用AI自动生成电子元件符号和焊盘，提升电路板设计效率。
Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation
- 通过多模态大模型构建智能生成流程，自动识别并生成元件符号与焊盘。
- 符号生成准确率达86%，焊盘生成准确率达80%，已构建1000个元件的数据库。
- 适合电子设计自动化从业者，推动PCB设计自动化发展。
丰富的可识别元件库是印刷电路板（PCB）设计与生成的基础。传统上，工程师需手动创建符号与焊盘并绘制电路图，耗时且易出错。本文利用多模态大语言模型（MLLM），提出SFgen——一种用于电子元件符号与焊盘的智能识别与生成流程。SFgen在符号生成上达到86%准确率，在焊盘生成上达到80%准确率。基于该方法，我们构建了名为SFnet的符号与焊盘数据库，目前已包含1000个元件，并持续扩展，为PCB设计的自动化奠定了基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。