Researchers at Princeton have developed AI-driven methods to automate the design of radio-frequency integrated circuits (RFICs), a process traditionally considered a 'dark art' requiring years of expert experience. Using reinforcement learning to determine circuit architecture and topology from scratch, combined with an AI emulator that predicts electromagnetic behavior without running full simulations, the team produced a 5G millimeter-wave power amplifier that outperformed state-of-the-art human designs. They also applied diffusion models to make AI-generated circuit structures more interpretable by engineers. The approach eliminates reliance on human-designed templates, dramatically reduces design time from months to minutes, and has been validated for multiport circuits and sub-terahertz amplifiers. Key remaining challenges include reducing AI hallucinations, improving generalizability, and building open datasets — since most simulation data is locked behind NDAs.
