Deep-learning-based X-ray CT slice analysis for layout verification in printed circuit boards

• Main Authors: Cheng, Deruo, Shi, Yiqiong, Tee, Yee Yang, Song, Jingsi, Wang, Xue, Wen, Bihan, Gwee, Bah Hwee
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2024
• Subjects: Engineering; Printed circuit boards; Hardware security
• Online Access: https://hdl.handle.net/10356/173858
• Institution: Nanyang Technological University

3D X-ray Computational Tomography (CT) systems have been employed to inspect Printed Circuit Boards (PCB) for security analysis, considering the heightened trustworthiness concern on the globalized supply chain. In this paper, we propose a deep-learning-based layout verification (DELVer) framework to automatically extract PCB layout information from X-ray CT slices and verify against the design files. Leveraging on geometrical projective transformation, our proposed DELVer framework aligns the acquired CT slice of each PCB layer with their corresponding design file, to train state-of-the-art deep learning models for layout extraction and verification. It thus alleviates the laborious manual data labeling for deep learning models. With a cross-device evaluation on 4 multi-layer satellite PCBs of board size around 90 cm2, our proposed DELVer framework demonstrates how deep learning models can generalize to unseen target PCBs for layout verification, establishing an efficient solution for PCB assurance and industrial failure analysis.