An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification Article Swipe
Uzma Batool
,
Mohd Ibrahim Shapiai
,
Salama A. Mostafa
,
Mohd Zamri Ibrahim
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3321025
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3321025
Silicon wafer defect classification is crucial for improving fabrication and chip production. Although deep learning methods have been successful in single-defect wafer classification, the increasing complexity of the fabrication process has introduced the challenge of multiple defects on wafers, which requires more robust feature learning and classification techniques. Attention mechanisms have been used to enhance feature learning for multiple wafer defects. However, they have limited use in a few mixed-type defect categories, and their performance declines as the number of mixed patterns increases. This work proposes an attention-augmented convolutional neural networks (A2CNN) model for enhanced discriminative feature learning of complex defects. The A2CNN model emphasizes the features in the channel and spatial dimensions. Additionally, the model adopts the focal loss function to reduce misclassification and a global average pooling layer to enhance the network’s generalization by reducing overfitting. The A2CNN model is evaluated on the MixedWM38 wafer defect dataset using 10-fold cross-validation. It achieves impressive results, with accuracy, precision, recall, and F1-score reported as 98.66%, 99.0%, 98.55%, and 98.82% respectively. Compared to existing works, the A2CNN model performs better by effectively learning valuable information for complex mixed-type wafer defects.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3321025
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10268403.pdf
- OA Status
- gold
- Cited By
- 12
- References
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- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387247620Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2023.3321025Digital Object Identifier
- Title
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An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Uzma Batool, Mohd Ibrahim Shapiai, Salama A. Mostafa, Mohd Zamri IbrahimList of authors in order
- Landing page
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https://doi.org/10.1109/access.2023.3321025Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10268403.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10268403.pdfDirect OA link when available
- Concepts
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Overfitting, Discriminative model, Computer science, Artificial intelligence, Pooling, Convolutional neural network, Wafer, Deep learning, Feature (linguistics), Pattern recognition (psychology), Artificial neural network, Machine learning, Materials science, Optoelectronics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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12Total citation count in OpenAlex
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2025: 9, 2024: 3Per-year citation counts (last 5 years)
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75Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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