AI加速模拟电路设计，实现参数优化与系统级协同调优。
AI-Powered Agile Analog Circuit Design and Optimization
- 用多目标贝叶斯优化自动调参，直接优化晶体管尺寸。
- 在关键词识别场景中，联合优化模拟带通滤波器性能。
- 适合需要快速迭代的模拟前端设计人员参考。
人工智能技术正重塑模拟电路设计，实现器件级调优与系统级协同优化。本文结合两种方法：(1) 基于多目标贝叶斯优化（MOBO）的晶体管尺寸自动调整，应用于线性可调跨导放大器；(2) 将AI集成到电路传递函数建模中，在关键词识别（KWS）应用中，通过机器学习训练循环优化模拟带通滤波器。综合结果表明，AI能提升模拟电路性能，减少设计迭代成本，并同步优化模拟组件与应用指标。
原文摘要 · Abstract (English)
Artificial intelligence (AI) techniques are transforming analog circuit design by automating device-level tuning and enabling system-level co-optimization. This paper integrates two approaches: (1) AI-assisted transistor sizing using Multi-Objective Bayesian Optimization (MOBO) for direct circuit parameter optimization, demonstrated on a linearly tunable transconductor; and (2) AI-integrated circuit transfer function modeling for system-level optimization in a keyword spotting (KWS) application, demonstrated by optimizing an analog bandpass filter within a machine learning training loop. The combined insights highlight how AI can improve analog performance, reduce design iteration effort, and jointly optimize analog components and application-level metrics.
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