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

Huanghaohe Zou, Peng Han, Emad Nazerian, Alex Q. Huang

TL;DR

PCBSchemaGen introduces a training-free, constraint-guided framework that uses an LLM to generate SKiDL-based PCB schematics while enforcing real IC package and pin constraints through a datasheet-derived Knowledge Graph and Subgraph Isomorphism verifier. The approach combines sophisticated prompt design (including CoT and ICL) with a multi-stage verification pipeline and a reusable subcircuit library, achieving end-to-end executable outputs (SKiDL to KiCad) across 23 tasks. Experiments show notable gains in design accuracy and efficiency, with strong alignment to expert judgment and substantial speedups over manual design. The methodology addresses data scarcity and verification gaps in PCB schematic design, enabling reliable, scalable hardware automation for heterogeneous digital, analog, and power domains.

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.

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

TL;DR

PCBSchemaGen introduces a training-free, constraint-guided framework that uses an LLM to generate SKiDL-based PCB schematics while enforcing real IC package and pin constraints through a datasheet-derived Knowledge Graph and Subgraph Isomorphism verifier. The approach combines sophisticated prompt design (including CoT and ICL) with a multi-stage verification pipeline and a reusable subcircuit library, achieving end-to-end executable outputs (SKiDL to KiCad) across 23 tasks. Experiments show notable gains in design accuracy and efficiency, with strong alignment to expert judgment and substantial speedups over manual design. The methodology addresses data scarcity and verification gaps in PCB schematic design, enabling reliable, scalable hardware automation for heterogeneous digital, analog, and power domains.

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.
Paper Structure (32 sections, 5 equations, 9 figures, 17 tables, 1 algorithm)

This paper contains 32 sections, 5 equations, 9 figures, 17 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries and Related Work
  3. Traditional PCB Schematic Design Workflow
  4. Existing Circuit Design
  5. Approach
  6. Prompt Design
  7. Knowledge Graph (KG) Design
  8. Verification Pipeline.
  9. feedback Loop & Subcircuit Library
  10. Experiments
  11. Experiment Setup
  12. Main Results
  13. Ablation Studies
  14. Human Experts Evaluation
  15. Conclusion
  16. ...and 17 more sections

Figures (9)

  • Figure 1: Overview of the PCBSchemaGen framework workflow. (a) User inputs natural language description of PCB schematic design task, it can be digital, analog or power task, or mix of them. (b) PCBSchemaGen agent read the KG and designed rules from IC datasheets. (c) The agent generates SKiDL code. (d) Correct SKiDL code generates KiCAD PCB schematic file. (e) KiCAD generates PCB Layout file that is ready for routing.
  • Figure 2: The multi-stage verification flow of PCBSchemaGen.
  • Figure 3: Illustration of the Subgraph Isomorphism (SI) based topological verification process.
  • Figure 4: Visualizations of generated PCB schematics. (a) Task #3 failure (wrong op-amp feedback $R_{1},R_{2},R_{4}$) vs. (b) correct output. (c) Task #11 failure (mis-connected Kelvin-Source) vs. (d) correct output.
  • Figure 5: Success distribution and cumulative token usage across retry rounds.
  • ...and 4 more figures