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Biomedical circuits and sensor interfaces
MICAS performs research on innovative integrated circuits with application in all possible fields. Two crucial application domains that receive special attention are sensing readout and biomedical. Indeed, sensors are the front door bridging the physical world with the electronics world. Multimedia, augmented/virtual reality, modern cars, in-house heating and entrance control, robotics, current manufacturing plants, etc. are examples where sensing (including cameras) is ubiquitous. Enabled by the increasing capabilities of (wireless) communications, like the Internet of Things, distributed sensing devices have recently become key to constructing smart system applications, like smart cars, smart mobility, smart houses, smart offices, smart cities, smart manufacturing, smart farming, environmental monitoring, etc. Challenges in the design of the sensing readout involve the reduction of power and area, while achieving increased performances in terms of sensitivity or dynamic range. The large amount of data generated in the sensing also calls for increased local data processing or computation “in the edge” to limit the communication cost. An application field that has always been of special interest in MICAS is biomedical. Electronics offers huge opportunities for improved diagnosis, increased monitoring, therapeutic stimulation, targeted drug delivery and more personalized medicine in general, saving and/or improving lives. Biomedical devices today range from large to handheld equipment and from wearables to implantables and ingestibles. Electronics not only allows for miniaturization but also adds intelligence and customization capabilities. For decades, the research at MICAS has been exploring improved chip implementations and innovative hardware solutions for biomedical applications, often in collaboration with medical doctors from the university hospital.
Research challenges
To enable the next wave of biomedical circuits and sensor interfaces, several research challenges need to be addressed in the coming years, mainly focusing on further miniaturization and persistently expanding functionality and performance. Researchers at MICAS actively strive to work on the following challenges for the next 5 years.
Direct sensor to digital readout
MICAS has a renowned tradition in developing innovative sensor readout architectures. For many application domains (e.g. automotive, internet of things, multimedia, etc.), traditional sensor readout architectures with amplification and data conversion lead to large power and area consumptions in advanced CMOS technologies. Therefore, a recent focus are solutions that directly convert the physical sensor signal into digital output. The research explores different novel design techniques such as time-encoding highly-digital implementations that avoid traditional bulky analog circuits. This results in ultra-small die areas and therefore low cost, without sacrificing performance, while on the other hand adding flexibility for additional on-sensor data processing. Also the need for sensor readout multiplexing and for absolute robustness (including drift and EMI resilience) are addressed in the research.
Smart sensing with information processing in the edge
The increasing use of distributed and connected sensors that operate almost on a continuous base generates huge amounts of data, which not only contains far less information content but also requires huge power for sensing and data transmission. Smart sensors exploit intelligent techniques “in the edge” to reduce this data deluge, without losing information content. MICAS fully investigates the capabilities and limitations of achieving this objective by exploiting event-driven readout techniques, lossless signal compression, local data processing in traditional and spiking neural networks, etc., all embedded in the smart sensor. Not only the algorithms are being investigated, but also the power- and area-efficient implementation in hardware.
From wearable to implantable and ingestible biomedical circuits
MICAS has a decades-long tradition in research on innovative circuits and systems for all kinds of biomedical applications, both on-body and in-body. Current trend is to continue the miniaturization, allowing to expand the capabilities and performance of implantable and ingestible biomedical devices. Targets are not only the analysis of physical and biochemical signals, but also of mental wellbeing and stress. Wireless, inductive or ultrasound powering and data communication are key, besides improved sensing (sensitivity and selectivity) and the (local) processing of the data. MICAS combines both innovative system and circuit research to design advanced biomedical solutions beyond the state of the art.
Several applications (e.g. biochemical essays, neural recording, etc.) require the readout of large numbers of parallel electrodes or measurement sites. MICAS carries out research at both system and circuit level to further enable increasing the readout density of such massively parallel applications, both for silicon and flexible technologies. Another intrinsically parallel application are imagers, where innovative readout architectures and circuit solutions are being investigated to improve performance, in particular exploiting the extra flexibility offered by 3D-stacked technologies.
High speed, low power in-body and body to surface communications
Ever more implants are envisioned for medical diagnosis and treatment. Visual prostheses, deep brain stimulation for pain treatement and bladder stimulation are only a few examples. All these implants need data communication through the human body. That remains a challenge, especially when data rates increase due to transmission of video from or to an implant. MICAS dives into this with a focus on ultrasound based communication.
A. Safa, A. Bourdoux, I. Ocket, F. Catthoor and G. G. E. Gielen, "On the Use of Spiking Neural Networks for Ultralow-Power Radar Gesture Recognition," in IEEE Microwave and Wireless Components Letters, doi: 10.1109/LMWC.2021.3125959.
A. Safa et al., "Improving the Accuracy of Spiking Neural Networks for Radar Gesture Recognition Through Preprocessing," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3109958.
X. Huang et al., "A Compact, Low-Power Analog Front-End with Event-Driven Input Biasing for High-Density Neural Recording in 22-nm FDSOI," in IEEE Transactions on Circuits and Systems II: Express Briefs, doi: 10.1109/TCSII.2021.3111257.
J. Pelgrims, K. Myny and W. Dehaene, "A 36V Ultrasonic Driver for Haptic Feedback Using Advanced Charge Recycling Achieving 0.20CV2f Power Consumption," ESSCIRC 2021 - IEEE 47th European Solid State Circuits Conference (ESSCIRC), 2021, pp. 159-162, doi: 10.1109/ESSCIRC53450.2021.9567765.
E. Sacco, J. Vergauwen and G. Gielen, "A 16.1-bit Resolution 0.064-mm2 Compact Highly Digital Closed-Loop Single-VCO-Based 1-1 Sturdy-MASH Resistance-to-Digital Converter With High Robustness in 180-nm CMOS," in IEEE Journal of Solid-State Circuits, vol. 55, no. 9, pp. 2456-2467, Sept. 2020, doi: 10.1109/JSSC.2020.2987692.
B. Baran, H. Pues and W. Dehaene, "Design of an Automotive Sensor Readout Class AB CMOS Amplifier for Maximum Robustness Against Transient Electromagnetic Interference," 2020 International Symposium on Electromagnetic Compatibility - EMC EUROPE, 2020, pp. 1-6, doi: 10.1109/EMCEUROPE48519.2020.9245863.
J. Van Assche and G. Gielen, "Power Efficiency Comparison of Event-Driven and Fixed-Rate Signal Conversion and Compression for Biomedical Applications," in IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 4, pp. 746-756, Aug. 2020, doi: 10.1109/TBCAS.2020.3009027.
J. Marin, E. Sacco, J. Vergauwen and G. Gielen, "A Robust BBPLL-Based 0.18-m CMOS Resistive Sensor Interface With High Drift Resilience Over a −40°C–175°C Temperature Range," in IEEE Journal of Solid-State Circuits, vol. 54, no. 7, pp. 1862-1873, July 2019, doi: 10.1109/JSSC.2019.2911888.
T. Bos, M. Verhelst, W.Dehaene "A Flexible End-to-End Dual ASIC Transceiver for OFDM Ultrasound In-Body Communication," 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2022, pp. 1-4, doi: 10.1109/BioCAS54905.2022.9948567
Current research topics
