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,...

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Autores principales:	Jessnor Arif Mat Jizat, Anwar P.P. Abdul Majeed, Ahmad Fakhri Ab. Nasir, Zahari Taha, Edmund Yuen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Logistic Regression Stochastic Gradient Descend Wafer defect detection Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/567f65f56f124228831b6f31d2f5757e
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spelling	oai:doaj.org-article:567f65f56f124228831b6f31d2f5757e2021-11-30T04:16:43ZEvaluation of the machine learning classifier in wafer defects classification2405-959510.1016/j.icte.2021.04.007https://doaj.org/article/567f65f56f124228831b6f31d2f5757e2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000515https://doaj.org/toc/2405-9595In 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.Jessnor Arif Mat JizatAnwar P.P. Abdul MajeedAhmad Fakhri Ab. NasirZahari TahaEdmund YuenElsevierarticleLogistic RegressionStochastic Gradient DescendWafer defect detectionInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 535-539 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Logistic Regression Stochastic Gradient Descend Wafer defect detection Information technology T58.5-58.64
spellingShingle	Logistic Regression Stochastic Gradient Descend Wafer defect detection Information technology T58.5-58.64 Jessnor Arif Mat Jizat Anwar P.P. Abdul Majeed Ahmad Fakhri Ab. Nasir Zahari Taha Edmund Yuen Evaluation of the machine learning classifier in wafer defects classification
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.
format	article
author	Jessnor Arif Mat Jizat Anwar P.P. Abdul Majeed Ahmad Fakhri Ab. Nasir Zahari Taha Edmund Yuen
author_facet	Jessnor Arif Mat Jizat Anwar P.P. Abdul Majeed Ahmad Fakhri Ab. Nasir Zahari Taha Edmund Yuen
author_sort	Jessnor Arif Mat Jizat
title	Evaluation of the machine learning classifier in wafer defects classification
title_short	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_sort	evaluation of the machine learning classifier in wafer defects classification
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/567f65f56f124228831b6f31d2f5757e
work_keys_str_mv	AT jessnorarifmatjizat evaluationofthemachinelearningclassifierinwaferdefectsclassification AT anwarppabdulmajeed evaluationofthemachinelearningclassifierinwaferdefectsclassification AT ahmadfakhriabnasir evaluationofthemachinelearningclassifierinwaferdefectsclassification AT zaharitaha evaluationofthemachinelearningclassifierinwaferdefectsclassification AT edmundyuen evaluationofthemachinelearningclassifierinwaferdefectsclassification
_version_	1718406802334285824

Evaluation of the machine learning classifier in wafer defects classification

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