AI-Enhanced RF/Mixed-Signal Circuits for Reliable Operations

ABSTRACT

AI-driven design and optimization are revolutionizing RF and mixed-signal circuits for operation in extreme environments, including high radiation and wide temperature ranges. This talk explores the use of reinforcement learning (RL) and generative models to improve circuit robustness and adaptability. RL-based self-healing techniques leverage embedded electromagnetic sensors for real-time monitoring and dynamic fault recovery while generative models accelerate design space exploration, enabling resilient and efficient circuit topologies. The presentation will highlight AI-enhanced designs such as adaptive power amplifiers, PMICs, and multispectral sensors that enhance performance and reliability in harsh environments.

BIOGRAPHY

Vanessa Chen received her Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2013, where she worked on energy-efficient, ultra-high-speed ADCs with real-time calibration and interned at IBM T. J. Watson Research Center. She previously held circuit design roles at Qualcomm in San Diego and Realtek in Taiwan, focusing on self-healing RF and mixed-signal circuits. Her research explores AI-enhanced circuits and systems, including intelligent sensory interfaces, RF/mixed-signal hardware security, and ubiquitous sensing and computing. Dr. Chen is a recipient of the NSF CAREER Award, the CMU College of Engineering Dean’s Early Career Fellowship, and the IBM PhD Fellowship. She has served on program committees for ISSCC, VLSI, CICC, A-SSCC, and DAC, as an Associate Editor for several IEEE journals, and is currently an IEEE SSCS Distinguished Lecturer for 2025–2026.