3D Defect Detection and Metrology of HBMs using Semi-Supervised Deep Learning

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3D Defect Detection and Metrology of HBMs using Semi-Supervised Deep Learning
Title:
3D Defect Detection and Metrology of HBMs using Semi-Supervised Deep Learning
Journal Title:
2023 IEEE 73rd Electronic Components and Technology Conference (ECTC)
Keywords:
Publication Date:
03 August 2023
Citation:
Pahwa, R. S., Chang, R., Jie, W., Ziyuan, Z., Lile, C., Xun, X., Sheng, F. C., Choong, C. S., & Rao, V. S. (2023). 3D Defect Detection and Metrology of HBMs using Semi-Supervised Deep Learning. 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC). https://doi.org/10.1109/ectc51909.2023.00161
Abstract:
3D Deep Learning has made tremendous progress recently and is being widely used in various fields, such as medical imaging, robotics, and autonomous vehicle driving, to identify and segment various structures. In this work, we leverage the recent developments in 3D semi-supervised learning to develop state-of-the-art models for 3D object detection and segmentation for various buried structures such as memory and logic die. We briefly describe our approach to fabricating, generating 3D scans, and annotating these samples. Thereafter, we explain our approach to locating these buried structures by demonstrating how semi-supervised learning is adopted to leverage vast amounts of available unlabeled data to improve both detection and segmentation performance. We also develop a metrology package that performs post-processing and outputs various important metrics for each package, such as void-to-solder ratio, pad misalignment, solder extrusion, and bond line thickness. Overall, we observe an improvement of up to 16% in object detection and 6% in 3D segmentation. Our final metrology results show a mean error of less than 1.24um for BLT and 0.753um for Pad misalignment when compared to the ground-truth labeled data.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C210812046

This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151
Description:
© 2023 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:
2377-5726
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