ML-Assisted RF IC Design Enablement: the New Frontier of AI for EDA
Hyunsu Chae, Song Hang Chai, Taiyun Chi, Sensen Li, David Zhigang Pan · 2025
While AI for EDA has seen great success in digital IC design and some success in analog design, its potential for enabling RFIC design is yet to be fully explored. Due to its high-frequency nature, RFIC involves challenges such as parasitic effects, electromagnetic interference (EMI), signal integrity (SI), and other non-idealities. The modeling of passive networks and the associated computationally expensive EM simulations remain the major bottleneck in manual RFIC designs. This paper discusses the challenges and opportunities in ML-assisted RFIC design, covering topics from physics-augmented surrogate modeling to the inverse design of passive structures.