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.

翻译：将自然语言硬件需求转化为正确的印刷电路板（PCB）原理图，在嵌入式、物联网和可穿戴设备开发领域仍存在困难。设计师必须在布局和原型制作之前，选择兼容的元器件、解读数据手册、添加支持电路并暴露正确的接口，而许多此类电路无法通过简单的仿真进行验证。我们提出pcbGPT，一个基于地面验证的系统，能够从自然语言规格说明生成可编辑的KiCad原理图。pcbGPT采用Python领域特定语言（DSL）表示电路，将工具增强的合成方法与元器件库搜索、数据手册驱动的设计知识、基于执行的检查、结构与语义验证以及支持迭代优化并与KiCad项目同步的交互式网页工作流相结合。我们在20个嵌入式原理图生成任务上对该系统进行了评估，这些任务包含参考实现、所需元器件和接口约束，支持自动比较。最佳模型整体pass@1达到0.90，pass@5达到1.00；在基础级和简单任务上pass@1为1.00，中等任务为0.91，困难任务为0.72。这些结果与失败分析共同表明，pcbGPT已能生成可用于早期原型制作的可审查初稿原理图，但尚不足以替代专家审查。