用自然语言自动生成可编辑的电路原理图，提升嵌入式开发效率
pcbGPT: Automatic PCB Schematic Synthesis from Natural Language Requirements
- 基于Python DSL和组件库搜索，结合数据手册知识生成电路
- 在20个任务中达到90%的单次通过率，复杂任务仍需人工审核
- 适合快速原型设计，但需专家审校，适用于嵌入式开发者
将自然语言硬件需求转化为正确印刷电路板（PCB）原理图，在嵌入式、物联网和可穿戴设备开发中仍具挑战。设计师需选择兼容元器件、解读数据手册、添加支持电路并正确暴露接口，方可进行布局与原型制作，而许多电路无法通过简单仿真验证。我们提出pcbGPT，一个基于真实环境的系统，可从自然语言规范生成可编辑的KiCad原理图。pcbGPT采用Python领域专用语言表示电路，结合工具增强的合成、组件库搜索、数据手册驱动的设计知识、执行式检查、结构与语义验证，以及支持迭代优化和与KiCad项目同步的交互式网页工作流。我们在20个嵌入式原理图生成任务上评估该系统，包含参考实现、所需元器件及接口约束，支持自动比较。最佳模型在pass@1下达到0.90，pass@5为1.00；基本与简单任务的pass@1为1.00，中等任务为0.91，复杂任务为0.72。结果结合失败分析表明，pcbGPT已能生成可用于早期原型设计的可审查初稿，但仍不足以替代专家评审。
原文摘要 · Abstract (English)
Translating natural-language hardware requirements into correct printed circuit board (PCB) schematics remains difficult in embedded, IoT, and wearable development. Designers must choose compatible components, interpret datasheets, add support circuitry, and expose correct interfaces before layout and prototyping can begin, while many such circuits cannot be validated through straightforward simulation. We present pcbGPT, a grounded system for generating editable KiCad schematics from natural-language specifications. pcbGPT represents circuits in a Python DSL and combines tool-augmented synthesis with component-library search, datasheet-grounded design knowledge, execution-based checking, structural and semantic validation, and an interactive web workflow that supports iterative refinement and synchronization with KiCad projects. We evaluate the system on 20 embedded schematic-generation tasks with reference implementations, required components, and interface constraints that enable automatic comparison. The best model reaches overall pass@1 of 0.90 and pass@5 of 1.00; pass@1 is 1.00 on basic and easy tasks, 0.91 on medium tasks, and 0.72 on hard tasks. These results, together with failure analysis, show that pcbGPT can already generate useful, reviewable first-draft schematics for early prototyping, but is not yet reliable enough to replace expert review.
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