Jie, W., Chang, R., Xun, X., Lile, C., Foo, C. S., & Pahwa, R. S. (2022). Improved Bump Detection and Defect Identification for HBMs using Refined Machine Learning Approach. 2022 IEEE 24th Electronics Packaging Technology Conference (EPTC). https://doi.org/10.1109/eptc56328.2022.10013164
The 2D-3D metrology is a critical step for in-line inspection and off-line failure analysis. Due to lack of relevant data and complexity of embedded components, identifying and segmenting defects such as voids, pad misalignments in 2D and 3D voxel data has been a challenge in the semiconductor industry. Addressing this problem has the potential to further improve fault detection in this field significantly. This work follows our previously published works in EPTC 2020, ECTC 2021, introducing a cost-effective and non-destructive approach using deep learning and 3D x-ray microscopy. In particular, we apply our 3D object detection and Semi-Supervised Learning (SSL) image segmentation on High Bandwidth memory and logic bumps (HBMs). This paper introduces new detection and segmentation methods that overcomes issues in the current data such as data imbalance or defective bumps. We applied better 2D-3D detection strategy and loss and activation functions for 3D semicon data. We describe the data features, our new approach on 2D-3D scanned data, methods developed to perform better object detection and segmentation to classify each pixel into individual categories such as solders, voids, Cu-Pillars, and Cu-Pad. We analyze in-depth observations from our new models and discuss the benefits and improvements of our revised approach.
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund
Grant Reference no. : C210812046
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Funds
Grant Reference no. : A20H6b0151