Semiconductors for molecular sensing and AI-powered health solutions
Main Chips Labs Pubs Courses Digital Biology|
Sebastian Magierowski
Associate Professor
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ResearchIn my lab we invent advanced CMOS chips that sense and process biological signals, integrating AI hardware for real-time analysis and interpretation. Our research emphasizes low-power, embedded systems optimized for mobile digital biology applications. |
One of our custom BioSoCs |
| Ours is a full-stack bio system-on-chip (BioSoC) approach integrating four key areas: | |
| Sensors: High-sensitivity CMOS-based sensors for detecting biological signals with precision. Here we're mostly focused on DNA and cellular signal capture. |
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| 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. |
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| 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. |
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| 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. |
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