Preprint
Emerging ML-AI Techniques for Analog and RF EDA
arXiv.org
12 May 2025
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.
Metrics
14 Record Views
Details
- Title
- Emerging ML-AI Techniques for Analog and RF EDA
- Creators
- Zhengfeng Wu - Drexel UniversityZiyi Chen - Drexel UniversityNnaemeka Achebe - Drexel UniversityVaibhav V Rao - Drexel UniversityPratik Shrestha - Drexel UniversityIoannis Savidis - Drexel University
- Publication Details
- arXiv.org
- Number of pages
- 9
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; College of Engineering
- Other Identifier
- 991022055174304721