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Islamic University of Technology

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CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts

Jan 08, 2026arXiv.org

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.

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