Exploring Active Learning for Semiconductor Defect Segmentation

• URL: http://arxiv.org/abs/2507.17359v1
• Date: Wed, 23 Jul 2025 09:44:11 GMT
• Title: Exploring Active Learning for Semiconductor Defect Segmentation
• Authors: Lile Cai, Ramanpreet Singh Pahwa, Xun Xu, Jie Wang, Richard Chang, Lining Zhang, Chuan-Sheng Foo,
• Abstract summary: In this work, we explore active learning (AL) as a potential solution to alleviate the annotation burden.<n>We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance.<n>To address these challenges, we propose to perform contrastive pretraining on the unlabelled data.<n>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.
• Score: 20.72106200701627
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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