Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functionality. Despite advances made by Large Language Models (LLMs) in digital circuit design, the complexity and scarcity of data in analog circuitry pose significant challenges. To mitigate these issues, we introduce AnalogCoder, the first training-free LLM agent for designing analog circuits through Python code generation. Firstly, AnalogCoder incorporates a feedback-enhanced flow with tailored domain-specific prompts, enabling the automated and self-correcting design of analog circuits with a high success rate. Secondly, it proposes a circuit tool library to archive successful designs as reusable modular sub-circuits, simplifying composite circuit creation. Thirdly, extensive experiments on a benchmark designed to cover a wide range of analog circuit tasks show that AnalogCoder outperforms other LLM-based methods. It has successfully designed 20 circuits, 5 more than standard GPT-4o. We believe AnalogCoder can significantly improve the labor-intensive chip design process, enabling non-experts to design analog circuits efficiently.

翻译：模拟电路设计是现代芯片技术中的一项重要任务，其核心在于选择元件类型、连接方式及参数，以确保电路功能正常。尽管大语言模型在数字电路设计领域已取得进展，但模拟电路的复杂性和数据稀缺性带来了重大挑战。为缓解这些问题，我们提出了AnalogCoder——首个通过Python代码生成进行模拟电路设计的免训练大语言模型智能体。首先，AnalogCoder采用反馈增强流程并结合定制化的领域特定提示，能够以高成功率实现自动化且具备自我修正能力的模拟电路设计。其次，它提出了一种电路工具库，用于将成功设计的电路归档为可复用的模块化子电路，从而简化复合电路的创建过程。第三，在覆盖广泛模拟电路任务的基准测试上进行的大量实验表明，AnalogCoder优于其他基于大语言模型的方法。它已成功设计出20个电路，比标准GPT-4o多设计5个。我们相信AnalogCoder能够显著改善劳动密集型的芯片设计流程，使非专业人士也能高效地设计模拟电路。