Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation

2026-07-22

AI Summary

This work addresses the inefficiency and error-proneness of manual symbol and footprint creation in traditional PCB design by proposing SFgen, a novel pipeline that leverages agent-driven multimodal large language models for automated recognition and generation of electronic component symbols and footprints. The approach introduces SFnet, an extensible component library that has already integrated 1,000 components and continues to expand. Experimental results demonstrate that SFgen achieves 86% accuracy in symbol generation and 80% accuracy in footprint generation, significantly enhancing the automation level of PCB design and the efficiency of component library construction.

Abstract

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.

Problem

• symbol generation
• footprint generation
• PCB design
• electronic components
• component library

Innovation

• agentic recognition
• symbol generation
• footprint generation
• multimodal large language models
• PCB automation