A systematic review of deep learning for silicon wafer defect recognition
Advancements in technology have made deep learning a hot research area, and we see its applications in various fields. Its widespread use in silicon wafer defect recognition is replacing traditional machine learning and image processing methods of defect monitoring. This article presents a review of...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/95105/1/UzmaBatool2021_ASystematicReviewofDeepLearning.pdf http://eprints.utm.my/id/eprint/95105/ http://dx.doi.org/10.1109/ACCESS.2021.3106171 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | Advancements in technology have made deep learning a hot research area, and we see its applications in various fields. Its widespread use in silicon wafer defect recognition is replacing traditional machine learning and image processing methods of defect monitoring. This article presents a review of the deep learning methods employed for wafer map defect recognition. A systematic literature review (SLR)has been conducted to determine how the semiconductor industry is being leveraged by advancements in deep learning research for wafer defects recognition and analysis. Forty-four articles from the well-known databases have been selected for this review. The detailed study of the selected articles identified the prominent deep learning algorithms and network architectures for wafer map defect classification, clustering, feature extraction, and data synthesis. The learning algorithms are grouped as supervised learning, unsupervised learning, and hybrid learning. The network architectures include different forms of Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and (Auto-encoder (AE). Issues of multi-class and multi-label defects have been addressed, solving data unavailability, class imbalance, instance labeling, and unknown defects. As future directions, it is recommended to invest more efforts in the accuracy of the data generation procedures and the defect pattern recognition frameworks for defect monitoring in real industrial environments. |
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