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/ http://umpir.ump.edu.my/id/eprint/6190/ http://umpir.ump.edu.my/id/eprint/6190/ http://umpir.ump.edu.my/id/eprint/6190/1/Evolutionary_Fuzzy_ARTMAP_Neural_Networks_for_Classification_of_Semiconductor_Defects.pdf

id	ump-6190
recordtype	eprints
spelling	ump-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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/ http://umpir.ump.edu.my/id/eprint/6190/ http://umpir.ump.edu.my/id/eprint/6190/ http://umpir.ump.edu.my/id/eprint/6190/1/Evolutionary_Fuzzy_ARTMAP_Neural_Networks_for_Classification_of_Semiconductor_Defects.pdf
first_indexed	2023-09-18T22:01:45Z
last_indexed	2023-09-18T22:01:45Z
_version_	1777414445168852992

Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

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