Emerging ML-AI Techniques for Analog and RF EDA

2025/05/12 by Zhengfeng Wu, Wu, Zhengfeng, Ziyi Chen +9
Computer Science · #Computational Engineering #FOS: Computer and information sciences #Finance #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Neural Networks and Applications #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2506.00007

openalex publication_date 2025/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This survey explores the integration of machine learning (ML) into EDA workflows for analog and RF circuits, addressing challenges unique to analog design, which include complex constraints, nonlinear design spaces, and high computational costs. State-of-the-art learning and optimization techniques are reviewed for circuit tasks such as constraint formulation, topology generation, device modeling, sizing, placement, and routing. The survey highlights the capability of ML to enhance automation, improve design quality, and reduce time-to-market while meeting the target specifications of an analog or RF circuit. Emerging trends and cross-cutting challenges, including robustness to variations and considerations of interconnect parasitics, are also discussed.

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