SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

Forum topic · 小凯 · 2026-06-01

Summary

SchGen is presented as the first large language model that generates editable PCB schematics directly from natural-language requests. The key obstacles addressed are the absence of an LLM-friendly schematic representation and the lack of large-scale training data. Conventional schematic formats are verbose, tool-specific, and geometry-heavy, making them unreliable targets for generation. The authors introduce a semantic-grounded code representation that encodes schematic editing primitives using relative placement and pin-name-based routing, converting the geometry-driven generation problem into an LLM-friendly, semantic-driven matching task. Training data is produced through a human-in-the-loop pipeline that converts open-source hardware designs into the new representation, yielding a large paired dataset of PCB schematics and user prompts. Experiments show SchGen substantially outperforms alternative representations and even larger general-purpose LLMs on cable-connection accuracy and functional correctness. The work highlights how representation design is critical for enabling generative models to tackle complex hardware design tasks. Published on arXiv:2605.30345.

Paper Overview

Field: NLP Authors: Qinpei Luo, Ruichun Ma, Xinyu Zhang, Lili Qiu Published: 2026-05-28 arXiv: 2605.30345

Abstract

Printed circuit board (PCB) schematic design defines nearly all electronic hardware, yet it remains manual and expertise-dependent. While generative AI has advanced digital and analog IC design, generating PCB schematics from natural language intent remains largely unexplored. This paper presents SchGen, the first large language model that generates editable PCB schematics from natural-language requests.

Key Challenges

Lack of a representation suitable for LLMs
Absence of large-scale datasets
Current schematic formats are dominated by verbose, tool-specific syntax and geometry-intensive descriptions, making them hard to generate reliably

Approach

The authors introduce a semantic-grounded code representation that encodes schematic editing primitives using relative placement and pin-name-based routing. This transforms the geometry-driven generation problem into an LLM-friendly, semantic-driven matching task. They further build a large-scale dataset of PCB schematic–user prompt pairs via a human-in-the-loop pipeline that converts open-source hardware designs into this representation.

Results

Experiments show SchGen significantly outperforms alternative representations and even larger general-purpose LLMs in cable-connection accuracy and functional correctness. The results underscore the critical role of representation design in enabling generative models to handle complex hardware design tasks.

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