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<p>The demand for reliable microelectronics in critical sectors like automotive, medical, and defense is heightened by the complexity of their global supply chains. This paper explores automated visual inspection (AVI) of printed circuit boards (PCBs) as a strategic approach utilizing deep instance segmentation models to enhance defect detection, component verification, and quality assurance. We introduce a novel dataset with detailed annotations designed to train artificial intelligence (AI) models for identifying PCB connectivity components. By employing high-resolution imaging and cutting-edge models, this work explores the challenges in these implementations and presents insights into advancing the capabilities of AVI systems.</p>

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AI-Driven Framework for Generalized Optical Inspection of Printed Circuit Board Interconnects

  • 期刊论文

  • 发表日期：

    2025年9月24日

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  • Patrick Craig,
  • Jonathan Pearson,
  • Shajib Ghosh,
  • Nitin Varshney,
  • Sanjeev J. Koppal,
  • Navid Asadizanjani

摘要

The demand for reliable microelectronics in critical sectors like automotive, medical, and defense is heightened by the complexity of their global supply chains. This paper explores automated visual inspection (AVI) of printed circuit boards (PCBs) as a strategic approach utilizing deep instance segmentation models to enhance defect detection, component verification, and quality assurance. We introduce a novel dataset with detailed annotations designed to train artificial intelligence (AI) models for identifying PCB connectivity components. By employing high-resolution imaging and cutting-edge models, this work explores the challenges in these implementations and presents insights into advancing the capabilities of AVI systems.