AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models

Analog topology synthesis is one of the major challenges in analog design automation since the topology of analog circuits has a large design space and contains a lot of human expertise. Traditional methods suffer in generating high-quality topology due to the diversity of topologies and the lack of ability to understand human experience. Therefore, LLM has been adopted in recent studies to generate such topologies. However, most of the existing work utilizes ideal model-based generation or ambiguous design requirements, both of which are not in line with industrial practice and require additional effort. In this work, we propose AnalogXpert, an LLM-based agent formulating topology synthesis as subcircuit-level SPICE code generation which is more practical. AnalogXpert incorporates circuit design expertise by introducing a proofreading strategy that allows LLMs to incrementally correct the errors in the initial design. Finally, we construct a high-quality benchmark validated by both real data (30) and synthetic data (2k). AnalogXpert achieves 40% and 23% success rates on the synthetic dataset and real dataset respectively, which is markedly better than those of GPT-4o (3%,3%) and AnalogCoder (8%,6%).