PCBSchemaGen: Constraint-Guided Schematic Design via LLM for Printed Circuit Boards (PCB)

TL;DR

PCBSchemaGen is a novel, training-free framework utilizing large language models and constraint-guided synthesis to automate complex PCB schematic design across multiple signal domains, improving accuracy and efficiency.

Contribution

It introduces the first training-free PCB schematic design framework combining LLM-based code generation with a verification system using knowledge graphs and subgraph isomorphism.

Findings

Significant improvement in design accuracy

Enhanced computational efficiency

Effective handling of heterogeneous PCB signals

Abstract

Printed Circuit Board (PCB) schematic design plays an essential role in all areas of electronic industries. Unlike prior works that focus on digital or analog circuits alone, PCB design must handle heterogeneous digital, analog, and power signals while adhering to real-world IC packages and pin constraints. Automated PCB schematic design remains unexplored due to the scarcity of open-source data and the absence of simulation-based verification. We introduce PCBSchemaGen, the first training-free framework for PCB schematic design that comprises LLM agent and Constraint-guided synthesis. Our approach makes three contributions: 1. an LLM-based code generation paradigm with iterative feedback with domain-specific prompts. 2. a verification framework leveraging a real-world IC datasheet derived Knowledge Graph (KG) and Subgraph Isomorphism encoding pin-role semantics and topological constraints. 3. an extensive experiment on 23 PCB schematic tasks spanning digital, analog, and power domains. Results demonstrate that PCBSchemaGen significantly improves design accuracy and computational efficiency.