CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis

TL;DR

CIRCUITSYNTH uses large language models to automate the generation and refinement of electronic circuit topologies, improving design efficiency and adherence to specifications.

Contribution

The paper introduces a novel two-phase LLM-based method for automated circuit topology synthesis, combining generation and refinement stages.

Findings

CIRCUITSYNTH outperforms fine-tuned LLM variants in experiments.

The approach effectively generates valid circuit topologies.

It provides a foundation for future enhancements in circuit design automation.

Abstract

Circuit topology generation plays a crucial role in the design of electronic circuits, influencing the fundamental functionality of the circuit. In this paper, we introduce CIRCUITSYNTH, a novel approach that harnesses LLMs to facilitate the automated synthesis of valid circuit topologies. With a dataset comprising both valid and invalid circuit configurations, CIRCUITSYNTH employs a sophisticated two-phase methodology, comprising Circuit Topology Generation and Circuit Topology Refinement. Experimental results demonstrate the effectiveness of CIRCUITSYNTH compared to various fine-tuned LLM variants. Our approach lays the foundation for future research aimed at enhancing circuit efficiency and specifying output voltage, thus enabling the automated generation of circuit topologies with improved performance and adherence to design requirements.