Cai, L., Pahwa, R. S., Xu, X., Wang, J., Chang, R., Zhang, L., & Foo, C.-S. (2022). Exploring Active Learning for Semiconductor Defect Segmentation. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9897842
The development of X-Ray microscopy (XRM) technology has enabled non-destructive inspection of semiconductor structures for defect identification. Deep learning is widely used as the state-of-the-art approach to perform visual analysis tasks. However, deep learning based models require large amount of annotated data to train. This can be time-consuming and expensive to obtain especially for dense prediction tasks like semantic segmentation. In this work, we explore active learning (AL) as a potential solution to alleviate the annotation burden. We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance. To address these challenges, we propose to perform contrastive pretraining on the unlabelled data to obtain the initialization weights for each AL cycle, and a rareness-aware acquisition function that favors the selection of samples containing rare classes. We evaluate our method on a semiconductor dataset that is compiled from XRM scans of high bandwidth memory structures composed of logic and memory dies, and demonstrate that our method achieves state-of-the-art performance.
This research / project is supported by the Agency for Science, Technologyand Research (A*STAR) - AME Programmatic Fund
Grant Reference no. : A20H6b0151
This research / project is supported by the Agency for Science, Technologyand Research (A*STAR) - Career Development Fund
Grant Reference no. : C210812046