What type of study is this?

This is a Literature Review study.

October 8, 2025Open Access

Emerging ML-AI Techniques for Analog and RF EDA

Key Points

Machine learning enhances automation and improves design quality in analog circuit workflows, especially for RF applications.
Optimization techniques reviewed include constraint formulation and device modeling tailored for complex analog design issues.
The survey highlights emerging cross-cutting challenges, such as robustness to variations in circuit design and interconnect parasitics.
Addressing high computational costs and nonlinear design spaces is critical for advancing EDA capabilities in analog and RF circuits.

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.

Read Full Paperexternally

KI fragen

Bookmark

View Full Paper