Evaluation of the machine learning classifier in wafer defects classification
In this paper, an evaluation of machine learning classifiers to be applied in wafer defect detection is described. The objective is to establish the best machine learning classifier for Wafer Defect Detection application. k-Nearest Neighbours (k-NN), Logistic Regression, Stochastic Gradient Descent,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31740/ http://umpir.ump.edu.my/id/eprint/31740/1/Evaluation%20of%20the%20machine%20learning%20classifier%20in%20wafer%20defects%20classification.pdf |
| _version_ | 1848823844779851776 |
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| author | Jessnor Arif, Mat Jizat Anwar, P. P. Abdul Majeed Ahmad Fakhri, Ab. Nasir Zahari, Taha Yuen, Edmund |
| author_facet | Jessnor Arif, Mat Jizat Anwar, P. P. Abdul Majeed Ahmad Fakhri, Ab. Nasir Zahari, Taha Yuen, Edmund |
| author_sort | Jessnor Arif, Mat Jizat |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In this paper, an evaluation of machine learning classifiers to be applied in wafer defect detection is described. The objective is to establish the best machine learning classifier for Wafer Defect Detection application. k-Nearest Neighbours (k-NN), Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine were evaluated with 3 defects categories and one non-defect category. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. Each image went through the embedding process by InceptionV3 algorithms before the evaluated classifier classifies the images. |
| first_indexed | 2025-11-15T03:03:36Z |
| format | Article |
| id | ump-31740 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:03:36Z |
| publishDate | 2021 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-317402021-07-30T07:58:11Z http://umpir.ump.edu.my/id/eprint/31740/ Evaluation of the machine learning classifier in wafer defects classification Jessnor Arif, Mat Jizat Anwar, P. P. Abdul Majeed Ahmad Fakhri, Ab. Nasir Zahari, Taha Yuen, Edmund TS Manufactures In this paper, an evaluation of machine learning classifiers to be applied in wafer defect detection is described. The objective is to establish the best machine learning classifier for Wafer Defect Detection application. k-Nearest Neighbours (k-NN), Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine were evaluated with 3 defects categories and one non-defect category. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. Each image went through the embedding process by InceptionV3 algorithms before the evaluated classifier classifies the images. Elsevier 2021-05-03 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/31740/1/Evaluation%20of%20the%20machine%20learning%20classifier%20in%20wafer%20defects%20classification.pdf Jessnor Arif, Mat Jizat and Anwar, P. P. Abdul Majeed and Ahmad Fakhri, Ab. Nasir and Zahari, Taha and Yuen, Edmund (2021) Evaluation of the machine learning classifier in wafer defects classification. ICT Express. pp. 1-5. ISSN 2405-9595. (In Press / Online First) (In Press / Online First) https://doi.org/10.1016/j.icte.2021.04.007 https://doi.org/10.1016/j.icte.2021.04.007 |
| spellingShingle | TS Manufactures Jessnor Arif, Mat Jizat Anwar, P. P. Abdul Majeed Ahmad Fakhri, Ab. Nasir Zahari, Taha Yuen, Edmund Evaluation of the machine learning classifier in wafer defects classification |
| title | Evaluation of the machine learning classifier in wafer defects classification |
| title_full | Evaluation of the machine learning classifier in wafer defects classification |
| title_fullStr | Evaluation of the machine learning classifier in wafer defects classification |
| title_full_unstemmed | Evaluation of the machine learning classifier in wafer defects classification |
| title_short | Evaluation of the machine learning classifier in wafer defects classification |
| title_sort | evaluation of the machine learning classifier in wafer defects classification |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/31740/ http://umpir.ump.edu.my/id/eprint/31740/ http://umpir.ump.edu.my/id/eprint/31740/ http://umpir.ump.edu.my/id/eprint/31740/1/Evaluation%20of%20the%20machine%20learning%20classifier%20in%20wafer%20defects%20classification.pdf |