Low-power CMOS digital-pixel Imagers for high-speed uncooled PbSe IR applications

This PhD dissertation describes the research and development of a new low-cost medium wavelength infrared MWIR monolithic imager technology for high-speed uncooled industrial applications. It takes the baton on the latest technological advances in the field of vapour phase deposition (VPD) PbSe-based medium wavelength IR (MWIR) detection accomplished by the industrial partner NIT S.L., adding fundamental knowledge on the investigation of novel VLSI analog and mixed-signal design techniques at circuit and system levels for the development of the readout integrated device attached to the detector. The work supports on the hypothesis that, by the use of the preceding design techniques, current standard inexpensive CMOS technologies fulfill all operational requirements of the VPD PbSe detector in terms of connectivity, reliability, functionality and scalability to integrate the device. The resulting monolithic PbSe-CMOS camera must consume very low power, operate at kHz frequencies, exhibit good uniformity and fit the CMOS read-out active pixels in the compact pitch of the focal plane, all while addressing the particular characteristics of the MWIR detector: high dark-to-signal ratios, large input parasitic capacitance values and remarkable mismatching in PbSe integration. In order to achieve these demands, this thesis proposes null inter-pixel crosstalk vision sensor architectures based on a digital-only focal plane array (FPA) of configurable pixel sensors. Each digital pixel sensor (DPS) cell is equipped with fast communication modules, self-biasing, offset cancellation, analog-to-digital converter (ADC) and fixed pattern noise (FPN) correction. In-pixel power consumption is minimized by the use of comprehensive MOSFET subthreshold operation. The main aim is to potentiate the integration of PbSe-based infra-red (IR)-image sensing technologies so as to widen its use, not only in distinct scenarios, but also at different stages of PbSe-CMOS integration maturity. For this purpose, we posit to investigate a comprehensive set of functional blocks distributed in two parallel approaches: • Frame-based “Smart” MWIR imaging based on new DPS circuit topologies with gain and offset FPN correction capabilities. This research line exploits the detector pitch to offer fully-digital programmability at pixel level and complete functionality with input parasitic capacitance compensation and internal frame memory. • Frame-free “Compact”-pitch MWIR vision based on a novel DPS lossless analog integrator and configurable temporal difference, combined with asynchronous communication protocols inside the focal plane. This strategy is conceived to allow extensive pitch compaction and readout speed increase by the suppression of in-pixel digital filtering, and the use of dynamic bandwidth allocation in each pixel of the FPA. In order make the electrical validation of first prototypes independent of the expensive PbSe deposition processes at wafer level, investigation is extended as well to the development of affordable sensor emulation strategies and integrated test platforms specifically oriented to image read-out integrated circuits. DPS cells, imagers and test chips have been fabricated and characterized in standard 0.15μm 1P6M, 0.35μm 2P4M and 2.5μm 2P1M CMOS technologies, all as part of research projects with industrial partnership. The research has led to the first high-speed uncooled frame-based IR quantum imager monolithically fabricated in a standard VLSI CMOS technology, and has given rise to the Tachyon series [1], a new line of commercial IR cameras used in real-time industrial, environmental and transportation control systems. The frame-free architectures investigated in this work represent a firm step forward to push further pixel pitch and system bandwidth up to the limits imposed by the evolving PbSe detector in future generations of the device.La present tesi doctoral descriu la recerca i el desenvolupament d'una nova tecnologia monolítica d'imatgeria infraroja de longitud d'ona mitja (MWIR), no refrigerada i de baix cost, per a usos industrials d'alta velocitat. El treball pren el relleu dels últims avenços assolits pel soci industrial NIT S.L. en el camp dels detectors MWIR de PbSe depositats en fase vapor (VPD), afegint-hi coneixement fonamental en la investigació de noves tècniques de disseny de circuits VLSI analògics i mixtes pel desenvolupament del dispositiu integrat de lectura unit al detector pixelat. Es parteix de la hipòtesi que, mitjançant l'ús de les esmentades tècniques de disseny, les tecnologies CMOS estàndard satisfan tots els requeriments operacionals del detector VPD PbSe respecte a connectivitat, fiabilitat, funcionalitat i escalabilitat per integrar de forma econòmica el dispositiu. La càmera PbSe-CMOS resultant ha de consumir molt baixa potència, operar a freqüències de kHz, exhibir bona uniformitat, i encabir els píxels actius CMOS de lectura en el pitch compacte del pla focal de la imatge, tot atenent a les particulars característiques del detector: altes relacions de corrent d'obscuritat a senyal, elevats valors de capacitat paràsita a l'entrada i dispersions importants en el procés de fabricació. Amb la finalitat de complir amb els requisits previs, es proposen arquitectures de sensors de visió de molt baix acoblament interpíxel basades en l'ús d'una matriu de pla focal (FPA) de píxels actius exclusivament digitals. Cada píxel sensor digital (DPS) està equipat amb mòduls de comunicació d'alta velocitat, autopolarització, cancel·lació de l'offset, conversió analògica-digital (ADC) i correcció del soroll de patró fixe (FPN). El consum en cada cel·la es minimitza fent un ús exhaustiu del MOSFET operant en subllindar. L'objectiu últim és potenciar la integració de les tecnologies de sensat d'imatge infraroja (IR) basades en PbSe per expandir-ne el seu ús, no només a diferents escenaris, sinó també en diferents estadis de maduresa de la integració PbSe-CMOS. En aquest sentit, es proposa investigar un conjunt complet de blocs funcionals distribuïts en dos enfocs paral·lels: - Dispositius d'imatgeria MWIR "Smart" basats en frames utilitzant noves topologies de circuit DPS amb correcció de l'FPN en guany i offset. Aquesta línia de recerca exprimeix el pitch del detector per oferir una programabilitat completament digital a nivell de píxel i plena funcionalitat amb compensació de la capacitat paràsita d'entrada i memòria interna de fotograma. - Dispositius de visió MWIR "Compact"-pitch "frame-free" en base a un novedós esquema d'integració analògica en el DPS i diferenciació temporal configurable, combinats amb protocols de comunicació asíncrons dins del pla focal. Aquesta estratègia es concep per permetre una alta compactació del pitch i un increment de la velocitat de lectura, mitjançant la supressió del filtrat digital intern i l'assignació dinàmica de l'ample de banda a cada píxel de l'FPA. Per tal d'independitzar la validació elèctrica dels primers prototips respecte a costosos processos de deposició del PbSe sensor a nivell d'oblia, la recerca s'amplia també al desenvolupament de noves estratègies d'emulació del detector d'IR i plataformes de test integrades especialment orientades a circuits integrats de lectura d'imatge. Cel·les DPS, dispositius d'imatge i xips de test s'han fabricat i caracteritzat, respectivament, en tecnologies CMOS estàndard 0.15 micres 1P6M, 0.35 micres 2P4M i 2.5 micres 2P1M, tots dins el marc de projectes de recerca amb socis industrials. Aquest treball ha conduït a la fabricació del primer dispositiu quàntic d'imatgeria IR d'alta velocitat, no refrigerat, basat en frames, i monolíticament fabricat en tecnologia VLSI CMOS estàndard, i ha donat lloc a Tachyon, una nova línia de càmeres IR comercials emprades en sistemes de control industrial, mediambiental i de transport en temps real.Postprint (published version

Real-time smart multisensing wearable platform for monitoring sweat biomarkers during exercise

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise in hot and humid conditions. Real-time noninvasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. In this work, we describe a platform that in- cludes different sweat biomonitoring prototypes of cost-effective, smart wearable devices for continuous biomonitoring of sweat during exercise. One prototype is based on conformable and disposable soft sensing patches with an integrated multi-sensor array requiring the integration of different sensors and printed sensors with their corresponding functionalization protocols on the same substrate. The second is based on silicon based sensors and paper microfluidics. Both platforms integrate a multi-sensor array for measuring sodium, potassium, and pH in sweat. We show preliminary results obtained from the multi-sensor prototypes placed on two athletes during exercise. We also show that the machine learning algorithms can predict the percentage of body weight loss during exercise from biomarkers such as heart rate and sweat sodium concentration collected over multiple subjects

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker. The system was placed on subjects carrying out an hour-long exercise session and results were compared to a wearable system using potentiometric robust silicon-based sensors and to commercially available HORIBA-LAQUAtwin devices. Both prototypes were applied to the real-time monitoring of sweat during cycling sessions and showed stable readings for around an hour. Analysis of the sweat biomarkers collected from the printed patch prototype shows that their real-time measurements correlate well (correlation coefficient ≥0.65 ) with other physiological biomarkers such as heart rate and regional sweat rate collected in the same session. We show for the first time, that the real-time sweat sodium and potassium concentration biomarker measurements from the printed sensors can be used to predict the core body temperature with root mean square error (RMSE) of 0.02 °C which is 71% lower compared to the use of only the physiological biomarkers. These results show that these wearable patch technologies are promising for real-time portable sweat monitoring analytical platforms, especially for athletes performing endurance exercise

Modular neural sensing system for predicting physicochemical properties

A modular artificial neural sensing system (1) comprising a hierarchical network of neural sensing units (3) comprising a neuromimetic sensor array (5) of artificial sensory synapses (Ws, W,in) and sensory neurons (N,in) for receiving physicochemical sensed signals (SS) and for outputting sensor output signals; an artificial neural network processor (7) for processing the sensor output signals, the processor (7) comprising processor neurons (Nrec) interconnected by processor synapses (Wrecu, Wrecs) forming first connections (WreCu) and second connections (Wrecs), the processor (7) being configured to output processor output signals; a first sensor interface (9) for feeding processed or unprocessed sensed signals (SS) into the processor (7); a second sensor interface (19) for receiving output predicted signals (PS') from other neural sensing units (3), and for feeding processed or unprocessed output predicted signals (PS') into the processor (7); a signal decoder (11, 13) for decoding the processor output signals, and for outputting decoder output signals (PS); an error feedback module (15) configured to receive the decoder output signals (PS) and teaching signals (TS) for generating error signals depending on a difference between teaching signals (TS) and decoder output signals (PS). The first connections (Wrecu) can be locally trained by at least any of the processed or unprocessed physicochemical sensed signals (SS) and/or any of the processed or unprocessed output predicted signals (PS'). The second connections (WreCs) can be trained by at least any of the error signals.Peer reviewedUniversität Zürich, Consejo Superior de Investigaciones CientíficasA1 Solicitud de patente con informe sobre el estado de la técnic

Dispositif de détection électrochimique neurocomputationnel pour prédire les propriétés d'une substance

A neurocomputational electrochemical sensing device (1) is proposed for predicting properties of a substance. The device (1) comprises: a plurality of electrochemical sensors constituting a sensor array (3), the sensors being sensitive to sensed attributes advantageous to predict a set of properties of interest of the substance, each sensor being configured to output a sensor output signal indicative of a sensor response of the respective sensor to measurable changes in the sensed attributes of the substance; a readout circuit (5) for biasing the sensors and for conditioning the sensor output signals into readout circuit output signals to facilitate further processing of the sensor responses; and an artificial neural network processor (7) for processing the readout circuit output signals, the processor (7) comprising neurons interconnected by synapses, the processor (7) being configured to output a set of processor output signals whose signal values are indicative of the properties to predict. The sensor array (3) comprises first electrochemical sensors selective to properties correlated with the desired properties to predict, and second electrochemical sensors sensitive primarily to the main interferents of the substance. The neurons and/or the synapses are configured to be trained to compensate for sensor drift and/or cross-sensitivities upon generating the processor output signals.Peer reviewedUniversität Zürich, Consejo Superior de Investigaciones CientíficasA1 Solicitud de patente con informe sobre el estado de la técnic

Dispositif de détection électrochimique par calcul neuronal pour prédire des propriétés d'une substance

[EN] A neurocomputational electrochemical sensing device (1) is proposed for predicting properties of a substance. The device (1) comprises: a plurality of electrochemical sensors constituting a sensor array (3), the sensors being sensitive to sensed attributes advantageous to predict a set of properties of interest of the substance, each sensor being configured to output a sensor output signal indicative of a sensor response of the respective sensor to measurable changes in the sensed attributes of the substance; a readout circuit (5) for biasing the sensors and for conditioning the sensor output signals into readout circuit output signals to facilitate further processing of the sensor responses; and an artificial neural network processor (7) for processing the readout circuit output signals, the processor (7) comprising neurons interconnected by synapses, the processor (7) being configured to output a set of processor output signals whose signal values are indicative of the properties to predict. The sensor array (3) comprises first electrochemical sensors selective to properties correlated with the desired properties to predict, and second electrochemical sensors sensitive primarily to the main interferents of the substance. The neurons and/or the synapses are configured to be trained to compensate for sensor drift and/or cross-sensitivities upon generating the processor output signals.[FR] L'invention concerne un dispositif de détection électrochimique par calcul neuronal (1) pour prédire des propriétés d'une substance. Le dispositif (1) comprend : une pluralité de capteurs électrochimiques constituant un réseau de capteurs (3), les capteurs étant sensibles à des attributs détectés permettant avantageusement de prédire un ensemble de propriétés d'intérêt de la substance, chaque capteur étant conçu pour émettre un signal de sortie de capteur indiquant une réponse de capteur du capteur respectif à des changements mesurables dans les attributs détectés de la substance ; un circuit de relevé (5) pour polariser les capteurs et pour conditionner les signaux de sortie de capteur en signaux de sortie de circuit de relevé afin de faciliter un traitement ultérieur des réponses de capteur ; et un processeur de réseau de neurones artificiels (7) pour traiter les signaux de sortie de circuit de relevé, le processeur (7) comprenant des neurones interconnectés par des synapses, le processeur (7) étant conçu pour émettre un ensemble de signaux de sortie de processeur dont les valeurs de signal indiquent les propriétés à prédire. Le réseau de capteurs (3) comprend des premiers capteurs électrochimiques sélectifs de propriétés corrélées aux propriétés à prédire souhaitées, et des seconds capteurs électrochimiques sensibles principalement aux interférents principaux de la substance. Les neurones et/ou les synapses sont conçus pour être entraînés à compenser la dérive et/ou les sensibilités croisées des capteurs lors de la génération des signaux de sortie de processeur.Peer reviewedUniversität Zürich, Consejo Superior de Investigaciones CientíficasA1 Solicitud de patente con informe sobre el estado de la técnic

Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays

Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15x more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178x lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor.ISSN:1662-453XISSN:1662-454

Live Demonstration: A Portable Microsensor Fusion System with Real-Time Measurement for On-Site Beverage Tasting

We demonstrate a portable multisensor fusion system for the automated analysis of multiple beverages. The system makes use of compact and low-power-consumption electronic equipment to simultaneously read out an array of microsensors formed by six ion-selective field-effect transistors (ISFETs), one conductivity sensor, one redox potential sensor, and two ampero-metric microelectrodes. A custom Python application running on a laptop computer receives real-time multivariate data via USB, and provides chemometric models to classify different varieties and to quantify relevant parameters of mineral water and wine. The software also includes a graphical user interface (GUI) to visualize readouts and analytical estimates

Cyber-Physical Systems and Internet of Things

Cyber-Physical Systems (CPS) and Internet of Things (IoT ) are complementary paradigms in digitalization. Sensors and actuators, hardware designs and development platforms, architectures and computational frameworks, modeling, control and optimization, and potential applications are analyzed and presented from impact and main challenges up to strategic plan.Peer reviewe