Chang, R., Wang, J., Yu, Y., Lim, M. K., Chong, S. C., Pahwa, R. S., & Yang, X. (2025). Generative AI-Powered Defect Detection for 3D X-Ray Microscopy Scans of High Bandwidth Memory Bumps. In (Editor), 2025 IEEE 27th Electronics Packaging Technology Conference (EPTC). https://doi.org/10.1109/eptc67330.2025.11392621
Abstract:
Defect detection in 2.5D-3D packages is key in the manufacturing process to ensure product quality and reliability. With the increasing miniaturization of microbumps in integrated circuits (IC), this task has been more challenging and time-consuming. With recent advances in deep learning AI and generative AI, the inspection process can be further enhanced. Deep learning models provide fast and accurate defect detection and metrology, and generative AI provides insights and meaningful information on defects to the operator. In this paper, we propose a framework that uses generative AI to leverage on multiple sources of data such as fabrication parameters, technical documents or historical analysis to provide a highly accurate and comprehensive report of the analysed samples. We integrate the hierarchical retrieval-augmented generation framework to build better prompts and queries to large language models. With this framework, the large language model can search in specific documents with better context and accurate information and prioritize relevant information related to a specific defect or sample. We demonstrate that our approach provides faster and more accurate inspection report with 11% improvement compared to a basic report.
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