TY - JOUR
T1 - Evaluation of the machine learning classifier in wafer defects classification
AU - Mat Jizat, Jessnor Arif
AU - P.P. Abdul Majeed, Anwar
AU - Ahmad, Ahmad Fakhri
AU - Taha, Zahari
AU - Yuen, Edmund
N1 - Funding Information:
The authors would like to thank IdealVision Sdn Bhd for providing the image dataset to make this evaluation possible as well as Universiti Malaysia Pahang for funding the study via UIC200815 and RDU202404 .
Publisher Copyright:
© 2021 The Korean Institute of Communications and Information Sciences (KICS)
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Logistic Regression
KW - Stochastic Gradient Descend
KW - Wafer defect detection
UR - http://www.scopus.com/inward/record.url?scp=85106225336&partnerID=8YFLogxK
U2 - 10.1016/j.icte.2021.04.007
DO - 10.1016/j.icte.2021.04.007
M3 - Article
AN - SCOPUS:85106225336
SN - 2405-9595
VL - 7
SP - 535
EP - 539
JO - ICT Express
JF - ICT Express
IS - 4
ER -