Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation
This work addresses the time-consuming and error-prone manual creation of component libraries for PCB designers, offering an automated solution with high accuracy.
SFgen uses multimodal large language models to automatically generate symbols and footprints for electronic components, achieving 86% accuracy for symbols and 80% for footprints, and creates an expanding database of 1000 components to enable automatic PCB design.
A rich and recognizable component library is the cornerstone of printed circuit board (PCB) design and generation. Traditionally, engineers manually create symbols and footprints and design PCB schematics, which is time-consuming and error-prone. Leveraging multimodal large language models (MLLMs), we develop SFgen, an agentic recognition and generation flow of symbol and footprint for electronic components. SFgen achieves 86% accuracy for symbol generation and 80% accuracy for footprint generation. We use the SFgen method to create SFnet, a database of symbols and footprints. It now has 1000 components and is expanding constantly, which lays the foundation for automatic generation of PCB designs.