LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation

Automation of analog topology design is crucial due to customized requirements of modern appli- cations with heavily manual engineering efforts. The state-of-the-art work applies a sequence-to- sequence approach and supervised finetuning on language models to generate topologies given user specifications. However, its circuit formulation is inefficient due to O(|V |2) token length and suffers from low precision sensitivity to numeric inputs. In this work, we introduce LaMAGIC2, a succinct float-input canonical formulation with identifier (SFCI) for language model-based ana- log topology generation. SFCI addresses these challenges by improving component-type recog- nition through identifier-based representations, re- ducing token length complexity to O(|V | + |E|), and enhancing numeric precision sensitivity for better performance under tight tolerances. Our ex- periments demonstrate that LaMAGIC2 achieves 34% higher success rates under a tight tolerance of 0.01 and 10X lower MSEs compared to a prior method. LaMAGIC2 also exhibits better transfer- ability for circuits with more vertices with up to 58.5% improvement. These advancements estab- lish LaMAGIC2 as a robust framework for analog topology generation.