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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they ar...

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Main Authors: Zuwairie, Ibrahim, Tan, Shing Chiang, Watada, Junzo, Marzuki, Khalid
Format: Article
Language:English
Published: IEEE 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/6190/1/Evolutionary_Fuzzy_ARTMAP_Neural_Networks_for_Classification_of_Semiconductor_Defects.pdf
http://umpir.ump.edu.my/id/eprint/6190/
http://dx.doi.org/10.1109/TNNLS.2014.2329097
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spelling my.ump.umpir.61902018-02-08T00:33:54Z http://umpir.ump.edu.my/id/eprint/6190/ Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects Zuwairie, Ibrahim Tan, Shing Chiang Watada, Junzo Marzuki, Khalid TK Electrical engineering. Electronics Nuclear engineering Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets. IEEE 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6190/1/Evolutionary_Fuzzy_ARTMAP_Neural_Networks_for_Classification_of_Semiconductor_Defects.pdf Zuwairie, Ibrahim and Tan, Shing Chiang and Watada, Junzo and Marzuki, Khalid (2014) Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects. IEEE Transactions on Neural Networks and Learning Systems, 26 (5). pp. 933-950. ISSN 2162-237X http://dx.doi.org/10.1109/TNNLS.2014.2329097 DOI: 10.1109/TNNLS.2014.2329097
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zuwairie, Ibrahim
Tan, Shing Chiang
Watada, Junzo
Marzuki, Khalid
Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
description Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.
format Article
author Zuwairie, Ibrahim
Tan, Shing Chiang
Watada, Junzo
Marzuki, Khalid
author_facet Zuwairie, Ibrahim
Tan, Shing Chiang
Watada, Junzo
Marzuki, Khalid
author_sort Zuwairie, Ibrahim
title Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
title_short Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
title_full Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
title_fullStr Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
title_full_unstemmed Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
title_sort evolutionary fuzzy artmap neural networks for classification of semiconductor defects
publisher IEEE
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/6190/1/Evolutionary_Fuzzy_ARTMAP_Neural_Networks_for_Classification_of_Semiconductor_Defects.pdf
http://umpir.ump.edu.my/id/eprint/6190/
http://dx.doi.org/10.1109/TNNLS.2014.2329097
_version_ 1643665324691685376
score 13.211869

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