EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit
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arXiv
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| Format: | Preprint |
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2025
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| author | Liu, Chang Chitnis, Danial |
| author_facet | Liu, Chang Chitnis, Danial |
| contents | The design of Analog and Mixed-Signal (AMS) integrated circuits (ICs) often involves significant manual effort, especially during the transistor sizing process. While Machine Learning techniques in Electronic Design Automation (EDA) have shown promise in reducing complexity and minimizing human intervention, they still face challenges such as numerous iterations and a lack of knowledge about AMS circuit design. Recently, Large Language Models (LLMs) have demonstrated significant potential across various fields, showing a certain level of knowledge in circuit design and indicating their potential to automate the transistor sizing process. In this work, we propose EEsizer, an LLM-based AI agent that integrates large language models with circuit simulators and custom data analysis functions, enabling fully automated, closed-loop transistor sizing without relying on external knowledge. By employing prompt engineering and Chain-of-Thought reasoning, the agent iteratively explores design directions, evaluates performance, and refines solutions with minimal human intervention. We first benchmarked 8 LLMs on six basic circuits and selected three high-performing models to optimize a 20-transistor CMOS operational amplifier, targeting multiple performance metrics, including rail-to-rail operation from 180 nm to 90 nm technology nodes. Notably, OpenAI o3 successfully achieved the user-intended target at 90 nm across three different test groups, with a maximum of 20 iterations, demonstrating adaptability and robustness at advanced nodes. To assess design robustness, we manually designed a bias circuit and performed a variation analysis using Gaussian-distributed variations on transistor dimensions and threshold voltages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25510 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit Liu, Chang Chitnis, Danial Machine Learning Hardware Architecture The design of Analog and Mixed-Signal (AMS) integrated circuits (ICs) often involves significant manual effort, especially during the transistor sizing process. While Machine Learning techniques in Electronic Design Automation (EDA) have shown promise in reducing complexity and minimizing human intervention, they still face challenges such as numerous iterations and a lack of knowledge about AMS circuit design. Recently, Large Language Models (LLMs) have demonstrated significant potential across various fields, showing a certain level of knowledge in circuit design and indicating their potential to automate the transistor sizing process. In this work, we propose EEsizer, an LLM-based AI agent that integrates large language models with circuit simulators and custom data analysis functions, enabling fully automated, closed-loop transistor sizing without relying on external knowledge. By employing prompt engineering and Chain-of-Thought reasoning, the agent iteratively explores design directions, evaluates performance, and refines solutions with minimal human intervention. We first benchmarked 8 LLMs on six basic circuits and selected three high-performing models to optimize a 20-transistor CMOS operational amplifier, targeting multiple performance metrics, including rail-to-rail operation from 180 nm to 90 nm technology nodes. Notably, OpenAI o3 successfully achieved the user-intended target at 90 nm across three different test groups, with a maximum of 20 iterations, demonstrating adaptability and robustness at advanced nodes. To assess design robustness, we manually designed a bias circuit and performed a variation analysis using Gaussian-distributed variations on transistor dimensions and threshold voltages. |
| title | EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2509.25510 |