CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts
Researchers have developed CircuitLM, a novel multi-agent framework designed to generate accurate circuit schematics from natural language prompts. This system addresses common LLM issues like hallucination and physical constraint violations by grounding its output in a curated component knowledge base. CircuitLM employs a five-stage pipeline, including component identification, pinout retrieval, chain-of-thought reasoning, JSON schematic synthesis, and visualization, to produce structured and visually interpretable schematics. Evaluation using five state-of-the-art LLMs and a dual-layered methodology involving an Electrical Rule Checking engine and an LLM-as-a-judge approach demonstrates its effectiveness in creating safe and structurally viable circuit designs. AI
IMPACT This framework could streamline hardware design by enabling natural language-to-schematic generation, potentially reducing errors and accelerating prototyping.