In the realm of electronic and electrical engineering, automation of analog circuit is increasingly vital given the complexity and customized requirements of modern applications. However, existing methods only develop search-based algorithms that require many simulation iterations to design a custom circuit topology, which is usually a time-consuming process. To this end, we introduce LaMAGIC, a pioneering language model-based topology generation model that leverages supervised finetuning for automated analog circuit design. LaMAGIC can efficiently generate an optimized circuit design from the custom specification in a single pass. Our approach involves a meticulous development and analysis of various input and output formulations for circuit. These formulations can ensure canonical representations of circuits and align with the autoregressive nature of LMs to effectively addressing the challenges of representing analog circuits as graphs. The experimental results show that LaMAGIC achieves a success rate of up to 96\% under a strict tolerance of 0.01. We also examine the scalability and adaptability of LaMAGIC, specifically testing its performance on more complex circuits. Our findings reveal the enhanced effectiveness of our adjacency matrix-based circuit formulation with floating-point input, suggesting its suitability for handling intricate circuit designs. This research not only demonstrates the potential of language models in graph generation, but also builds a foundational framework for future explorations in automated analog circuit design.

翻译：在电子电气工程领域，鉴于现代应用的复杂性和定制化需求，模拟电路的自动化设计日益重要。然而，现有方法仅开发基于搜索的算法，这些算法需要大量仿真迭代来设计定制电路拓扑，通常是一个耗时的过程。为此，我们提出了LaMAGIC，一种开创性的基于语言模型的拓扑生成模型，它利用监督微调技术实现自动化模拟电路设计。LaMAGIC能够根据定制规范，以单次前向推理高效生成优化的电路设计。我们的方法包括对各种电路输入输出表示形式进行细致的开发与分析。这些表示形式能确保电路的规范表达，并与语言模型的自回归特性保持一致，从而有效应对将模拟电路表示为图结构所带来的挑战。实验结果表明，在0.01的严格容差下，LaMAGIC的成功率高达96%。我们还检验了LaMAGIC的可扩展性和适应性，特别测试了其在更复杂电路上的性能。我们的研究结果表明，采用浮点数输入的基于邻接矩阵的电路表示形式具有增强的效能，表明其适用于处理复杂的电路设计。这项研究不仅展示了语言模型在图生成领域的潜力，也为未来自动化模拟电路设计的探索建立了基础框架。