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

• URL: http://arxiv.org/abs/2508.02518v2
• Date: Sun, 31 Aug 2025 04:25:48 GMT
• Title: AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs
• Authors: Yao Lai, Souradip Poddar, Sungyoung Lee, Guojin Chen, Mengkang Hu, Bei Yu, Ping Luo, David Z. Pan,
• Abstract summary: We present AnalogCoder-Pro, a multimodal large language model (LLM) framework that integrates generative and optimization techniques.<n>The framework features a multimodal diagnosis-and-repair feedback loop that uses simulation error messages and waveform images to autonomously correct design errors.<n>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.
• Score: 40.14708895187017
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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