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language:
- en
license: mit
tags:
- PCB
- EDA
- KiCAD
- Hardware-Design
- Schematic-Generation
- LLM
- Circuit-Design
library_name: transformers
---
# SchGen
[]()
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**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
---
## 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
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.
```
---
## 🔥 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:
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
---
## 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.
---
## 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.
---
## 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
---
## 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`
---
## License
This project is licensed under the MIT License.
---
## 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. |