Exploring Active Learning for Semiconductor Defect Segmentation

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Exploring Active Learning for Semiconductor Defect Segmentation
Title:
Exploring Active Learning for Semiconductor Defect Segmentation
Other Titles:
2022 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
18 October 2022
Citation:
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
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
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
Description:
© 2022 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:
2381-8549
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