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

CircuitLM is a multi-agent AI framework that converts natural language descriptions into machine-readable circuit schematics, addressing persistent hallucination and constraint-violation issues in LLM-based electronic design automation. The system uses a five-stage pipeline combining retrieval-augmented generation with dual-layer verification—electrical rule checking and LLM-as-judge evaluation—to produce structurally viable, prototype-ready circuits.

CircuitLM tackles a genuine pain point in electronic design automation where large language models struggle to generate physically accurate circuit schematics. The core innovation lies not in raw LLM capability but in architectural constraints: grounding generation in a curated component knowledge base and enforcing deterministic verification rules prevents the hallucination and constraint violations that plague naive prompt-to-schematic approaches.

The problem stems from LLMs' tendency to fabricate components, assign incorrect pinouts, or violate electrical rules when operating without structured guidance. Previous attempts relied on fine-tuning or prompt engineering alone, yielding unreliable outputs unsuitable for manufacturing or prototyping. CircuitLM's five-stage pipeline—component identification, pinout retrieval, reasoning, JSON synthesis, and visualization—systematically reduces failure modes by anchoring each stage to verifiable, deterministic processes.

The evaluation methodology demonstrates rigor often absent in AI systems research. The dual-layered assessment combines strict Electrical Rule Checking (categorizing faults by severity) with LLM-as-judge meta-evaluation that catches context-specific design flaws. Testing across 100 prompts and five state-of-the-art LLMs provides meaningful comparative data rather than anecdotal claims.

Industry implications extend beyond research: reliable AI-assisted circuit design accelerates hardware prototyping for startups and reduces design iteration cycles for established manufacturers. The public code release enables broader adoption and community validation. However, impact depends on real-world testing—whether CircuitLM handles novel, complex designs or remains effective only on trained prompt distributions remains unclear from current data.

• →CircuitLM combines retrieval-augmented generation with deterministic verification to eliminate hallucination in LLM-generated circuit schematics.
• →The framework's five-stage pipeline grounds generation in curated component knowledge and enforces strict electrical rule compliance.
• →Dual-layer evaluation using both rule-checking and LLM-as-judge assessment provides comprehensive fault detection beyond traditional ERC engines.
• →Public code release enables adoption across hardware design workflows and academic research communities.
• →Success depends on real-world validation; effectiveness on novel designs outside training distribution remains untested.