Cross-Domain Adaptation of Automated 3D X-Ray Defect Detection from HBM to Optical Transceivers

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Cross-Domain Adaptation of Automated 3D X-Ray Defect Detection from HBM to Optical Transceivers
Title:
Cross-Domain Adaptation of Automated 3D X-Ray Defect Detection from HBM to Optical Transceivers
Journal Title:
2025 IEEE 27th Electronics Packaging Technology Conference (EPTC)
Publication Date:
24 February 2026
Citation:
Wang, J., Chang, R., Gourikutty, S. B. N., Wai, L. C. E., Yu, Y., Yang, X., & Pahwa, R. S. (2025). Cross-Domain Adaptation of Automated 3D X-Ray Defect Detection from HBM to Optical Transceivers. In (Editor), 2025 IEEE 27th Electronics Packaging Technology Conference (EPTC). https://doi.org/10.1109/eptc67330.2025.11392412
Abstract:
The inexorable trend towards heterogeneous integration in high-bandwidth optical transceivers presents a formidable challenge for in-line metrology and yield enhancement. This work introduces a novel, end-to-end automated inspection framework leveraging high-resolution X-ray computed tomography to overcome the limitations of conventional surface-based techniques. Our dataset consists of 3D X-ray scans of diced packages from 300 mm reconstituted wafers, each integrating silicon photonics integrated circuits, electronic integrated circuits, through-mold interconnects, and multi-layer redistribution layers, encapsulated within epoxy mold compounds. Traditional inspection methods, such as optical microscopy and electrical testing, are fundamentally blind to critical buried defects like solder voids at micro-bump interfaces, pad misalignment, and other related defects. Our automated workflow addresses this critical visibility gap.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021
Description:
© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
979-8-3315-6145-1
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