SchGen

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}, 
}

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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:

I want a 1.8V regulated supply from VIN using an AP2112K LDO,
with a test point on the 1.8V rail and a solder-jumper-selectable LED indicator.

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🔥 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.

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Model Details

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Usage

The recommended workflow is:

1. Provide a natural-language circuit request
2. Generate Python schematic-construction code
3. Execute the code to render a KiCad schematic
4. 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

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Evaluation

SchGen was evaluated using several schematic-generation metrics:

• Valid Circuits
  Measures whether generated code executes successfully and produces valid schematics.

• Spatial Violation
  Measures overlaps among symbols, labels, and wires.

• Netlist Accuracy
  Measures connectivity correctness against ground-truth netlists.

SchGen outperforms several frontier LLM baselines on schematic generation tasks when all models are provided with the same schematic-generation APIs.

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Limitations

SchGen is an early-stage research system and currently focuses on:

• small and medium-scale schematic modules
• hobbyist and open-source hardware designs
• English-language requests

The model may underperform on:

• RF or high-frequency circuits
• industrial or enterprise hardware
• large multi-board systems
• safety-critical applications

Generated outputs should always undergo:

• Electrical Rule Checking (ERC)
• Design Rule Checking (DRC)
• human engineering review

SchGen is intended as an assistive tool rather than a fully autonomous hardware engineer.

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Technical Requirements

The model generates executable Python code and requires:

• Python environment
• KiCad installation
• Custom schematic-generation APIs

Inference was validated on:

• NVIDIA A100 GPUs
• 4-bit quantized configurations

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Dataset

SchGen was trained on a custom dataset of approximately 8K pairs of:

• natural-language hardware requests
• Python schematic-generation code

The dataset was synthesized through:

1. GPT-generated draft schematics
2. Human correction and annotation
3. LLM-generated user requests

The dataset is available at https://huggingface.co/datasets/microsoft/SchGen_dataset

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License

This project is licensed under the MIT License.

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Contact

This project was conducted by members of Microsoft Research.

For questions, feedback, or collaboration inquiries:

• ruichunma@microsoft.com

If issues or problematic behavior are identified, the repository may be updated with appropriate mitigations.