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This paper introduces SFgen, a novel approach leveraging multimodal large language models (MLLMs) to automate the recognition and generation of symbols and footprints for electronic components in PCB design. By achieving 86% accuracy in symbol generation and 80% in footprint generation, SFgen significantly reduces the time and errors associated with traditional manual methods. The resulting SFnet database, containing 1000 components and continuously expanding, sets the stage for the automatic generation of PCB designs, enhancing efficiency in electronic engineering workflows.
Achieving 86% accuracy in automated symbol generation could revolutionize PCB design by drastically reducing manual errors and time investment.
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.
