用机器学习提升模拟/射频电路设计自动化与效率
Emerging ML-AI Techniques for Analog and RF EDA
- 将机器学习融入模拟/射频EDA流程，解决非线性与高成本问题
- 可自动完成拓扑生成、参数优化等任务，缩短研发周期
- 适合芯片设计工程师与EDA工具开发者参考
本文综述了机器学习在模拟与射频电路电子设计自动化（EDA）工作流中的应用，针对模拟设计特有的复杂约束、非线性设计空间和高计算成本等挑战，梳理了最先进的学习与优化技术。这些技术适用于电路约束建模、拓扑生成、器件建模、尺寸优化、布局布线等任务。研究表明，机器学习能有效提升设计自动化程度，改善设计质量，并加速产品上市进程，同时满足目标性能指标。文章还探讨了新兴趋势及共性挑战，如对工艺波动的鲁棒性、互连寄生效应的考量等。
原文摘要 · Abstract (English)
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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