LLMACD: An LLM-Based Analog Circuit Designer Driven by Behavior Parameters

Analog circuit design is traditionally dependent on extensive human expertise, making the process time consuming and labor intensive. This paper introduces LLMACD, a knowledge-embedded automation designer for analog circuit design based on large language models (LLMs). We design a refined prompt manager to extract circuit representation, embed circuit knowledge, and model the knowledge-intensive design process within a Chain-of-Thought (CoT) workflow. Leveraging LLMs’ in-context learning capabilities and high-quality human-annotated design experience, LLMACD derives transistor behavioral parameters by analyzing performance expressions in a human-like manner without complex iterative loops, thereby accelerating the design process. Experimental results demonstrate that LLMACD outperforms traditional iterative transistor sizing optimization methods, achieving faster design times and enhanced circuit performance, with performance gains of up to 2 times.