Digital CMOS ISFET architectures and algorithmic methods for point-of-care diagnostics

Over the past decade, the surge of infectious diseases outbreaks across the globe is redefining how healthcare is provided and delivered to patients, with a clear trend towards distributed diagnosis at the Point-of-Care (PoC). In this context, Ion-Sensitive Field Effect Transistors (ISFETs) fabricated on standard CMOS technology have emerged as a promising solution to achieve a precise, deliverable and inexpensive platform that could be deployed worldwide to provide a rapid diagnosis of infectious diseases. This thesis presents advancements for the future of ISFET-based PoC diagnostic platforms, proposing and implementing a set of hardware and software methodologies to overcome its main challenges and enhance its sensing capabilities. The first part of this thesis focuses on novel hardware architectures that enable direct integration with computational capabilities while providing pixel programmability and adaptability required to overcome pressing challenges on ISFET-based PoC platforms. This section explores oscillator-based ISFET architectures, a set of sensing front-ends that encodes the chemical information on the duty cycle of a PWM signal. Two initial architectures are proposed and fabricated in AMS 0.35um, confirming multiple degrees of programmability and potential for multi-sensing. One of these architectures is optimised to create a dual-sensing pixel capable of sensing both temperature and chemical information on the same spatial point while modulating this information simultaneously on a single waveform. This dual-sensing capability, verified in silico using TSMC 0.18um process, is vital for DNA-based diagnosis where protocols such as LAMP or PCR require precise thermal control. The COVID-19 pandemic highlighted the need for a deliverable diagnosis that perform nucleic acid amplification tests at the PoC, requiring minimal footprint by integrating sensing and computational capabilities. In response to this challenge, a paradigm shift is proposed, advocating for integrating all elements of the portable diagnostic platform under a single piece of silicon, realising a ``Diagnosis-on-a-Chip". This approach is enabled by a novel Digital ISFET Pixel that integrates both ADC and memory with sensing elements on each pixel, enhancing its parallelism. Furthermore, this architecture removes the need for external instrumentation or memories and facilitates its integration with computational capabilities on-chip, such as the proposed ARM Cortex M3 system. These computational capabilities need to be complemented with software methods that enable sensing enhancement and new applications using ISFET arrays. The second part of this thesis is devoted to these methods. Leveraging the programmability capabilities available on oscillator-based architectures, various digital signal processing algorithms are implemented to overcome the most urgent ISFET non-idealities, such as trapped charge, drift and chemical noise. These methods enable fast trapped charge cancellation and enhanced dynamic range through real-time drift compensation, achieving over 36 hours of continuous monitoring without pixel saturation. Furthermore, the recent development of data-driven models and software methods open a wide range of opportunities for ISFET sensing and beyond. In the last section of this thesis, two examples of these opportunities are explored: the optimisation of image compression algorithms on chemical images generated by an ultra-high frame-rate ISFET array; and a proposed paradigm shift on surface Electromyography (sEMG) signals, moving from data-harvesting to information-focused sensing. These examples represent an initial step forward on a journey towards a new generation of miniaturised, precise and efficient sensors for PoC diagnostics.Open Acces

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

The Prediction of Nociceptive Neural Activity in Passive Tissues following Lumbar Spine Flexion

Low back pain is a costly and debilitating disorder; however, most cases are categorized as being non-specific: low back pain without an identifiable origin or cause. Non-specific low back pain can be broadly considered and treated as either musculoskeletal disorders or pain disorders. In the musculoskeletal case, mechanics and loading history are believed to disrupt or damage tissues in the low back, which then generate nociceptive signals to be interpreted as pain. If the low back pain is a pain disorder, the disruption or damage is not with the tissues of the lower back, but rather the nervous system that transmits or interprets these nociceptive signals. Additionally, these subcategories of non-specific low back pain are not wholly independent since mechanical exposures can influence nervous system activity and vice versa. A specific outcome of this interconnectedness between mechanics and neural encoding is that a mechanical exposure can alter our ability to detect mechanical loads or mechanical sensitivity. One mechanical exposure that is linked to low back pain development and has been documented to alter neural activity is lumbar spine flexion. The purpose of this thesis was to determine the extent and mechanisms underlying how lumbar spine flexion can alter lower back mechanical sensitivity through a combination of viscoelastic creep and muscle activity, and to determine the implications those changes could have on the development of low back pain. The methods undertaken to achieve this thesis’ purpose were a combination of in-vivo human laboratory experiments, ex-vivo benchtop histology and mechanical testing, and in-silico modelling across four studies. Studies 1 and 2 quantified how mechanical sensitivity was altered over time in response to static and repetitive lumbar spine flexion respectively, Study 3 quantified the innervation properties of lumbar spine tissues, and Study 4 simulated mechanical exposures before and after lumbar spine flexion exposures to determine the nociceptive neural activity those exposures and conditions could generate. The first two studies employed a similar design and methodology measuring mechanical sensitivity and biomechanical variables before and up to 40 minutes after a 10-minute lumbar spine flexion exposure. For Study 1, the exposure was a static, seated, maximal lumbar spine flexion exposure and for Study 2, the exposure was a repetitive, standing, maximal lumbar spine flexion exposure. A custom motorized pressure algometer was constructed for these studies and used to track three measures of mechanical sensitivity—pressure-pain threshold, stimulus intensity, and stimulus unpleasantness—in the lower back and tibial shaft. Accelerometry was used in both studies to track the development and recovery from viscoelastic creep through lumbar spine flexion range of motion, and surface electromyography was used to determine flexion-relaxation (mean amplitude) in Study 1, and muscle fatigue (mean power frequency) in Study 2. Isometric joint strength and ratings of perceived exertion were also measured in Study 2. These data were fed into two main statistical processes: the first aimed to determine the time-course of mechanical sensitivity changes in the lower back relative to the tibial control site, and the second was to determine if any of the biomechanical variables (creep, muscle use, strength) or tibial mechanical sensitivities could predict lower back mechanical sensitivity changes. The static exposure generated a 10.3% creep response (4.4 ± 2.7°) in flexion range of motion that lasted for at least 40 minutes after the exposure. This exposure caused a transient increase in lower back stimulus unpleasantness but otherwise did not affect mechanical sensitivity nor did it affect flexion-relaxation. The strongest predictor of lower back mechanical sensitivity throughout the static exposure was the tibial surrogate; however, the magnitude of creep was also a significant predictor of changes in lower back pressure-pain thresholds. Despite being significant, these significant predictors could not explain the majority to the variance in mechanical sensitivity, and these changes appear more related to emotional affect than a physiological response. Study 1 concluded that a static lumbar spine flexion exposure that did not incorporate muscle activity did not alter nociceptive activity but could shape how nociceptive activity is experienced. The repetitive exposure generated a 5.0% creep response (2.7 ± 1.4°) in flexion range of motion dissipated within 5 minutes of the exposure ending. This exposure caused an immediate and transient decrease in lumbar spine extensor mean power frequency (5.1%) and lower back joint strength (9.8%) indicative of muscle fatigue, and a delayed 13.6% increase in lower back pressure-pain thresholds occurring 10 minutes after the exposure ended. Like Study 1, tibial mechanical sensitivities were the strongest predictor of lower back mechanical sensitivities, however interaction terms between these tibial surrogates and either creep magnitude or fatigue indicators (mean power frequency and strength) were also significant predictors. The delayed desensitization following this repetitive exposure was believed to arise from a combination of creep development and muscle use. The third study used lumbar spine tissues harvested from four cadaveric donors to determine the relative concentration of four neural membrane molecules (Protein Gene Product 9.5 (PGP9.5), Calcitonin Gene-Related Peptide, Bradykinin B1-Receptor, and Acid-Sensing Ion Channel 3 (ASIC3)) relevant to detecting mechanical stimuli in three tissues (dermal skin, superficial posterior annulus fibrosus, and the supraspinous-interspinous ligament complex) using Western Blotting. Only PGP9.5 and ASIC3 were found consistently in any of the three tissues. PGP9.5 had similar concentrations in skin and ligament, both of which were at least 12.8 times higher than in annular tissues. ASIC3 was most common in skin, followed by ligament, then annulus fibrosus, however the ratio of ASIC3:PGP9.5 was highest in annular tissue. The fourth study documents a model of nociceptive activity that predicts the likelihood that three exposures (pressure-pain threshold, flexion range of motion, and tissue failure) would generate nociceptive activity in the brainstem given a tissue (skin, annulus, or ligament), a viscoelastic state, ζ(t), and a muscle activity state, ϕ(t). The model simulated a single tissue-exposure combination for a sample of 100 mechanical sensitivities derived from the data in Studies 1 and 2. The model itself consisted of a Sensitivity Module that converted a tissue stress to an electrical current and a Neurological Module that used the electrical current to simulate the behaviour of a network of Hodgkin-Huxley neurons. The pressure-pain threshold exposure was used to validate the model and derive values for ζ(t) and ϕ(t), which were then applied to the other two exposures in annular and ligament tissues. While ζ(t), representing any effects related to creep following lumbar spine flexion, had minimal effects on nociceptive neural activity, ϕ(t), representing muscle activity-related effects of lumbar spine flexion, could inhibit nociceptive activity substantially. A major prediction from the model is that annulus fibrosus failure would be unlikely to generate any nociceptive activity in 12% of the population, and that characteristics of the exposure could increase that percentage to as many as 99.9% depending on the mode of failure. Flexion range of motion consistently generated no nociceptive activity in all tissues and conditions, and ligament failure consistently generated nociceptive activity regardless of other factors. While both viscoelastic creep and muscle activity related to lumbar spine flexion can influence mechanical sensitivity, the effects of muscle activity were more prominent, and could meaningfully influence the connection between tissue disruption and low back pain. These effects were most notable in exposures that have the potential to damage the annulus fibrosus

Ferroelectrets: from material science to energy harvesting and sensor applications

The purpose of this thesis is to develop innovative ferroelectrets that can be used in energy harvesting devices as well as mechanical sensors. In the first stage, the focus lies on the application of ferroelectrets as energy harvesters. The inability to control the environment where the energy harvesters will be applied, requires the use of materials that can be utilized in harsh environment such as high temperature or humidity. Therefore, new ferroelectrets based on polymers with excellent electret properties, such as fluoroethylene propylene (FEP) are developed. Two types of ferroelectrets are considered, one optimized for the longitidunal piezoelectric effect and the other one optimized for the transverse piezoelectric effect in these materials. Hereby, new void structures are achieved through thermally fusing such films so that parallel tunnels (parallel-tunnel ferroelectrets) are formed between them, or by fusing round-section FEP tubes together so that they form a band or membrane. The FEP tube configuration is optimized based on a finite element model showing that implementing a single tube structure (25 mm × 1.5 mm) as the energy harvester exhibits the largest output power. By building the energy harvester and modeling it analytically, it is demonstrated that the generated power is highly dependent on parameters such as wall thickness, load resistance, and seismic mass. Utilizing a seismic mass of 80 g at resonance frequencies around 80 Hz and an input acceleration of 1 g (9.81 m s−2), output powers up to 300 μW are reached for a transducer with 25 μm thick walls. The parallel-tunnel ferroelectrets (40 mm × 10 mm) are characterized and used in an energy harvester device based on the transverse piezoelectric effect. The energy harvesting device is an air-spaced cantilever arrangement produced by additive manufacturing technique (3D-printing). The device is tested by exposing it to sinusoidal vibrations with an acceleration a, generated by a shaker. By placing the ferroelectret at a defined distance from the neutral axis of the cantilever beam and using a proper pre-stress of the ferroelectret, an output power exceeding 1000 μW at the resonance frequency of approximately 35 Hz is reached. This demonstrates a significant improvement of air-spaced vibrational energy harvesting with ferroelectrets and greatly exceeds previous performance data for ferroelectret energy harvester of maximal 230 μW. In the second stage of the dissertation, the focus is shifted to develop ferroelectrets for chosen applications such as force myography, ultrasonic transducer and smart insole. Hereby, new arrangements and manufacturing methods are investigated to build the ferroelectret sensors. Furthermore, and following the recent requirements of eco-friendlier sensors, ferroelectrets based on polylactic acid (PLA) are investigated. PLA is a biodegradable and bioabsorbable material derived from renewable plant sources, such as corn or potato starch, tapioca roots, and sugar canes. This work relays a promising new technique in the fabrication of ferroelectrets. The novel structure is achieved through sandwiching a 3D-printed grid of periodically spaced thermoplastic polyurethane (TPU) spacers and air channels between two 12.5 μm-thick FEP films. Due to the ultra-soft TPU sections, very high quasistatic (22.000 pC N−1) and dynamic (7500 pC N−1) d33-coefficients are achieved. The isothermal stability of the d33-coefficients showed a strong dependence on poling temperature. Furthermore, the thermally stimulated discharge currents revealed well-known instability of positive charge carriers in FEP, thereby offering the possibility of stabilization by high-temperature poling. A similar approach is taken by replacing the environmentally harmful FEP by PLA. Large piezoelectric d33-coefficients of up to 2850 pC N−1 are recorded directly after charging and stabilized at about 1500 pC N−1 after approximately 50 days under ambient environmental conditions. These ferroelectrets when used for force myography to detect the slightest muscle movement when moving a finger, resulted in signal shapes and magnitudes that can be clearly distinguished from each other using simple machine learning algorithms known as Support Vector Machine (SVM) with a classification accuracy of 89.5%. Following the new manufacturing route using 3D-printing, an insole is printed using pure polypropylene filament and consists of eight independent sensors, each with a piezoelectric d33 coefficient of approximately 2000 pC N−1. The active part of the insole is protected using a 3D-printed PLA cover that features eight defined embossments on the bottom part, which focus the force on the sensors and act as overload protection against excessive stress. In addition to determining the gait pattern, an accelerometer is implemented to measure kinematic parameters and validate the sensor output signals. The combination of the high sensitivity of the sensors and the kinematic movement of the foot, opens new perspectives regarding diagnosis possibilities through gait analysis. By 3D-printing a PLA backplate and using it in combination with a bulk PLA film, a new possibility to build ultrasonic transducers is presented. The ultrasonic transducer consists of three main components all made from PLA: the film presenting the vibrating plate, the printed backplate with well-defined groves, and the printed holder. The PLA film and the printed backplate build together the ferroelectret with artificial air voids. The printed holder clamps the film on the backplate and fixes the ferroelectret together. The resulting sound pressure is measured with a calibrated microphone (Type 4138, Bruel & Kjaer) at a distance of 30 cm. The biodegradable ultrasonic transducer exhibits a large bandwidth of approximately 45 kHz and fractional bandwidth of 70%. The resulting sound pressure at the resonance frequency can be increased from 98 dB up to 106 dB for driving voltages from 30 to 70 V. respectively. The obtained theoretical and experimental results are an excellent base for further optimizing ferroelectrets to be accepted in the field of energy harvesting and mechanical sensors, where flexibility and high sensitivity are mandatory for the applications