PCB-QA: Evaluating LLMs over the First PCB Design Question-Answer Dataset (arxiv.org)

A groundbreaking study has introduced PCB-QA, the first question-answer dataset specifically designed to evaluate Large Language Models (LLMs) in the context of printed circuit board (PCB) design. This dataset comprises 480 question-answer pairs derived from eight diverse open-source hardware projects, addressing critical aspects of PCB design, such as component connections and simulation data from SPICE. The significance of this work lies in its potential to enhance the integration of LLMs within the electronic design automation (EDA) sector, particularly for PCBs, an area that previously lacked robust text-based evaluation datasets and prompting methodologies. By benchmarking four advanced LLMs, including the Gemini 3 Flash Preview—which achieved a remarkable 93% accuracy using a JSON-based format—the study demonstrates that LLMs can effectively comprehend PCB designs in both textual and graphical representations. This work paves the way for future developments in automating and facilitating PCB design tasks through AI, allowing engineers to leverage LLMs in evaluating and enhancing their design processes. The open-source nature of the questionnaire also encourages further research and collaboration in the evolving intersection of AI and hardware design.