SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

2026-05-28

AI Summary

This work addresses the longstanding challenge in PCB schematic design, which heavily relies on manual effort and lacks effective methods for automatically generating editable circuit diagrams from natural language. We propose SchGen, the first large language model tailored for this task, introducing a novel semantic-anchored code representation that reframes the geometry-driven generation problem as a semantic matching task. To support training, we construct a large-scale paired dataset linking natural language descriptions to schematics. SchGen integrates pin-name-aware routing with relative component layout to enable end-to-end generation of editable schematics from text. Experimental results demonstrate that SchGen significantly outperforms existing representation schemes and even larger general-purpose language models in terms of wiring accuracy and functional correctness, highlighting the critical role of domain-specific semantic representations in complex hardware generation tasks.

Abstract

Printed circuit board (PCB) schematic design defines nearly all electronic hardware, but it remains manual and expertise-intensive. While generative AI has advanced digital and analog IC design, PCB schematic generation from natural-language intent is largely unexplored. This paper presents SchGen, the first large language model that generates editable PCB schematics from natural-language requests. The key challenge lies in the lack of an LLM-suited representation and a large-scale dataset. Current schematic formats are dominated by verbose, tool-specific syntax and geometry-heavy descriptions, making them difficult to generate reliably. We introduce a semantically grounded code representation that encodes schematic editing primitives with relative placement and pin-name-based wiring, transforming a geometry-driven generation problem into a semantics-driven matching task amenable to LLMs. We further construct a large-scale dataset of PCB schematics paired with user prompts via a human-agent collaborative pipeline that converts open-source hardware designs into our representation. Experiments show that SchGen significantly outperforms alternative representations and even larger general-purpose LLMs on wire connectivity accuracy and functional correctness. Our results highlight the critical role of representation design in enabling generative models for complex hardware design tasks.

Problem

• PCB schematic generation
• natural-language intent
• semantic representation
• large language models
• hardware design automation

Innovation

• semantic-grounded representation
• PCB schematic generation
• large language model
• natural-language to hardware
• relative placement wiring