Oversampling based on data augmentation in convolutional neural network for silicon wafer defect classification
Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for su...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/92785/ http://dx.doi.org/10.3233/FAIA200547 |
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