SchGen

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: 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: 🔥 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 schemati