用多模态大模型实现模拟电路自动设计与优化
AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs
- 通过仿真错误和波形图反馈，自动修复电路设计缺陷
- 在13类电路中成功设计28个，性能优于现有方法
- 适合集成电路设计工程师快速构建复杂系统
尽管近年来取得进展，模拟前端设计仍严重依赖专家经验与迭代仿真，限制了自动化潜力。我们提出AnalogCoder-Pro，一种融合生成与优化技术的多模态大语言模型框架。该框架采用多模态诊断-修复反馈回路，利用仿真错误信息和波形图像自主修正设计缺陷；同时通过归档成功设计为模块化子电路，建立可复用的电路工具库，加速复杂系统开发。此外，它实现端到端自动化：从目标规格生成电路拓扑，提取关键参数，并应用贝叶斯优化进行器件尺寸调节。在涵盖13类电路的精选基准测试集上，AnalogCoder-Pro成功设计出28个电路，各项性能指标持续优于现有基于LLM的方法。
原文摘要 · Abstract (English)
Despite recent advances, analog front-end design still relies heavily on expert intuition and iterative simulations, which limits the potential for automation. We present AnalogCoder-Pro, a multimodal large language model (LLM) framework that integrates generative and optimization techniques. The framework features a multimodal diagnosis-and-repair feedback loop that uses simulation error messages and waveform images to autonomously correct design errors. It also builds a reusable circuit tool library by archiving successful designs as modular subcircuits, accelerating the development of complex systems. Furthermore, it enables end-to-end automation by generating circuit topologies from target specifications, extracting key parameters, and applying Bayesian optimization for device sizing. On a curated benchmark suite covering 13 circuit types, AnalogCoder-Pro successfully designed 28 circuits and consistently outperformed existing LLM-based methods in figures of merit.
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