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arXiv · 2508.02518

AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs

Yao Lai·Souradip Poddar·Sungyoung Lee·Guojin Chen·Mengkang Hu·Bei Yu·Ping Luo·David Z. Pan
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Abstract

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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Yao Lai, Souradip Poddar, Sungyoung Lee, Guojin Chen, Mengkang Hu, Bei Yu, Ping Luo, David Z. Pan. 2025-08-31. AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs. https://arxiv.org/abs/2508.02518

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