An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification
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, wh...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40649/1/An%20attention-augmented%20convolutional%20neural%20network.pdf |
| _version_ | 1825815531581603840 |
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| author | Batool, Uzma Mohd Ibrahim, Shapiai Mostafa, Salama A. Mohd Zamri, Ibrahim |
| author_facet | Batool, Uzma Mohd Ibrahim, Shapiai Mostafa, Salama A. Mohd Zamri, Ibrahim |
| author_sort | Batool, Uzma |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-09-25T03:48:22Z |
| format | Article |
| id | UMPir40649 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:48:22Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir406492024-04-30T06:42:10Z http://umpir.ump.edu.my/id/eprint/40649/ An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification Batool, Uzma Mohd Ibrahim, Shapiai Mostafa, Salama A. Mohd Zamri, Ibrahim T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. Institute of Electrical and Electronics Engineers Inc. 2023 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/40649/1/An%20attention-augmented%20convolutional%20neural%20network.pdf Batool, Uzma and Mohd Ibrahim, Shapiai and Mostafa, Salama A. and Mohd Zamri, Ibrahim (2023) An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification. IEEE Access, 11. 108891 -108905. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2023.3321025 https://doi.org/10.1109/ACCESS.2023.3321025 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Batool, Uzma Mohd Ibrahim, Shapiai Mostafa, Salama A. Mohd Zamri, Ibrahim An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification |
| title | An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification |
| title_full | An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification |
| title_fullStr | An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification |
| title_full_unstemmed | An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification |
| title_short | An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification |
| title_sort | attention augmented convolutional neural network with focal loss for mixed type wafer defect classification |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/40649/1/An%20attention-augmented%20convolutional%20neural%20network.pdf |
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