Flynn research Group
Welcome to the Flynn Research Group Website. The Flynn Research Group is a part of the Electrical Engineering and Computer Science Department at the University of Michigan.
Our main interest is in analog and mixed-signal circuits, analog-to-digital conversion, and other interface circuits. These include high-speed serial transceivers, RF transceivers and sensors. Our main focus is on circuits that transfer information between the analog and digital domains.
See more about our journal and conference papers.
The Flynn Group is made up of both graduate and undergraduate students with a range of interests within circuit design.
Mike Flynn named Fawwaz T. Ulaby Collegiate Professor of Electrical and Computer Engineering
Mike Flynn was named the Fawwaz T. Ulaby Collegiate Professor of Electrical and Computer Engineering in recognition of his outstanding contributions in the areas of research, education, and leadership.
“Fawwaz inspires me because he is the perfect professor,” Flynn said. “Not only does he have an amazing research track record, but he also excels in service and is a tremendous educator. His genuine concern for students sets an example for us all.”
Flynn is one of the world’s premier scholars in the area of analog and mixed-signal integrated circuits and systems, analog-to-digital conversion (ADC), and other interface circuits, from high-speed serial transceivers to radio frequency transceivers and sensors. His pioneering research has improved the performance and energy efficiency of analog-digital interfaces and transformed the field.
Evelyn Ware recognized by 2022 NSF Graduate Research Fellowship program
Evelyn Ware’s research explores how we can use machine learning algorithms to calibrate analog-to-digital converters. ADCs make it possible to take in analog information and then process it digitally, which is required in such disparate applications as medical imaging, 5G networks, GPS, radar and communication systems, and sensor interfaces. Calibration is often necessary to improve performance.
“Artificial intelligence and deep neural networks provide the next frontier for ADC architecture and calibration,” said Ware. “Using DNNs for calibration of ADCs has been explored in simulation but there is a striking lack of prior literature surrounding practical implementations of DNN-ADC calibration schemes on chip, which is essential for their use in real-world systems.”
Ware presented her work at the SRC TECHCON conference in 2021.