Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Batool, Uzma, Mohd Ibrahim, Shapiai, Mostafa, Salama A., Mohd Zamri, Ibrahim
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/40649/1/An%20attention-augmented%20convolutional%20neural%20network.pdf
http://umpir.ump.edu.my/id/eprint/40649/
https://doi.org/10.1109/ACCESS.2023.3321025
https://doi.org/10.1109/ACCESS.2023.3321025
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.40649
record_format eprints
spelling my.ump.umpir.406492024-04-30T06:42:10Z 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
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Article
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
title 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_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_sort attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40649/1/An%20attention-augmented%20convolutional%20neural%20network.pdf
http://umpir.ump.edu.my/id/eprint/40649/
https://doi.org/10.1109/ACCESS.2023.3321025
https://doi.org/10.1109/ACCESS.2023.3321025
_version_ 1822924250386792448
score 13.232414

Similar Items