Emerging ML-AI Techniques for Analog and RF EDA
It addresses automation challenges for analog and RF circuit designers, but it is incremental as it reviews existing techniques rather than introducing new methods.
This survey explores how machine learning can be integrated into EDA workflows for analog and RF circuits to tackle challenges like complex constraints and high computational costs, highlighting its potential to enhance automation, improve design quality, and reduce time-to-market.
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.