Yu, Y., Lu, K., Wang, J., Chang, R., Lim, M. K., Chong, S. C., Pahwa, R. S., & Yang, X. (2025). AI-Powered Super-Resolution for Scalable and Efficient 3D X-Ray Inspection of 3D-Stacked HBMs. 2025 IEEE 27th Electronics Packaging Technology Conference (EPTC), 1–7. https://doi.org/10.1109/eptc67330.2025.11392237
Abstract:
Defect detection in semiconductor packaging, particularly for 3D High Bandwidth Memory (HBM) packages, is critical for ensuring manufacturing quality and long-term reliability. However, accurate detection of fine structural defects in complex 3D-stacked HBM packages demands high spatial resolution scanning, which in turn requires long acquisition times. Such requirements make conventional high-resolution 3D X-ray imaging impractical for high-throughput industrial settings. To overcome this limitation, we introduce a deep learning–based framework that enables resolution-enhanced 3D X-ray imaging from low-fidelity acquisitions. Our approach leverages super-resolution networks to enhance low-fidelity volumes from fewer scans, thereby reducing dependency on expensive high-resolution imaging systems. This computational enhancement facilitates scalable, non-destructive inspection of advanced semiconductor packages without compromising defect detectability. Extensive experiments on industrial HBM datasets show that our method reduces acquisition time by up to 80% while maintaining comparable quality of spatial resolution and improving sample quality for downstream defect analysis. By significantly lowering both imaging and computational costs, our work offers a practical pathway towards the widespread deployment of AI-driven, high-throughput semiconductor inspection systems.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021