The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation

Ismail, Mohd Khairuddin and Lim, Shi Xuen and Mohd Azraai, Mohd Razman and Jessnor Arif, Mat Jizat and Yuen, Edmund and Jiang, Haochuan and Yap, Eng Hwa and Anwar, P. P. Abdul Majeed (2023) The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation. In: Robot Intelligence Technology and Applications 7: RiTA 2022 , 7-9 December 2022 , Daejeon, Korea. pp. 304-309., 642. ISBN 978-3-031-26889-2

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Abstract

Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection is often labor-intensive based that is prone to error owing to a myriad of issue. Hence, there is push toward automatic defect detection in the industry. This work shall investigate the efficacy of a transfer learning pipeline that consists of different pre-trained DenseNet 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 passes 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 DenseNet121 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 93% and 86%, 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.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by SCOPUS, Lecture Notes in Networks and Systems
Uncontrolled Keywords:	Transfer learning, Wafer inspection, DenseNet
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Noorul Farina Arifin
Date Deposited:	13 Mar 2023 03:12
Last Modified:	13 Mar 2023 03:44
URI:	http://umpir.ump.edu.my/id/eprint/37269
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