Emerging ML-AI Techniques for Analog and RF EDA
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913868578553856 |
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| author | Wu, Zhengfeng Chen, Ziyi Achebe, Nnaemeka Rao, Vaibhav V. Shrestha, Pratik Savidis, Ioannis |
| author_facet | Wu, Zhengfeng Chen, Ziyi Achebe, Nnaemeka Rao, Vaibhav V. Shrestha, Pratik Savidis, Ioannis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emerging ML-AI Techniques for Analog and RF EDA Wu, Zhengfeng Chen, Ziyi Achebe, Nnaemeka Rao, Vaibhav V. Shrestha, Pratik Savidis, Ioannis Hardware Architecture Computational Engineering, Finance, and Science Machine Learning 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. |
| title | Emerging ML-AI Techniques for Analog and RF EDA |
| topic | Hardware Architecture Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2506.00007 |