PCB-QA: A Task-Specific Question Answering Framework for Text-Centric Chip Datasheet Queries in PCB Design

In PCB design, engineers often rely on datasheets to obtain crucial chip information, a process that is both time-consuming and labor-intensive. To streamline this workflow, we present PCB-QA, a task-specific Retrieval-Augmented Generation (RAG)-based question-answering system that automatically addresses text-centric chip-related queries using the latest datasheets. Rather than aiming to replace broader commercial AI-assisted EDA platforms, this work focuses on a narrower and reproducible research problem: grounded datasheet question answering over a maintained corpus. While existing RAG technologies have made significant progress in document-based question answering, several challenges remain, primarily due to the erroneous segmentation of documents, the retrieval of inaccurate references, and under-specified user queries. To improve the accuracy of PCB-QA’s responses, our framework incorporates three key components. First, we propose a datasheet segmentation model that is specifically trained to divide the corpus into semantically coherent chunks. Second, we integrate a correlation estimation module to better assess the relevance of retrieved references and reduce the influence of distractors. Finally, recognizing that user inputs may lack critical details, we employ a query-rationalization mechanism to facilitate further reasoning and refine the query for under-specified technical and application-related questions. These task-specific customizations improve retrieval quality and support more accurate grounded answers for datasheet-centric QA. Experimental results on our customized ChipQuestion benchmark demonstrate that PCB-QA achieves consistent improvements over representative RAG baselines on text-grounded datasheet QA. We also clarify the current scope of PCB-QA: the present implementation is primarily text-centric and does not yet fully address image-only figures, waveform plots, complex visual tables, or other visually grounded datasheet content.