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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| Language: | English |
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IEEE
2014
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| Online Access: | 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 |
| _version_ | 1848817717799288832 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T01:26:13Z |
| format | Article |
| id | ump-6190 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:26:13Z |
| publishDate | 2014 |
| publisher | IEEE |
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| 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. (Published) http://dx.doi.org/10.1109/TNNLS.2014.2329097 DOI: 10.1109/TNNLS.2014.2329097 |
| 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 |
| title | 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_short | Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects |
| title_sort | evolutionary fuzzy artmap neural networks for classification of semiconductor defects |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| 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 |