Data-Efficient Supervised Learning for RF and mm-Wave Circuit Design: Techniques, Challenges, and Benefits

Modern radio-frequency (RF) and millimeter-wave (mm-wave) circuits are becoming increasingly complex to meet stringent performance requirements for high-speed communications, radar, imaging, and sensing. Manual circuit design flows depend heavily on expert knowledge and lengthy simulations to iteratively tune numerous parameters. Recent advances in machine learning (ML), particularly supervised learning, offer a promising avenue for automating and accelerating circuit design. This paper synthesizes and expands on insights from recent developments, demonstrating how supervised ML models can map performance specifications to optimal circuit parameters. We also delve into technical results from two recent works to highlight reported error rates, data efficiency, and approaches to multi-block circuit design. Key challenges-such as limited data availability, non-unique mappings, and out-of-distribution generalization-are discussed, alongside future directions in datadriven RF design.