By Hossein Kassiri Bidhendi a Thesis Submitted in Conformity

MULTI-MODAL DENSELY-INTEGRATED CLOSED-LOOP NEUROSTIMULATORS FOR MONITORING AND TREATMENT OF NEUROLOGICAL DISORDERS by Hossein Kassiri Bidhendi A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto c Copyright 2016 by Hossein Kassiri Bidhendi II Abstract Multi-Modal Densely-Integrated Closed-Loop Neurostimulators for Monitoring and Treatment of Neurological Disorders Hossein Kassiri Bidhendi Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2016 This dissertation presents the design, implementation and validation of four multi- and single-die systems-on-chip (SoCs) for diagnostic and treatment of neurological disorders. The first prototype is a multi-die wireless device that is designed and implemented as the first step toward a fully-integrated wireless brain machine interface SoC. The device is sized at 2 × 2 × 0.7 cm3, weighs 6 grams, and is comprised of two mini- boards and a power receiver coil. It takes advantage of two previously-reported chips (one on each board) as the core components for neural recording and stimulation, and for wireless data/power communication, respectively. The system is validated in: (a) in vivo detection and control of epileptic seizures in rats with temporal lobe epilepsy, and (b) sleep-stage classification and triggering responsive stimulation for REM (rapid eye movement) sleep suppression. The second prototype is a 16 mm2 0.13 μm CMOS SoC. In this design, all the system-level functionalities of the above multi-die system (i.e. wireless data transmit- ters, wireless power receiver, signal processing unit for seizure detection, etc.) are integrated on a single die, and the AC-coupled recording channel is replaced with a chopper-stabilized digitally-assisted DC-coupled front-end. This is done by borrowing III circuit blocks of three previously-reported chips (1. wireless power and data 2. DC- coupled front-end and 3. stimulator, and digital backend) and combining them on the same die. Channel-to-channel gain mismatch among the 64 channels of this chip is re- moved by utilizing a multiplying ADC, included in each channel, in a digital calibration loop. The third prototype, which is the latest generation of responsive neurostimulator SoCs developed in our lab, features 64 correlated double-sampled Δ2Σ-based (a Δ stage and a ΔΣ stage) neural recording front-ends capable of recording brain signals with rail-to-rail DC offset variation. The mixed-signal design results in the channel area reduction by an order of magnitude (0.013 mm2 for amplifier+ADC+stimulator), and channel power consumption being linearly scalable with the input signal frequency bandwidth. Additionally, using a current-output-DAC that is placed in the feedback path of the Δ2Σ ADC, a mixed-mode analog-digital multiplication is performed in each channel. This yields a compact implementation of band-pass digital filters, as well as voltage gain scaling. The analog multiplication circuit is reused as a current-mode stimulator when the SoC is configured to perform neurostimulation. The chip occupies 6mm2 and is validated in vivo in epileptic seizure monitoring, detection, and abortion. The fourth prototype is a wireless 4-channel dual-mode arbitrary-waveform neurostimulator IC with 20 V voltage compliance. The system uses a load-aware adaptive supply voltage control, which results in up to 68.5% saving in power consumption. The 10 mm2 SoC is implemented in a 0.35μm HV-CMOS process. It is housed in a 2 × 2 × 0.7 cm3 multi-PCB device, that also provides wireless power and data/commands telemetry for the chip. This design is preceded by the design of a board-level high- voltage hybrid 16-channel electrical and 8-channel optogenetic stimulator, validated in vivo for both its electrical and optogenetic stimulation functionalities. IV To Him who taught me to love To my parents for their unconditional love V Acknowledgements First and foremost, I would like to thank Professor Roman Genov, my PhD advisor for his insight, guidance, and support throughout my doctoral studies. Coming from a pure electronic background, I had very limited knowledge on the field of biomedical electronics when I started my PhD. He patiently introduced me to the field, connected me with the right collaborators, and generously shared his vast knowledge on the field. During my work in Prof. Genov’s lab, I always felt that I have access to every resource required to implement my ideas. This included funding for fabrication of about a dozen silicon chips and PCBs, to access to world-class experts for consulting about and validating of my designs. Finally I would like to thank him for supporting me in scholarship applications, for giving me the opportunity to mentor undergraduate and graduate stu- dents, and for all the non-electronic life lessons I learned from him as my supervisor, and also as a friend. I would also like to thank my defense committee: Prof. Gulak, Prof. Liscidini, Prof. Plataniotis, Prof. Ng, and my external examiner, Prof. Verma for reading my thesis and their feedback which helped improving this thesis. During my 5 years in U of T, I was lucky to have Prof. Perez Velzquez, Prof. Carlen, and Prof. Valiante as collaborators to ask my neuroscience questions and for testing my prototypes in their labs. They generously offered me their knowledge, lab space, and equipment in Toronto Hospital for Sick Children and Toronto Western Hospital. I would like to thank them and their group members for their continuous help during the past 5 years. Specifically, I would like to thank Yana Adamchik, Michael Chang, and Wanida Nuwisait for their help with different animal experiments. Over the past few years, there were a few people who contributed significantly in my research which I am very grateful to. I would like to thank Amer Samarah for patiently VI answering my CAD questions, Karim Abdelhalim for introducing me to this project, and Arshya Feyzi for always being available to hear and criticize my circuit ideas. My special thanks goes to M. Tariqus Salam and Nima Soltani. Tariq was involved in all of the animal experiments I did over the past few years, and helped me more than anyone else to validate my designs. He was always available to answer my never- ending neuroscience questions, connected me to the right people in the hospital, and more importantly, has been a great friend. Nima has shared the 5-year journey with me almost from the first day, we co-designed a chip together, had several long technical discussions which resulted in building our PhD ideas, and spent hundreds of hours in the lab helping each other in testing and validating our designs. I would also like to acknowledge my U of T colleagues, who made my PhD the most memorable experience of my life. I would like to thank Farhad Ramezankhani, Enver G. Kilinc, Javid Musayev, Navid Sarhang Nejad, Behzad Dehlaghi Jadid, Saber Amini, Masumi Shibata, Hyunjoong Lee, Stefan Shopov, Konstantinos Vasilakopoulos, Sevil Zeynep Lulec, Aynaz Vatankhah, Samira Karimelahi, Alireza Sharif Bakhtiar, Kentaro Yamamoto, Derek Ho, Alireza Nilchi, Hamed Sadeghi, Amir Hejazi, Saeid Mojiri, and Mahdi Marsousi. I am also grateful to ECE staff members Jennifer Rodriguez, Jaro Pristupa, Darlene Gorzo, Jayne Leake, and Gaja Sanmugaratnam who helped me in different ways during the past 5 years. I was fortunate to have the opportunity to mentor graduate and undergraduate stu- dents who also contributed into my research. I would like to thank Aditi Chemparathy, Yu Hu, Richard Gao, Chang Liu, Gairik Dutta, Shreedutt Hegde, Fadime Bekmambe- tova, Fred Chen, Behraz Vatankhahghadim, Kevin Gumba, Sana Tonekaboni, Peter Li and Alan Li. My wife had to go through a lot of trouble so that I could finish this work. I am greatly indebted to her for all she has done. Finally, I would like to thank my parents VII and my sister for their unconditional support throughout the years and motivating me to finish. VIII Contents 1 Introduction 1 1.1 Objective ................................. 1 1.2 Implantable Brain Machine Interfaces .................. 3 1.2.1 System Overview ......................... 3 1.2.2 System Design Requirements .................. 4 1.3 Large Form-Factor Neurostimulators ................... 6 1.3.1 Academic Designs ........................ 6 1.3.2 Commercially-Available Neurostimulators ........... 7 1.4 Integrated Circuits for Wireless Responsive Neurostimulators ...... 12 1.4.1 Electrodes ............................. 12 1.4.2 Recording Amplifier ....................... 16 1.4.3 Digital Backend ......................... 21 1.4.4 Wireless Data and Power ..................... 22 1.5 Electrical Current/Voltage and Optogenetic Stimulation ......... 24 1.5.1 Electrical High-Voltage Stimulators ............... 24 1.5.2 Optogenetic Stimulation ..................... 28 1.6 Thesis Organization ............................ 34 2 Implantable Wireless Mini-Board for Monitoring and Treatment of Neuro- IX logical Disorders 41 2.1 Battery-less Modular Responsive Neurostimulator for Prediction and Abortion of Epileptic Seizures ...................... 42 2.1.1 Introduction ............................ 42 2.1.2 Methods and Material ...................... 43 2.1.3 Results .............................. 48 2.1.4 Safety ............................... 50 2.1.5 Conclusion ............................ 53 2.2 Electronic Sleep Stage Classifiers: A Survey and VLSI Design Method- ology

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