SchGen is a large language model for PCB schematic generation from natural-language requests.
The model is supervised fine-tuned from GPT-OSS-20B using a custom dataset of approximately 8K paired user requests and schematic-generation code samples.
SchGen generates executable Python code that can be rendered into KiCad schematic designs using customized schematic APIs.
➡️ Base Model: GPT-OSS-20B
➡️ License: MIT
➡️ Framework: Transformers
➡️ Context Length: 13,312 tokens
To cite this model and corresponding paper, please use the following bib item:
@misc{luo2026schgenpcbschematicgeneration,
title={SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations},
author={Qinpei Luo and Ruichun Ma and Xinyu Zhang and Lili Qiu},
year={2026},
eprint={2605.30345},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2605.30345},
}
Overview
Printed circuit board (PCB) design is a critical but expertise-intensive process in embedded systems, IoT, robotics, and AI hardware.
SchGen explores whether large language models can assist hardware design by generating schematic construction code directly from natural-language descriptions.
The input is a user request describing a circuit design requirement, and the output is executable Python code that can generate a KiCad schematic using custom APIs.
Example input:
text
1I want a 1.8V regulated supply from VIN using an AP2112K LDO,
2with a test point on the 1.8V rail and a solder-jumper-selectable LED indicator.
🔥 Key Features
🔌 Natural Language to Schematic Code
Generates executable Python schematic-generation code directly from user requests.
🧠 KiCad-Oriented Design Flow
Designed around custom Code-to-Schematic APIs for KiCad schematic construction.
📐 Structured Hardware Generation
Produces editable and programmatic schematic representations instead of images.
🛠️ Research-Focused PCB Generation
Intended for experimentation, benchmarking, and AI-assisted hardware prototyping.
Model Details
Item
Value
Base Model
GPT-OSS-20B
Parameters
20B
Architecture
Supervised Fine-Tuned LLM
Input
Natural-language design requests
Output
Python schematic-generation code
Context Length
13,312
Training Hardware
1× NVIDIA A100
Training Time
~21 hours
Usage
The recommended workflow is:
Provide a natural-language circuit request
Generate Python schematic-construction code
Execute the code to render a KiCad schematic
Verify outputs using ERC/DRC tools
The model is designed for integration into:
EDA automation pipelines
Hardware engineering copilots
Synthetic schematic generation systems
Research workflows for AI-assisted PCB design
Evaluation
SchGen was evaluated using several schematic-generation metrics:
