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

• URL: http://arxiv.org/abs/2412.19824v1
• Date: Tue, 17 Dec 2024 09:08:08 GMT
• Title: AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models
• Authors: Haoyi Zhang, Shizhao Sun, Yibo Lin, Runsheng Wang, Jiang Bian,
• Abstract summary: We propose AnalogXpert, a LLM-based agent aiming at solving practical topology synthesis problem. First, we represent analog topology as SPICE code and introduce a subcircuit library to reduce the design space. Second, we decompose the problem into two sub-tasks through the use of CoT and incontext learning techniques. Third, we introduce a proofreading strategy that allows LLMs to incrementally correct the errors in the initial design.
• Score: 10.986618360243526
• License:
• Abstract: Analog circuits are crucial in modern electronic systems, and automating their design has attracted significant research interest. One of major challenges is topology synthesis, which determines circuit components and their connections. Recent studies explore large language models (LLM) for topology synthesis. However, the scenarios addressed by these studies do not align well with practical applications. Specifically, existing work uses vague design requirements as input and outputs an ideal model, but detailed structural requirements and device-level models are more practical. Moreover, current approaches either formulate topology synthesis as graph generation or Python code generation, whereas practical topology design is a complex process that demands extensive design knowledge. In this work, we propose AnalogXpert, a LLM-based agent aiming at solving practical topology synthesis problem by incorporating circuit design expertise into LLMs. First, we represent analog topology as SPICE code and introduce a subcircuit library to reduce the design space, in the same manner as experienced designers. Second, we decompose the problem into two sub-task (i.e., block selection and block connection) through the use of CoT and incontext learning techniques, to mimic the practical design process. Third, we introduce a proofreading strategy that allows LLMs to incrementally correct the errors in the initial design, akin to human designers who iteratively check and adjust the initial topology design to ensure accuracy. Finally, we construct a high-quality benchmark containing 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% on both the synthetic dataset and the real dataset).

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