LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits

Abstract

In the realm of electronic and electrical engineer- ing, automation of analog circuit is increasingly vital given the complexity and customized require- ments 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 super- vised finetuning for automated analog circuit de- sign. LaMAGIC can efficiently generate an opti- mized circuit design from the custom specifica- tion 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 ex- perimental results show that LaMAGIC achieves a success rate of up to 96% under a strict tol- erance 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 foun- dational framework for future explorations in au- tomated analog circuit design.