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Evaluation of the machine learning classifier in wafer defects classification

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)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Logistic regression; Stochastic gradient descend; Wafer defect detection
Subjects: T Technology > TS Manufactures
Faculty/Division: Institute of Postgraduate Studies
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 30 Jul 2021 07:58
Last Modified: 30 Jul 2021 07:58
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