AI-Driven Integrated Circuit Design: A Survey of Techniques, Challenges, and Opportunities

Analog and radio-frequency integrated circuit (RFIC) design has traditionally been defined by inherent complexity, reliance on manual iterations, and dependence on domain-specific heuristics. Recently, however, this landscape has undergone a transformative shift driven by advances in Artificial Intelligence (AI) and Machine Learning (ML). This survey provides a systematic and forward-looking review of AI-enabled methodologies—including evolutionary algorithms, Bayesian optimization, reinforcement learning, deep learning, and large language models—applied to key design and measurement stages: circuit topology and structure synthesis, circuit optimization, layout automation, and post-silicon calibration and fault diagnosis. By mapping these techniques to each phase of the analog and RFIC development pipeline, we identify emerging trends, persistent challenges such as generalization and data efficiency, and the trajectory toward fully autonomous, scalable, and innovation-driven analog/RFIC design. Beyond circuit design, this evolving ecosystem is also expected to reshape the broader Electronic Design Automation (EDA) landscape. This article serves as a comprehensive reference for academic researchers and industry practitioners seeking to leverage AI in next-generation circuit and system design.