TY - GEN
T1 - The Classification of Wafer Defects
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2022
AU - Xuen, Lim Shi
AU - Mohd Khairuddin, Ismail
AU - Mohd Razman, Mohd Azraai
AU - Mat Jizat, Jessnor Arif
AU - Yuen, Edmund
AU - Yap, Eng Hwa
AU - Tan, Andrew Huey Ping
AU - Abdul Majeed, Anwar P.P.
N1 - Funding Information:
Acknowledgements The authors would like to thank IdealVision Sdn Bhd for providing the image dataset as well as Universiti Malaysia Pahang for funding the study via UIC200815 and RDU202404.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection are often labour-intensive based that is prone to error owing to a myriad of issue. Hence, there is push towards automatic defect detection in the industry. This work shall investigate the efficacy of a transfer learning pipeline that consists of a different pre-trained ResNet convolutional neural network models in which its fully connected layer is swapped with different support vector machine (SVM) models in classifying the defect state of a wafer whether it pass or fail. The optimal hyperparameters are identified via the grid-search technique. It was shown from the present investigation that the features extracted via the ResNet101v2 transfer learning model with a linear-based SVM model with a C and gamma parameter of 0.01, respectively, could yield a validation and test classification accuracy of 96% and 94%, respectively, on a stratified 60:20:20 data split ratio. The result from the present study demonstrates that the proposed pipeline is able to classify the defect level of the wafer well.
AB - Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection are often labour-intensive based that is prone to error owing to a myriad of issue. Hence, there is push towards automatic defect detection in the industry. This work shall investigate the efficacy of a transfer learning pipeline that consists of a different pre-trained ResNet convolutional neural network models in which its fully connected layer is swapped with different support vector machine (SVM) models in classifying the defect state of a wafer whether it pass or fail. The optimal hyperparameters are identified via the grid-search technique. It was shown from the present investigation that the features extracted via the ResNet101v2 transfer learning model with a linear-based SVM model with a C and gamma parameter of 0.01, respectively, could yield a validation and test classification accuracy of 96% and 94%, respectively, on a stratified 60:20:20 data split ratio. The result from the present study demonstrates that the proposed pipeline is able to classify the defect level of the wafer well.
KW - DenseNet
KW - Transfer learning
KW - Wafer inspection
UR - http://www.scopus.com/inward/record.url?scp=85151048848&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-8703-8_23
DO - 10.1007/978-981-19-8703-8_23
M3 - Conference Proceeding
AN - SCOPUS:85151048848
SN - 9789811987021
T3 - Lecture Notes in Electrical Engineering
SP - 277
EP - 283
BT - Advances in Intelligent Manufacturing and Mechatronics - Selected Articles from the Innovative Manufacturing, Mechatronics and Materials Forum iM3F 2022
A2 - Abdullah, Muhammad Amirul
A2 - Khairuddin, Ismail Mohd.
A2 - Mohd. Isa, Wan Hasbullah
A2 - Mohd. Razman, Mohd. Azraai
A2 - Rasid, Mohd. Azri Hizami
A2 - Zainal, Sheikh Muhammad Hafiz Fahami
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Bentley, Barry
A2 - Liu, Pengcheng
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 20 July 2022 through 20 July 2022
ER -