🤖 AI Summary
This work addresses the challenges of deploying natural language–to–circuit diagram generation, which often suffers from hallucinated details, violations of electrical constraints, and non-machine-readable outputs. The authors propose a multi-agent LLM-assisted design framework that translates natural language instructions into structured, verifiable circuit schematics through a five-stage pipeline: component identification, pin retrieval, expert reasoning, JSON synthesis, and SVG visualization. Integrating an embedded component knowledge base, chain-of-thought reasoning, and the CircuitJSON format, the method introduces a novel dual-metric circuit verification (DMCV) mechanism to guarantee both electrical and topological correctness. Experimental evaluation on 100 embedded-system prompts demonstrates strong performance in microcontroller-related designs, enabling reliable translation from natural language to high-fidelity, deployable hardware specifications.
📝 Abstract
Generating accurate circuit schematics from high-level natural language descriptions remains a persistent challenge in electronics design, as large language models (LLMs) frequently hallucinate in granular details, violate electrical constraints, and produce non-machine-readable outputs. We present CircuitLM, a novel multi-agent LLM-aided circuit design pipeline that translates user prompts into structured, visually interpretable CircuitJSON schematics through five sequential stages: (i) LLM-based component identification, (ii) canonical pinout retrieval, (iii) chain-of-thought reasoning by an electronics expert agent, (iv) JSON schematic synthesis, and (v) force-directed SVG visualization. Anchored by a curated, embedding-powered component knowledge base. While LLMs often violate electrical constraints, CircuitLM bridges this gap by grounding generation in a verified and dynamically extensible component database, initially comprising 50 components. To ensure safety, we incorporate a hybrid evaluation framework, namely Dual-Metric Circuit Validation (DMCV), validated against human-expert assessments, which achieves high fidelity in microcontroller-centric designs. We evaluate the system on 100 diverse embedded-systems prompts across six LLMs and introduce DMCV to assess both structural and electrical validity. This work bridges natural language input to deployable hardware designs, enabling reliable circuit prototyping by non-experts. Our code and data will be made public upon acceptance.