Paper Detail
Qinpei Luo, Ruichun Ma, Xinyu Zhang, Lili Qiu
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.
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@article{luo2026schgen,
title = {SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations},
author = {Qinpei Luo and Ruichun Ma and Xinyu Zhang and Lili Qiu},
year = {2026},
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. Curr},
url = {https://arxiv.org/abs/2605.30345},
keywords = {cs.AI, cs.CL, cs.LG, Schematic, Computer science, Representation (politics), Construct (python library), Pipeline (software)},
eprint = {2605.30345},
archiveprefix = {arXiv},
}
{}