印刷电路板 (PCB) 原理图设计定义了几乎所有电子硬件,，但它仍然需要大量手动和专业知识。虽然生成式 AI 拥有先进的数字和模拟 IC 设计, PCB 原理图，但根据自然语言意图生成的原理图在很大程度上尚未被探索。本文介绍了 SchGen, 第一个大型语言模型，可根据自然语言请求生成可编辑的 PCB 原理图。关键的挑战在于缺乏适合法学硕士的表示和大规模数据集。当前的原理图格式以详细的, 工具特定语法和大量几何描述, 为主，这使得它们难以可靠地生成。我们引入了一种基于语义的代码表示，它使用相对位置和基于引脚名称的接线来编码原理图编辑原语,，将几何驱动的生成问题转换为适合法学硕士的语义驱动的匹配任务。我们进一步构建了一个大规模的 PCB 原理图数据集，并通过人工代理协作管道将开源硬件设计转换为我们的表示形式，并与用户提示配对。实验表明，SchGen 在线路连接准确性和功能正确性方面显着优于替代表示形式，甚至更大的通用 LLM。我们的结果强调了表示设计在为复杂的硬件设计任务启用生成模型方面的关键作用。
Printed circuit board (PCB) schematic design defines nearly all electronic hardware, but it remains manual and expertise-intensive. While generative AI has advanced digital and analog IC design, PCB schematic generation from natural-language intent is largely unexplored. This paper presents SchGen, the first large language model that generates editable PCB schematics from natural-language requests. The key challenge lies in the lack of an LLM-suited representation and a large-scale dataset. Current schematic formats are dominated by verbose, tool-specific syntax and geometry-heavy descriptions, making them difficult to generate reliably. We introduce a semantically grounded code representation that encodes schematic editing primitives with relative placement and pin-name-based wiring, transforming a geometry-driven generation problem into a semantics-driven matching task amenable to LLMs. We further construct a large-scale dataset of PCB schematics paired with user prompts via a human-agent collaborative pipeline that converts open-source hardware designs into our representation. Experiments show that SchGen significantly outperforms alternative representations and even larger general-purpose LLMs on wire connectivity accuracy and functional correctness. Our results highlight the critical role of representation design in enabling generative models for complex hardware design tasks.
科目: 人工智能 (cs.AI); 计算和语言 (cs.CL); 机器学习 (cs.LG)
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)