudes values up to 20 V had no benefits for the treatment of apnea disease and even could harmed the other tongue missions [52]. In this report, for the frequencies range between 33–38 Hz, stimulation amplitude was limited during successive calibration from 2.2 to 3 V. 7.1.3 Pulse Duration An average pulse width range between 0.2–1.0 ms could effect on the patient confort by the fact that a marked painful raise in neuronal tension could be obtained [53]. That’s why human pilot had investigated this issue to publish in 2001 a limited pulse width range between 94.3–110.5 μs. 7.1.4 Pulse Waveform The rectangle pulse waveform had been used as standard shape for neural stimulation. But, some studies demonstrated that non-rectangular waveforms could perform

92 | G. Ben Salah et al. more energy-efficient neural stimulation and also decreased stimulation artifacts (sinusoidal, exponential waveform, etc) [54].

8 Conclusions and Future Works In summary, we have presented the obstructive sleep apnea OSA disease and defined its factors and consequences. Then we have depicted the invasive and the non-invasive treatments by emphasizing on Continuous Positive Pressure CPAP which was considered the most commonly used therapy for patients with moderate to severe OSA. After that, we have shown the importance of Functional Electrical Stimulation FES of Hypoglossal Nerve HGN in elevating the tongue muscles and precluding upper airway occlusion for patients who didn’t tolerate with CPAP. Multiple groups have analyzed this assuring substitute treatment as its efficiency. Therefore, we have done a comparative study between published devices. Later, we have presented a new define method for the detection of the OSA based on esophageal pressure Pes parameter since it reflected genuinely the decrease in pharyngeal neuromuscular activity. Furthermore, the HGN stimulation requirements have been detailed (frequency, amplitude, duration, and waveform). Future work including the design of low voltage programmable Implantable Pulse Generator. It will be activated only if the esophageal pressure Pes overtakes the threshold value (−13.5 cmH2 O) and powered by an inductive coupled links.

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Biographies Ghada Ben Salah was born in Sfax, Tunisia, in 1992. She received her Engineering Diploma in circuits and microelectronic systems in 2015 from the National School of Electronics and Telecommunications of Sfax E.N.ET’Com. She is currently working towards her PhD in Electronic Engineering at National Engineering School of Sfax (E.N.I.S). She is member of Electronics, Micro technology Communication (EMC) from Advanced MicroElectroThermal Systems research unit (METS). Her research interests include data conversion, sensors and medical implants.

Karim Abbes was born in Sfax, Tunisia, in 1979. He received the B. Sc in electrical Engineering from College of Sciences and Techniques, Tunis, Tunisia (ESSTT) in 2003, the M. Sc in System on chip from the National Engineers School of Sfax, Tunisia in 2005 and the Ph. D. degree in Informatics Systems Engineering from ENIS, Tunisia, in 2012. From 2005 to 2009, he was an Associate in the Faculty of Sciences, Sfax, Tunisia. From 2010 to 2012, he was an Associate in Private Polytechnic of Advanced Sciences Institute, Sfax, Tunisia. Actually he is an associate professor in the Faculty of Sciences, Sfax, Tunisia. He is a member of the Laboratory Micro-Electro Thermal Systems. His research interests include data conversion, analog/mixed-signal circuit design and test.

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Chokri Abdelmoula was born in Sfax (Tunisia) in 1963. He graduated in Electrical Engineering 1987, obtained the master (N.T.S.I.D) and then the thesis both in Engineering of Informatics Systems in 2003 and 2009 respectively. In 2014, he obtained the HDR He is now an associate professor in the National School of Electronics and Telecommunications of Sfax “ENET’Com in industrial Computing department at the University of Sfax. Also, he is the Director Internship of the “ENET’Com” School. His research interest includes applications of intelligent methods (neural networks, fuzzy logic, and evolutionary algorithms) to pattern recognition, robotic systems, vision systems, telemedicine and industrial processes. He is associate reviewer of many international scientific journals (“International Journal of Computer Engineering Research”, “Journal of Control Science and Engineering”, “Journal of Electrical and Electronics Engineering Research”, “Information Technology Research Journal”, “Scientific Research and Essays”, etc.). He was the organizing chair of the International Conference on Design Technology & Integrated Systems 2012. He is member of EMC research Group, and a member of METS (Micro Electro Thermal System) Laboratory in National Engineering School of Sfax Dr. ABDELMOULA organised several international Conferences and has served on several technical program committees. Mohamed Masmoudi was born in Sfax, Tunisia, in 1961. He received the Engineer in electrical Engineering degree from the National Engineers School of Sfax, Sfax, Tunisia in 1985 and the PhD degree in Microelectronics from the Laboratory of Computer Sciences, Robotics and Microelectronics of Montpellier, Montpellier, France in 1989. From 1989 to 1994, he was an Associate Professor with the National Engineers School of Monastir, Monastir, Tunisia. Since 1995, he has been with the National Engineers School of Sfax, Sfax, Tunisia, where, since 1999, he has been a Professor engaged in developing Microelectronics in the engineering program of the university, and where he is also the Head of the Laboratory Electronics, Microtechnology and Communication. He is the author and co-author of several papers in the Microelectronic field. He has been a reviewer for several journals. He is the founder of Solid State Circuits Tunisia Chapter Dr. Masmoudi organised several international Conferences and has served on several technical program committees.

Wagah F. Mohamad and Munther N. Al-Tikriti

Investigation of Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors Abstract: Cost consideration of the development of electronic devices is one of prime importance. One simple approach to lower the cost of production of photovoltaic and detectors is by using low cost materials such as amorphous silicon and germanium. These two semiconductors have different optoelectronic properties, such as energy gap, photoconductivity and absorption coefficient. The use of an alloy from the mixing of silicon with certain percentages of germanium would produce photodetectors with improved electronic characteristics and photoconductivity. A number of a-SiGe alloy thin films with different quantities of germanium have been fabricated using thermal vacuum evaporation technique. Conduction mechanism and activation energy of the prepared samples had been calculated and analyzed. The I–V characteristics, the photogenerated current and detectivity of these samples are subjected to measurement and discussion. Hall measurements are also conducted so to calculate the Hall I–V characteristics, Hall mobility, carrier concentration and type identification of the samples. Keywords: Amorphous silicon germanium photodetector, photoconductivity, Hall measurements, detectivity, conduction mechanism, activation energy

1 Introduction The common approach to reduce the cost of photovoltaic and photodetectors systems is through the use of low cost material. Amorphous materials are solids with atomic formation, which is randomly arranged. They tend to lack long term order in their lattice network. Amorphous silicon has favorable features such as wide controllability and could be fabricated as thin films that show good mass reduction [1]. The energy gap of amorphous silicon is between 1.7 to 1.9 eV. It has large photoconductivity and optical absorption coefficient within the visible spectrum [2] but the response seems to be low at high wave length. In general, amorphous germanium has a small optical energy gap between 0.9 to 1 eV so it has low photoconductivity and higher absorption coefficient over wide wave length range [3]. Due to low conductivity of amorphous germanium it would produce inefficient solar cell. On the other hand good photodetectors could be produced from the mixture of amorphous silicon and germanium (aSiGe). To achieve good improvement of electronic properties and photoconductivity Wagah F. Mohamad, Munther N. Al-Tikriti, Faculty of Engineering, Philadelphia University, Amman, Jordan, e-mails: [email protected], [email protected] De Gruyter Oldenbourg, ASSD – Advances in Systems, Signals and Devices, Volume 10, 2019, pp. 97–109. https://doi.org/10.1515/9783110592566-006

98 | W. F. Mohamad and M. N. Al-Tikriti of such thin films a fraction of Ge is added to Si. The use of a semiconductor with smaller band gap improves the short circuit current and reduces the optical energy gap which considerably improves the detectivity of the photodetectors [4]. There are wide ranges of applications for silicon germanium alloy. This includes photodetector, infrared sources and detectors [5]. One of the major applications of a-SiGe in the flat panel display has large substrate area and a large number of pixels which are called photodetector FET [6]. Recently, however, research literature showed that some silicon-germanium photodetectors in the near infrared range had been proposed and integrated for power monitoring and optical interconnection [7]. Bulk photodetector can be classified as the oldest and thoroughly understood generation of silicon optoelectronic components. They are now, commercially produced with operating range of wavelength below 1100 nm in addition to the occurrence of band-to-band absorption [8]. In this paper a number of a-SiGe alloy thin films of different Ge quantities have been fabricated using thermal vacuum evaporation technique. The method of mixing, alloying and crystallization of samples had been explained in reference [4]. Hall measurements are also conducted in order to calculate Hall I–V characteristics, hall mobility, carrier concentration and to identify the type of the prepared thin films. Conduction mechanism and activation energy of the samples have been calculated and discussed. In addition, few samples of a-SiGe(n)/Si(p) photodetector of area 4 cm2 are prepared. I–V characteristic, the photogenerated current and the detectivity of these samples are also measured and discussed.

2 Conduction Mechanism and Activation Energy The conductivity σ of an amorphous semiconductor slab can mathematically be expressed as [9]: σ = σ0 exp(−

Ea ) kT

(1)

where σ0 is a constant, K is the Boltzmann constant, T is the temperature and Ea is the activation energy and can be defined for pure single crystal as: Ea = Ec − EF

(2)

where Ec is the conduction band level and EF is Fermi level. In amorphous semiconductor, there are three mechanisms that describe the conduction current process [10]. At high temperature, the mechanism is through the exited charge carriers in the extended states. While at room temperature, the conduction occurs by tunneling through the localized states that is hopping of electrons near mobility edge (extended

Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 99

states). But at low temperatures, which are less than room temperature, the conduction occurs by hopping the charge carriers near Fermi level. The Poole-Frenkel effect due to the thermal excitation of the electrons over the potential barrier E0 within the band gap of the semiconductor which is field lowered, the conduction current I is given by [11]: 1

I = σ0 E0 exp(

βV 2 ) kT

(3)

where V is the average applied voltage and β is the theoretical Poole-Frenkel coefficient given by: β=√

e3 πε0 εr

(4)

where e is the electronic charge, ε0 is the space permittivity and εr is the relative permittivity of the materials used. The value of β can also be determined experimentally 1 from the slope1 of the linear portion of the plot of ln I against V 2 as shown in: 1

βV 2 ln I = ln(σ0 E0 ) + kT β = slope1 KT

(5)

From equation (1) and because all the measurements are conducted around room temperature, the activation energy is expressed as follows and can be calculated from the slope2 of the ln σ against T1 : Ea KT Ea = −(slope2 )K ln σ = ln σ0 −

(6)

To investigate the conduction mechanism in a-SiGe thin films, ln I is plotted 1 against V 2 as shown in Fig. 1 for samples of different values of the ratio of germanium in the silicon mixture (x). The plots are almost linear at the voltage range from 1 to 5 volts. Having known that εSi ε0 = 1.04 × 10−12 F/cm and εGe ε0 = 1.44 × 10−12 F/cm and using equation (4) and Fig. 1, the theoretical values of β are calculated and displayed in Tab. 1. The similarity between theoretical and experimental value of β means that the conduction mechanism is Poole-Frenkel type and the general equation for the conduction current equation (3) can be used to explain this process. Figure 2 illustrates the variation of natural log of surface conductivity with the reciprocal of the temperature at different biasing voltage for two samples x = 0.2 and x = 0.5. It is evident that there are two values of activation energy. The activation energy is usually high at higher temperature (450 ∘ K) and it is equal to 0.528 eV and

100 | W. F. Mohamad and M. N. Al-Tikriti

1

Figure 1: ln I against V 2 for different values of x.

Table 1: Theoretical and experimental values of β for Si, Ge and SiGe for different values of x. 1

1

β (eV V 2 cm 2 ) Si Ge a-SiGe

x x x x x

= 0.1 = 0.2 = 0.3 = 0.4 = 0.5

2.2143 × 10−4 1.8797 × 10−4 2.0864 × 10−4 2.6226 × 10−4 2.6201 × 10−4 2.4790 × 10−4 2.6899 × 10−4

Theoretical Theoretical Experimental Experimental Experimental Experimental Experimental

Figure 2: Variation of natural logarithm of the conductivity surface with ages for x = 0.2 and x = 0.5.

1 T

at different biasing volt-

0.415 eV for x = 0.2 and x = 0.5, respectively. While, it is 0.23 eV and 0.28 eV respectively at low temperature (320 ∘ K). The maximum variation of Ea is found at low biasing voltage (5 V). Increasing the biasing voltage limits the effect of temperature by controlling the motion of the charge carriers, consequently increasing the conduction current.

Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 101

From the above presentation, it can be concluded that there are two types of dangling bonds residing on Si and Ge atoms and high number of defects in the Ge-rich samples which can merge the sub-gap absorption tail due to strained bonds. Hence the activation energy is linearly depending on germanium quantity and the ratio of SiGe could lower the Fermi level [12].

Figure 3: Variations of ln σ with different annealing temperature and constant biasing voltage (V = 5 V) for sample x = 0.3.

Figure 3 shows variations of ln σ versus T1 of sample x = 0.3 at different annealing temperatures. Calculation of activation energy with different values of x and biasing voltages are summarized in Tab. 2. From Tab. 2, as the temperature changes the density of states (DOS) and the Fermi level position have changed. At low temperature the change of DOS becomes dominant which decreases the activation energy. At high temperature the Fermi level position change will become dominant and that will increase the activation energy. Also it is noticed that the activation energy will be minimum at 600 ∘ C annealing and this could be attributed to the maximum defects located around mid-gap and the narrow band of Fermi-Dirac occupation. Table 2: Calculated values of activation energy with different values of x, Tann and biasing voltages.

x

T (∘ K)

0.2 0.2 0.3 0.3 0.4 0.4 0.5 0.5

320 450 320 450 320 450 320 450

Ea in eV (V = 5 V) Tann (∘ K) 500 600 0.318 0.459 0.304 0.359 0.338 0.424 0.213 0.438

0.167 0.306 0.127 0.235 0.151 0.200 0.134 0.146

700 0.215 0.387 0.273 0.335 0.182 0.162 0.266 0.370

Ea in eV (V = 25 V) Tann (∘ K) 500 600 0.196 0.407 0.297 0.443 0.308 0.537 0.213 0.476

0.226 0.408 0.1521 0.278 0.179 0.211 0.131 0.155

700 0.248 0.337 0.303 0.428 0.183 0.304 0.231 0.376

102 | W. F. Mohamad and M. N. Al-Tikriti

3 Hall Measurements Annealed a-SiGe films are polycrystalline structure, each individual crystallite, generally of a good quality, but are interrupted by numerous grain boundaries. Charges will be trapped at the grain boundaries forming a potential barrier which impede carrier transformation. Hall measurement is a very good tool that can measure the conductivity and identify the type of the semiconductors films. The relationship between Hall coefficient RH and carrier concentration n can be calculated accurately from [13]: RH =

r ne

(7)

where r is the scattering factor which lies between 1 & 2 (close to unity). The relation between Hall mobility μH and film conductivity can be written as: μH = RH σ

(8)

The change in the carrier concentration and/or electron mobility will cause a change in bulk resistivity ρB as: ρB =

1 enμH

(9)

In order to decrease the resistivity, the grain size must be increased. Annealing is an efficient method to increase the grain size. Hall mobility μH , Hall coefficient RH and carrier concentration n are calculated for different values of Ge and listed in Tab. 3. Table 3: Calculated values of Hall mobility μH , Hall coefficient RH and carrier concentration n for different values of x. x

RH (cm/Col)

μH (cm2 /V s)

n (cm−3 )

0 0.1 0.2 0.3

80.30 60.17 45.91 40.10

0.161 × 10−3 0.187 × 10−3 2.296 × 10−3 12.03 × 10−3

77.8b × 1015 103.9 × 1015 136.1 × 1015 154.9 × 1015

From Tab. 3 and Hall measurements, it is clear that all a-SiGe fabricated films are ntype and the electrons are the majority carriers. Also the Hall mobility and carrier concentration increase slowly for small values of x (x ≤ 0.1) and rapidly as x becomes large (x ≥ 0.1).

Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 103

4 Hetero-Junction Characteristic Si1−x Gex (n)/Si(p) hetero-junction of 4 cm2 area are fabricated using thermal evaporation technique with Si is used as a substrate. The deposition rate and substrate temperature are kept constant throughout the fabrication process and equal to 2Å/s and 180 ∘ C respectively. The germanium quantity and SiGe layer thickness are varied in order to vary the junction depth and to determine the best rectifying properties as given in Tab. 4. Table 4: The germanium quantity and SiGe layer with variable thickness. Sample S1 S2 S3 S4

x

Thickness (Å)

Ideality factor

0 0.1 0.1 0.3

4000 2000 4000 2000

10.18 3.43 4.74 1.98

Figure 4 shows the variation of dark current with the biasing voltage. It is clear that increasing x leads to a good diode effect with large forward and small reverse currents. Ideally the value of Ideality factor should be around unity. If the ideality factor > 1, the recombination current will dominate and the forward current will be reduced. It is clear that sample S4 gives the best junction properties.

Figure 4: Variations of dark current with the biasing voltage.

The reverse current of the above samples are calculated against the reverse voltage as shown in Fig. 5. Sample S2 shows the highest dark reverse current as compared to the other samples. Figure 6 gives a plot of C −2 versus reverse and forward voltage (−0.5V < Vbias < 0.5V) for sample S4 . The plot is linear which is indicative of abrupt hetero-junction,

104 | W. F. Mohamad and M. N. Al-Tikriti

Figure 5: The reverse current of all samples against the reverse voltage.

Figure 6: Shows a plot of C −2 versus reverse and forward voltage (−0.5V < Vbias < 0.5V ) for sample S4 .

whose intercept on the voltage axis gives the built in junction potential (VD ). The doping profile of one side can be found from [14]: C2 =

eεn εp NA NC

2(εn NA + εp NC )

(VD − V)

(10)

From equation (10) and Fig. 6, the built in junction potential is equal to 0.45 V and the carrier concentration is 1.55 × 1017 /cm3 .

5 Optoelectronic Measurements In order to calculate the detectivity of the fabricated device, the photogenerated Iph current should be determined. Because Iph is a minority carriers current the junction is reverse biased during measurements. In Tab. 5, the ratio of the photogenerated current I to dark current Iph for the different samples are calculated and given for three different D values of biasing voltage. This ratio is very important in photodetector devices where very low leakage current is needed. It is clear that sample S3 and S4 are better with I regard to the ratio of Iph . As this ratio increases the detectivity increases. D

Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 105

Table 5: Ratio of photogenerated current to dark current Sample Vbias (V) Iph ID

S1

S2

Iph ID

for the different samples.

S3

S4

0.1

0.5

1

0.1

0.5

1

0.1

0.5

1

0.1

0.5

1

1.7

1.4

1.3

2.2

2.4

1.6

1.1

2.0

5.4

2.9

5.4

6.5

Figures 7 and 8 represent the variation of Iph versus reverse biasing voltage under various illumination intensities Φ for samples S3 and S4 . As VR increases the magnitude of Iph is increased for all light intensity. This is because the carrier transit time decreases consequently the recombination probability is minimized. For higher light intensity the variation of Iph with VR is almost linear. This indicates that the photogenerated carriers are not completely collected and the effect of series resistance of the structure started to appear [15].

Figure 7: Variations of Iph vs the reverse voltage VR under various illumination intensities for sample S3 .

Figure 8: Variations of Iph vs the reverse voltage VR under various illumination intensities for sample S4 .

106 | W. F. Mohamad and M. N. Al-Tikriti Detectivity D is defined as the signal to noise ratio optical power Pin as: D=

S N

per unit quantity of incident

S 1 × Pin N

(11)

Light filters of specified wavelength have been placed in front of the samples to permit a single wavelength to be transmitted through. The effect of incident radiation wavelength λ on the detectivity for sample S4 for different values of light intensities and biasing voltages are presented in Fig. 9 and Fig. 10, respectively. The detectivity decreases as the light intensity increases due to the reduction of signal to noise ratio, while it is increased as the biasing voltage increases. There is a maxima at 525 nm for both figures. Also the detectivity shows tendency increase as the wavelength increases even after giving maximum value particularly for sample 4 promising that the detectors could be a good candidate at infrared region.

Figure 9: Variations of detectivity versus wavelength under different values of light intensity for sample S4 .

Figure 10: Variations of detectivity versus wavelength under different values of reverse voltages for sample S4 .

Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 107

6 Conclusion The use of amorphous silicon and germanium can be considered as good alternative for the fabrication of photodetectors from view point of cost and technical specifications. The focus of this research paper had been to thoroughly investigate the optoelectronic characteristics of photodetectors fabricated from silicon germanium alloy. The emphasis had been on conduction mechanism, activation energy, photoconductivity, detectivity and Hall measurement. The theoretical evaluation and experimental calculation of Poole-Frenkel coefficient β show good degree of similarity which consequently indicates that the conduction mechanism is Poole-Frenkel type and the general equation for the conduction current can be used satisfactorily to explain this process. From the study of ln σ with respect to T1 , it can be concluded that the activation energy is linearly dependant on the percentage of germanium x and the ratio of SiGe could influence the Fermi level which is lowered with the increase of the ratio. This can be explained that there are two types of dangling bounds residing on the Si and Ge atoms with high number defects in the samples that have higher values of x. The results also indicate that for sample with x = 0.3 the activation energy is minimum at 600 ∘ C annealing temperature. This can be attributed to the presence of maximum defects situated near the mid-gap and narrow band of Fermi-Dirac occupation. From the Hall measurement, the fabricated a-SiGe films are type n and the electrons are the majority carriers. Hall mobility and carrier concentration increase slowly for small values of x (x < 0.1) and more rapidly for large x (x > 0.1). Hetero-junction characteristics and for dark current condition show that as x increases the junction exhibits good diode effect with large forward and small reverse currents. When the ideality factor is around unity the recombination current will be dominate and that reduces the forward current. The optoelectronic measurements show that for samples S3 and S4 as the reverse voltage VR increases the photogenerated current Iph increases for all light intensities due to the minimization recombination probability as result to the reduction in transit time. For higher light intensities there is some linearity variation of Iph with VR due to the fact that the photogenerated carriers are not fully collected. For sample S4 the detectivity tends to decrease with the increase of light intensity due to the reduction of NS ratio. The detectivity increases as the biasing voltage increases and exhibits maximum values at 525 nm wavelength.

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Biographies Wagah F. Mohamad, is professor of electronic at Philadelphia University, Jordan. He was awarded his Master degree and PhD in Electronics and Electrical Engineering from Manchester University, England; 1977 and 1980 respectively. He teaches courses on analogue electronics, digital electronics, microelectronics, solid state electronics and advanced design of digital electronics. His research interests includes design and fabrications of microelectronics circuits, fabrication and characterization of thin films, photodetectors fabrications, applications and fabrication also investigation of solar cells. He published two scientific books and more than fifty papers in reviewed journal and international conferences. He supervised five PhD students and more than 15 M. Sc. students. He has long experience with academic administration as a head of research group, head of department, deputy dean and dean of engineering college.

Optoelectronic Properties of Amorphous Silicon Germanium Photodetectors | 109

Munther N. Al-Tikriti, British graduate, B. Sc (Honour) in Electrical Engineering from London University – England, and awarded M. Sc and Ph. D in Control Engineering from Salford University – England. Taught in various universities in England, Iraq and Jordan. Published over 50 scientific papers in different international and regional journals and conferences. Published seven books in Arabic. Supervised over 60 postgraduate theses 10 for Ph. D degrees.