Tutorials

Block 1 – AM

T1: Towards Smart and Closed-Loop Neuromodulation
by Jie Yang, Mahdi Tarkhan, and Mohamad Sawan, Westlake University, China
8:30 AM – 12:00 PM

T2: Artificial Intelligence for Design of Energy-Efficient Electronic Systems
by Ahmed Ragab, CanmetENERGY, Canada.
8:30 AM – 12:00 PM

T3: A Gentle Introduction to Machine Learning
by Pierre Gravel, Institute Intelligence Data, Université Laval, Canada
T3: Artificial intelligence in 5G/6G wireless communication systems
by Seyedeh Samira Moosavi, Institute Intelligence Data, Université Laval, Canada
8:30 AM – 12:00 PM

T4: Polymers and Polymeric Composites in IoT Devices: A Crash Course
by Hyun-Joong Chung, University of Alberta, Alberta, Canada
8:30 AM – 12:00 PM

Block 2 – PM

T5: Emerging Materials, Manufacturing and Circuit Topologies for Printed Thin Film Transistors
by Sharmistha Bhadra, McGill University, Québec, Canada.
1:00 PM – 4:30 PM

T6: Analog, Mixed Signal and Power Integrated Circuits (ICs) for automotive applications and testing methodology for quality
by Sri Navaneeth Easwaran, Texas Instruments, United States & Robert Weigel, University of Erlangen-Nuremberg, Erlangen, Germany.
1:30 PM – 4:30 PM

T7: Artificial Intelligence for Wearable Devices & Study Case of Electromyography-based Prosthesis Control Systems
by Mounir Boukadoum, Université du Québec à Montréal, & Simon Tam, Université Laval, Québec, Canada
1:30 PM – 4:30 PM

Emerging Materials, Manufacturing and Circuit Topologies for Printed Thin Film Transistors
June 19, 2022 / 1:30 PM – 4:30 PM

by Sharmistha Bhadra, McGill University, Canada.

— Abstract — Research in printed electronics technology has led to growing commercial interests due to costeffective and environment friendly manufacturing. IoT devices have been one of the prominent areas of application for printed electronics as low cost and environmentally devices are sought for these applications. However, there is no commercially available all printed IoT devices today. Sensors are the only printed elements used in some IoT devices. The main obstacle to achieve all printed IoT devices is the poor performance printed thin film transistors (TFTs). As transistors are the building block of most of the electronic devices, it is important to develop high performance printed TFTs to make all printed IoT devices a reality. The tutorial session is intended to identify the root causes of some of the limitations associated with development of high performance printed TFTs and propose methods to circumvent them. It will cover comparison of conventional technologies, materials used for printed TFTs, their limiting factors and present emerging materials and technologies that can overcome those limitations. It will also cover how new circuit topologies can improve performance of printed TFTs. The tutorial will present some examples of high performance printed TFTs from the literature (with some from the author’s laboratory). At the end, the tutorial will allow the attendees to brainstorm on possible ways to improve the performance of a specific printed TFT. There is an emerging body of research investigating printed TFTs to fabricate all printed useful electronic devices such as amplifier, microcontroller and memory. However, performance of printed TFTs is poor compared to their microfabricated counterparts. This is due to many factors such as the accuracy, resolution, uniformity and repeatability limitations of printing processes, low carrier mobility of printable semiconductor materials, environmental instability of printable materials, low dielectric constants of printable dielectric materials, adhesion issue between two printed layers. By attending the tutorial the researchers will understand the concept of printed TFTs and will be able to identify basic limitations of printed electronics technology to develop high performance TFTs. They will learn how new materials, fabrication processes and advanced circuit topologies can overcome these limitations. Overall, they will acquire knowledge necessary for design and development of high performance TFTs.

— Bio — Sharmistha Bhadra received the B.Sc. degree in computer engineering from the University of New Brunswick, Canada and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Manitoba, Canada. From 2015 to 2016 she was an NSERC postdoctoral fellow at the University of British Columbia, Canada. She joined McGill University in 2016 and is currently an assistant professor. Her current research interests are in the area of printed and flexible hybrid electronics, microelectronics, microelectromechanical systems, and sensors and wearables. She has published over 77 papers and holds 2 patents in these areas. Her research program at McGill University leverages conventional design and development tools as well as printed electronics technology to find cost effective and high performance innovative electronic technology. One of her more concrete short term research goal is to use of printed and flexible hybrid electronics technology to develop high performance sensors and other basic electronic components such as thin film transistors (TFTs). Her projects in printed and flexible electronics area include design and development of a high performance acoustic gas sensor, fully printed chipless RFIDs for liquid and ion sensing, a printed TFT based ammonium ion sensor, a flexible hybrid system integrated with smart mandibular device, single wall carbon nanotube based high performance printed TFTs. Previously she has worked on flexible hybrid system for motion artifact free respiratory monitoring. This work was selected as one of the best papers in IEEE Sensors 2018 conference. Her work in printed and flexible electronics area has been presented and published in prestigious conferences and journals such as IEEE Sensors Journal, IEEE Transactions on Biomedical Circuits and Systems, IEEE Journal of Flexible Electronics, Sensors and Actuators A, IEEE International Conference on Flexible and Printable Sensors and Systems, IEEE International Flexible Electronics Technology Conference, IEEE Sensors Conference. She has organized a special session on printed and flexible IoT sensors in IEEE sensors 2019 conference and served as a track chair for that session. She has given invited talks and organized tutorials on printed and flexible electronics in different venues such as IEEE FLEPS Conference, IEEE Sensors Conference, IEEE Sensors Council Vancouver Chapter, IEEE International Flexible Electronics Conference, IEEE Electron Devices Vancouver Chapter, IEEE Newfoundland Labrador Computer, Communication, and Circuits & Systems Joint Societies, ReSMiQ Day.

Towards Smart and Closed-Loop Neuromodulation
June 19, 2022 / 8:30 AM – 12:00 PM

by Jie Yang, Mahdi Tarkhan, and Mohamad Sawan, Westlake University, China

— Abstract — The human brain is a miraculous result of biological evolution, hundreds of billions of interconnections between nerve cells construct the foundation for the cognition, thinking, consciousness, and language functions of human beings. Elucidation of the basic principles of neural networks in the brain not only allows us to introduce novel artificial intelligence algorithms and computing paradigms but also facilitates the diagnosis and intervention of various brain diseases. Exploration of the brain becomes the most challenging scientific problem of the 21st century. Thanks to the development of the semiconductor industry, the integration of large quantities of low-noise bio-amplifiers and analog-to-digital converters (ADC) now allows the recording of various brain signals such as local field potential, action potential, electroencephalography (EEG), electrocorticography (ECoG), fNIRS, etc. The channel counts can range from hundreds to hundreds of thousands. Moreover, with the emergence of artificially intelligent technology, the analysis of the recorded signals can be analyzed to help discover biomarkers for disease diagnosis and patterns for perception, emotion, etc. On the other hand, electrical and optical neural stimulation circuits enable the modulation of neurons to suppress or excite the signal transmission of neurons or neural networks. Neuromodulation plays an important role in the treatment of various neurological and psychological diseases. The loop between neural recording, signal analysis, and neuromodulation can be closed to form a completely closed-loop system. It can be used to conduct novel studies, experiments, and treatments that were once not possible before. In this tutorial, we focus on the most currently conducted research activities in circuits and systems for brain-machine interface and closed-loop neuromodulation. Firstly, we give a general background of neuromodulation and give the overall specification from a system perspective. Then, analog front-end (AFE) architectures and circuits for neural recording will be introduced including time-division and frequency-division multiplexing architectures, AC- and DC-coupled AFE, and direct-digital readout AFE. Consequently, popular signal compression and analysis algorithms and circuits are demonstrated including compressive sensing, principal component analysis, support vector machine, regression, and deep neural networks will be explained. To close the loop, popular neuromodulation methods and corresponding circuits will be elaborated. At last, we conclude this tutorial by showcasing and outlooking a few closed-loop systems and their applications. This is a very comprehensive topic in that it covers almost all the current state-of-the-art circuit and system techniques, such as analog, digital, and power electronics. Additionally, this type of research is very hot not only in the academic community but also in industry. Because it relates to human health and safety, standards are more stringent, and therefore, the designer must put in more effort to achieve practical circuits and systems. Considering this, it will be a great chance for NEWCAS attendees to be acquainted with the latest advancements in bioelectronics through case studies.

— Bio — 

Jie Yang received the B.S. degree in electronic science and technology from Tianjin University, Tianjin, China, in 2010, and the Ph.D. degree in microelectronics from the Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China, in 2015. He was a post-doctoral fellow with the I2Sense Lab, Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada from 2015 to 2019. He joined Westlake University as a research associate professor at Cutting-Edge Net of Biomedical Research and INnovation (CenBRAIN), School of Engineering, Westlake University. His current research interests include algorithms and very large-scale integration architecture for intelligent biomedical diagnosis and prediction, image processing, computer vision, and deep learning.

Mahdi Tarkhan (M’21) was born in Zahedan, Iran, in 1984. He received the M.Sc. degree in digital electronics from Sharif University of Technology, Tehran, Iran, and the Ph.D. degree in analog electronics from Ferdowsi University of Mashhad, Iran, in 2009 and 2018, respectively. In 2020, he joined the Cutting-Edge Net of Biomedical Research and Innovation (CenBRAIN), School of Engineering, Westlake University, Hangzhou, China, where he is currently a postdoctoral fellow in biomedical circuits and systems. His current research involves low-noise and low-power analog and mixed-signal circuits and system design for biomedical applications.

Mohamad Sawan (S’88-M’89-SM’96-F’04) received the Ph.D. degree in Electrical Engineering from Universite de Sherbrooke, Sherbrooke, QC, Canada, in 1990. He is a Professor of Microelectronics and Biomedical Engineering, in leave of absence from Polytechnique Montréal, Canada. He joined Westlake University, Hangzhou, China, in 2019, where he is a Chair Professor founder and director of the Center for Biomedical Research And INnovation (CenBRAIN). He was Chair Professor awarded the Canada Research Chair in Smart Medical Devices (2001-2015), was leading the Microsystems Strategic Alliance of Quebec – ReSMiQ (1999-2018). He founded and Chaired the IEEE-Solid State Circuits Society Montreal Chapter (1999-2018) and founded the Polystim Neurotech Laboratory in Polytechnique Montréal (1994-Present), including two major research infrastructures intended to build advanced Medical devices. Dr. Sawan was Deputy Editor-in Chief of the IEEE Transactions on Circuits and Systems-II: Express Briefs (2010-2013), Co-Founder, Associate Editor and Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems (2016-2019), Associate Editor of the IEEE Transactions on Biomedical Engineering. He is founder of the Interregional IEEE-NEWCAS Conference, and Co-Founder of the International IEEE-BioCAS, ICECS and LSC conferences. He hosted in Montreal, as General Chair, the 2016 IEEE International Symposium on Circuits and Systems (ISCAS), and will host as General Chair, the 2020 IEEE International Engineering, Medicine, Biology Conference (EMBC). He served as member of the Board of Governors (2014-2018), and he is Vice-President Publications (2019-Present) of the IEEE CAS Society. Dr. Sawan published more than 800 peer reviewed papers, two books, 10 book chapters, and 12 patents. He received several awards, among them the Queen Elizabeth II Golden Jubilee Medal, the Barbara Turnbull 2003 Award for spinal-cord research, the Bombardier and Jacques-Rousseau Awards for academic achievements, the Shanghai International Collaboration Award, and the medal of merit from the President of Lebanon for his outstanding contributions. Dr. Sawan is Fellow of the IEEE, Fellow of the Canadian Academy of Engineering, and Fellow of the Engineering Institutes of Canada. He is also “Officer” of the National Order of Quebec.

Artificial Intelligence for Design of Energy-Efficient Electronic Systems
June 19, 2022 / 8:30 AM – 12:00 PM

by Ahmed Ragab, CanmetENERGY, Canada.

— Abstract — The objective of this tutorial is to provide the attendees with key advancements of Artificial Intelligence (AI) and related Machine Learning (ML) technologies and their applications in the design of energy-efficient electronic circuits. The tutorial intends to provide an update/overview and to open a dialog on how to address the challenges faced in the design process. The different pathways to innovate through electronic systems design will be discussed through use cases selected from our ongoing projects, followed by a discussion on some opportunities.

— Bio — Ahmed Ragab is an AI research scientist working for CanmetENERGY, an energy innovation center of Natural Resources Canada (NRCan). He is an adjunct professor at the Department of Mathematics and Industrial Engineering, Polytechnique Montréal. His research interests include AI, Data Fusion, Image Processing, Causality Analysis, Operations Research, Discrete Event Systems, and Process Mining. His main thematic activities focus on the practical challenges of Big Data and AI in a number of applications including Abnormal Events Diagnosis & Prognosis, Supervisory Control, Real-Time Optimization, Systems Design and Predictive Maintenance. He has a bunch of experience in developing advanced algorithms and tools in the manufacturing industry, aiming at reducing energy consumption, Greenhouse gas (GHG) emissions, and operational and maintenance costs while improving operations’ performance.

Analog, Mixed Signal and Power Integrated Circuits (ICs) for automotive applications and testing methodology for quality
June 19, 2022 / 1:30 PM – 4:30 PM

by Sri Navaneeth Easwaran, Texas Instruments, United States & Robert Weigel, University of Erlangen-Nuremberg, Erlangen, Germany.

— Abstract — Power transistors form the main component of automotive Integrated Circuits (ICs) and they handle several amperes of current (>2A). State of the Art is to integrate several power MOSFETs along with their gate drivers (including charge pumps or boost converters that supply the gate drivers) whose operating voltage range is from 5V to 60V. Reliability of these integrated gate drivers and power transistors is a key factor to meet the high-quality demands of the automotive applications. In this tutorial, challenges related to the design and reliability of circuits like LDOs, High Side (HS) drivers, Low Side (LS) drivers and configurable HS/ LS drivers are discussed along with information related to floating nodes, aging and reliability related concerns like NBTI/PBTI, HCI etc. and proven design techniques to simulate and mitigate these challenges. These gate drivers have to be thoroughly designed for robustness w.r.to. Electrical and Thermal Safe Operating Area (SOA) and its test methodology by shorting the outputs to ground and battery will be discussed. In this tutorial, Design FMEA (Failure Mode Effect Analysis) based analysis to mitigate risks at design and system level along with test concepts towards very low dppm (defective parts per million) will be discussed. This tutorial will be valuable for the design, product and test engineers developing ICs for automotive and industrial applications.

— Bio —

Sri Navaneeth Easwaran, Senior Member IEEE, received his Bachelor’s (1998, Bharathidasan University), Master’s (2006, University Twente) degrees in Electrical Engineering and Dr. –Ing. degree from University of Erlangen-Nuremberg in 2017. He worked at SPIC Electronics, STMicroelectronics, Philips Semiconductors between 1998 and 2006. From 2006 he is with Texas Instruments (TI) where he was the design lead of airbag squib driver ICs. and System Basis Chips. He is an IET Fellow (Feb 2021), TI Senior Member Technical Staff, has 20+ granted patents and 14 publications. He has offered tutorials on automotive ICs at IEEE Conferences. Since Dec 2020, he is offering iDLP (Industrial Distinguished Lecturer Program) CASS seminars on smart automotive circuits.

Robert Weigel, Fellow IEEE and Fellow ITG, is Full Professor at the University of Erlangen-Nuremberg, Germany. He co-founded several companies some of which were later overtaken by Infineon, Intel and Apple, respectively. He has been engaged in microwave electronic circuits and systems and has published more than 1200 papers. He received the 2002 VDE ITG-Award, the 2007 IEEE Microwave Applications Award, the 2016 IEEE MTT-S Distinguished Educator Award, the 2018 Distinguished Service Award of the EuMA and the 2018 IEEE Rudolf Henning Distinguished Mentoring Award. He has been Distinguished Microwave Lecturer, MTT-S AdCom Member, and the 2014 MTT-S President.

Artificial Intelligence for Wearable Devices & Study Case of Electromyography-based Prosthesis Control Systems
June 19, 2022 / 1:30 PM – 4:30 PM

by Mounir Boukadoum, Université du Québec à Montréal, & Simon Tam, Université Laval, Québec, Canada

— Abstract — This tutorial explores the application of artificial intelligence in wearable devices and its unique inherent design challenges on both software and hardware. Through the use case of electromyography-based prosthesis control, real-time sensing, signal processing, and machine learning pipelines will be detailed. Overall system design and hardware integration will be discussed as potential solutions to most recent challenges in the state-of-the-art. Rehabilitation engineering seeks to provide technological tools for patients to recover as much of their abilities as possible following a major trauma or consequences of debilitating diseases. In the case of a limb amputation, a prosthesis aims to physically replace a missing extremity while trying to recover most of its functionalities. The degrees-of-freedom (DoF) required from the prosthesis vary from a limb to another, with the hand being especially complex. It is used in a wide variety of tasks, from daily routines to specific activities and involve multiple joints with different axes of motion. A multitude of muscles also allow for modulation of motion amplitude and strength. As such, not only is the hand a complex tool to replicate mechanically, but the design of an appropriate control interface also poses a major challenge. Machine learning provides a powerful tool to interface body and machines. In the case of amputated patients, where each user shows distinctive physical characteristics and resilient muscle abilities, machine learning algorithms provide adaptive solutions as they learn from user-generated signals. This tutorial will present how to structure a standard machine learning classification problem and how it can be applied to biomedical systems such as pattern recognition in electromyography (EMG) signals. Different sensor and feature extraction methods will be presented for classification algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Neural Networks. Challenges and strategies will be discussed to improve the state-of-the-art on user-centered needs such as ease-of-use and intuitiveness. To address those concerns, an approach leveraging high-density EMG (HDEMG) and deep learning will be presented. This method aims to use convolution neural network (CNN) learned feature extraction capabilities to better fit unique users and enables transfer learning strategies to alleviate the dataset recording burden of individual users. A walkthrough of the complete sensing, preprocessing and inference pipeline will be given. Experiment protocols, tools and metrics will be presented to evaluate classification accuracy and real-time control reliability. Furthermore, real-time and user-centered wearable biomedical devices provide a unique set of challenges for applied artificial intelligence. Hardware constraints and limited availability of data for given individuals require creative approaches and paradigm shifts in algorithm design and hardware integration. State-of-the-art solutions and hardware platforms will be reviewed and discussed in regards to their potential of further advancing research in various fields of application and materializing results in the real-world.

— Bio —

Mounir Boukadoum is professor of microelectronics engineering at The University of Quebec at Montreal (UQAM), Canada. He has a background in physics and electrical engineering, with an emphasis on biomedical applications, and received the PhD degree in Electrical Engineering from the University of Houston, Texas, in 1983. His current research covers the applications of artificial intelligence and nature-inspired techniques to analysis and design problems, particularly in relation to biomedical outcomes. Pr. Boukadoum is currently director of the Microelectronics Prototyping laboratory at UQAM (MicroPro) and the Quebec Strategic Alliance for microsystems (ReSMiQ), a research consortium of 10 Quebec universities and engineering schools. He is a cofounder of the Montreal chapter of the IEEE Computational Intelligence Society’s and was its president from 2009 to 2018. He is also a cofounder of the IEEE NEWCAS conference, now a regional flagship conference of the IEEE CAS Society. Pr. Boukadoum has been an active member of IEEE for over 25 years, with involvement in organizing several major IEEE conferences, of which ISCAS and EMBC. He is currently a member of the NEWCAS, ICECS and ICM steering committees, and member of the Neural Systems and Applications technical committee (NSA-TC). He has published over 200 refereed journal and conference papers.

Simon Tam received the B. Eng. Degree in electrical engineering from Laval University, Quebec, QC, Canada, in 2018. He has pursued the M. Sc. degree and is currently pursuing a Ph. D. in electrical engineering at Laval University under the supervision of prof. Benoit Gosselin (Biomedical Microsystems Laboratory). His main research interests include machine learning/deep learning in biomedical applications, smart sensors and electronic systems, and myoelectric control.

Polymers and Polymeric Composites in IoT Devices: A Crash Course
June 19, 2022 / 8:30 AM – 12:00 PM

by Hyun-Joong Chung, University of Alberta, Alberta, Canada

— Abstract — Polymers, in general, are ubiquitous in electronic devices; they are in capacitors, in batteries, in PCB substrates, in packing, in sensors, and in flexible conducting traces, just to name a few.  Network polymers refer to highly interconnected polymers that are highly stable under heating and to chemical exposure. Most epoxies, polyimides, and elastomers are network polymers. The important class of electronic material is, however, often one of the most misunderstood and misused substance in consumer electronics.  In this 3-hour crash course, I will provide (i) basic concepts that are necessary to understand the characteristics of network polymers, (ii) understanding thermoset polymers (such as electronic packaging and adhesive materials), (iii) understanding elastomers and gels (substrate for stretchable electronics), and (iv) their applications in stretchable electronics and soft robotics.  The lecture will build up the story from polymeric materials used in conventional electronic devices, such as electrical insulators, adhesives, capacitor dielectrics, wire/cable materials.  Then, the stories about specialty polymers, such as conductive conjugated polymers, ferroelectrics, liquid crystals, and ionomers, will be told.  Finally, the story will reach to the current trend of flexible, stretchable and printed IoTs, with an outlook of future technologies.

— Bio — Hyun-Joong Chung, Associate Professor of Chemical and Materials Engineering at the University of Alberta, is a materials engineer who leads a broadly interdisciplinary research program on understanding physicochemical properties of gels and elastomers with or without functional additives and reinforcements, as well as on translating fundamental understanding to biomedical and energy device applications.  His contributions include key studies on the role of jamming nanoparticles in phase-separating polymer blends. His works on oxide semiconductors and wearable devices which have been highly recognized by international information display and flexible electronics communities.

A Gentle Introduction to Machine Learning
by Pierre Gravel, Institute Intelligence Data, Université Laval, Canada
Artificial intelligence in 5G/6G wireless communication systems
by Seyedeh Samira Moosavi, Institute Intelligence Data, Université Laval, Canada
June 19, 2022 / / 8:30 AM – 12:00 PM

— Abstract — With the incremental commercial use of 5G wireless technology, academic research on the sixth-generation (6G) mobile communication technology has also begun. 6G wireless networks will enhance and expand 5G applications by achieving a higher data rate per user/device (10–100 times greater than 5G). Besides, it will support wider coverage and a larger number of connected devices, massive Internet of Things (IoT), distributed massive multiple-input multiple-output (DM-MIMO), and high and reliable connectivity. Machine learning (ML) techniques, as part of artificial intelligence (Al), have proven to be extremely useful in a wide variety of applications and now their applications in 6G wireless communication systems have been the subject that attracts incredible interest in recent years. ML involves teaching the machines to perform tasks independently based on making data-driven decisions and can accurately estimate various parameters and promote interactive decision-making. Therefore, one of the main and key components of 6G systems will be the use of AI for mobile communication networks and we believe that 5G/6G and AI will be the two core technologies of the future intelligent system. In this tutorial, we first focus on the most applicable ML approaches and algorithms and present the requirements and the best context of use for each of those algorithms. A series of simple examples are given as introductory concepts. Then, in the second part, we introduce the most current applications of AI in 5G/6G wireless technologies which consist of body-area networks, smart homes and industry, connected healthcare, remote surgery, mission-critical, autonomous driving, and vehicle-to-vehicle (V2V) communications, etc. Finally, we present a related project which is about localization in 5G wireless technology using ML methods. In this project, a fingerprint-based positioning method in M-MIMO systems was proposed to provide an accurate and reliable localization system in a 5G wireless network. For this purpose, signal features in DM-MIMO systems and instantaneous channel state information (CSI) in collocated M-MIMO (CM-MIMO) systems were extracted from the received signals as fingerprints. Then, an optimal clustering scheme was presented to split up the target area into several small regions, which minimized the searching space of reference points and decreased the computational complexity. Finally, a regression model using algorithms such as Gaussian process regression (GPR) and neural network was created for each region based on the data distribution within each region to provide further positioning accuracy. Through this approach, the average error was improved to a few meters, which was expected in 5G networks and the computational complexity of utilizing the ML method was also reduced.

— Bio — Seyedeh Samira Moosavi received her B.Sc. degree in Information Technology Engineering in 2013, her M.Sc. degree in Network Engineering from Shiraz University of Technology (SUTech) in 2016, and her Ph.D. degree in Electrical and Computer Engineering from Laval University in 2021. Her research interests include data mining, machine learning and its applications in 5G wireless network technology and smart cities as well as health and insurance.