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English(EN) Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation

AI通过SFgen和SFnet数据库实现电子元件设计的自动化

研究人员开发了SFgen,一个利用多模态大语言模型(MLLMs)自动创建电子元件符号和封装的新系统。该代理识别和生成流程在符号生成方面达到86%的准确率,在封装生成方面达到80%的准确率。该方法已被用于构建SFnet,一个目前包含1000个电子元件的数据库,并正在持续扩展,旨在实现印刷电路板(PCB)设计的自动生成。 AI

影响 自动化了PCB设计中传统上手动且易出错的流程,有望加速开发周期。

排序理由 该集群描述了一篇研究论文,其中详细介绍了使用AI进行电子元件设计的新方法和数据库。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI通过SFgen和SFnet数据库实现电子元件设计的自动化

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该集群描述了一篇研究论文,其中详细介绍了使用AI进行电子元件设计的新方法和数据库。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yichen Shi, Yuzhi Liu, Zhuofu Tao, Li Huang, Yuhao Gao, Ting-Jung Lin, Lei Hel ·

    Agentic 识别与生成电子元件符号和封装数据库

    arXiv:2607.19767v1 Announce Type: new 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 an…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    通过代理识别和生成实现电子元件符号和封装数据库

    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 langu…