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

2026-01-31

AI Summary

This work addresses the challenges in automated PCB schematic design, which are hindered by heterogeneous signal processing, difficulties in modeling realistic IC package constraints, and a lack of open-source datasets and validation methodologies. The authors propose the first training-free framework for automatic schematic generation, integrating large language model agents with constraint-guided synthesis. By leveraging domain-specific prompts, the system iteratively generates circuit code and constructs a knowledge graph derived from IC datasheets to enable precise validation of both topological structure and pin semantics. The approach supports mixed-signal designs encompassing digital, analog, and power circuits, demonstrating significant improvements in design accuracy and computational efficiency across 23 real-world tasks, thereby establishing a novel training-free paradigm for PCB schematic generation.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• PCB schematic design
• heterogeneous signals
• pin constraints
• automated design
• IC packages

Innovation

• LLM-based schematic generation
• Constraint-guided synthesis
• Knowledge Graph
• Subgraph Isomorphism
• PCB design automation