PCBSchemaGen: Reward-Guided LLM Code Synthesis for Printed Circuit Boards (PCB) Schematic Design with Structured Verification

2026-01-27 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Electronic Design Automation · Depth: Expert, extended

Summary

PCBSchemaGen is a novel, training-free framework for automated Printed Circuit Board (PCB) schematic design, addressing the scarcity of open-source data and simulation-based verification in this complex domain. It integrates an LLM agent for SKiDL code generation with a constraint-guided synthesis and multi-phase verification system. This system leverages a Knowledge Graph (KG) derived from real-world IC datasheets and Subgraph Isomorphism (SI) to encode pin-role semantics and topological constraints. Evaluated on 23 PCB schematic tasks spanning digital, analog, and power domains, PCBSchemaGen significantly improves design accuracy and computational efficiency, achieving a ~37x speedup over human experts, with an average cost of 0.07 USD and runtime of 2.4 minutes per task. All source code, KG, and benchmark are open-sourced at https://github.com/HZou9/PCBSchemaGen.

Key takeaway

For AI Engineers developing automated hardware design tools, PCBSchemaGen demonstrates a robust approach to overcome data scarcity and verification challenges in PCB schematic generation. You should integrate knowledge-graph-driven constraint verification and iterative feedback loops into your LLM-based design workflows. This strategy ensures functional correctness and significantly boosts efficiency, enabling rapid prototyping of complex digital, analog, and power circuits.

Key insights

PCBSchemaGen automates PCB schematic design using LLM code synthesis guided by real-world IC constraints and iterative verification.

Principles

• Anchor LLM generation in real-world physical IC constraints.
• Iterative feedback from verifier enhances code synthesis success.
• Domain-specific KGs reduce LLM context usage significantly.

Method

An LLM generates SKiDL code using domain-specific prompts, Chain-of-Thought, and In-context Learning. A multi-stage verifier, based on a datasheet-driven Knowledge Graph and Subgraph Isomorphism, provides iterative feedback for refinement.

In practice

• Utilize SKiDL for Python-based hardware description.
• Construct domain-specific KGs from IC datasheets.
• Implement multi-stage verification with explicit error localization.

Topics

• PCB Design Automation
• LLM Code Synthesis
• Knowledge Graphs
• Subgraph Isomorphism
• SKiDL
• Hardware Design Automation

Code references

• HZou9/PCBSchemaGen

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.