  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.
