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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.02518 |
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| _version_ | 1866908511131140096 |
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| author | Lai, Yao Poddar, Souradip Lee, Sungyoung Chen, Guojin Hu, Mengkang Yu, Bei Luo, Ping Pan, David Z. |
| author_facet | Lai, Yao Poddar, Souradip Lee, Sungyoung Chen, Guojin Hu, Mengkang Yu, Bei Luo, Ping Pan, David Z. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs Lai, Yao Poddar, Souradip Lee, Sungyoung Chen, Guojin Hu, Mengkang Yu, Bei Luo, Ping Pan, David Z. Machine Learning 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. |
| title | AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.02518 |