Chips & Digital Biology

Semiconductors for molecular sensing and AI-powered health solutions

Sebastian Magierowski

Associate Professor
Dept Electrical Engineering & Computer Science
Lassonde School of Engineering, York University
LAS1012B, 4700 Keele St
Toronto, Ontario, Canada M3J 1P3

email:google me or try a link below
phone:1-416-736-2100 x44652
EECS, Micro/Nanoelectronics, Biomedical, EMIL Lab, EMIL Wiki, EMIL Repo, Sequuntur, Sequential Machines
Likeness

Research

We invent CMOS chips that sense and process biosignals, embedding AI hardware for real-time analysis and interpretation. Our research emphasizes low-power semiconductors for mobile digital biology applications.

Packaged Chip

One of our custom BioSoCs

Full-stack bio system-on-chip (BioSoC) approach:
Sensors: High-sensitivity CMOS-based sensors for detecting biological signals with precision. Here we're mostly focused on DNA and cellular signal capture.
Sensors
Analog: Custom analog front-ends for signal amplification and filtering, ensuring accurate data capture. We're particularly interested in leveraging advanced CMOS nodes for this work. High-speed I/O and on-chip power management are critical concerns.
Analog
Digital: RISC-V processors and accelerators for bioinformatics and deep learning, optimized for low-power systems. Our insight into the specific needs of digital biology allows us to precisely trim the hardware for extremely low-power operation without excessive performance sacrifices.
Digital
Software: None of the hardware above is of much use unless targeted with excellent software. We make Linux-based systems running custom algorithms for real-time molecular diagnostics and analysis.
Software