| --- |
| language: |
| - en |
|
|
| license: mit |
|
|
| tags: |
| - PCB |
| - EDA |
| - KiCAD |
| - Hardware-Design |
| - Schematic-Generation |
| - LLM |
| - Circuit-Design |
|
|
| library_name: transformers |
| --- |
| |
| # 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 |
|
|
| --- |
|
|
| ## 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. |
