Evaluation of the Transfer Learning Models 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 and Lim, Shi Xuen (2022) Evaluation of the Transfer Learning Models in Wafer Defects Classification. In: Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles from iM3F 2020, Malaysia , 6 August 2020 , Universiti Malaysia Pahang (Virtual). pp. 873-881., 730. ISBN https://doi.org/10.1007/978-981-33-4597-3_78

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Abstract

In a semiconductor industry, wafer defect detection has becoming ubiquitous. Various machine learning algorithms had been adopted to be the “brain” behind the machine for reliable, fast defect detection. Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had been developed for different applications. In this paper, an evaluation for these transfer learning to be applied in wafer defect detection. The objective is to establish the best transfer learning algorithms with a known baseline parameter for Wafer Defect Detection. Five algorithms were evaluated namely VGG16, VGG19, InceptionV3, DeepLoc and Squeezenet. All the algorithms were pretrained from ImageNet data-base before training with the wafer defect images. Three defects categories and one non-defect were chosen for this evaluation. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. Each image went through the embedding process by the evaluated algorithms. This enhanced image data numbers then went through Logistic Regression as a classifier. A 20-fold cross-validation was used to validate the score metrics. Almost all the algorithms score 85% and above in terms of accuracy, precision and recall

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by SCOPUS, Part of the Lecture Notes in Electrical Engineering book series
Uncontrolled Keywords:	Transfer learning, VGG16, Wafer defect detection
Subjects:	T Technology > TJ Mechanical engineering and machinery
Faculty/Division:	Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Noorul Farina Arifin
Date Deposited:	13 Mar 2023 03:27
Last Modified:	13 Mar 2023 03:43
URI:	http://umpir.ump.edu.my/id/eprint/36763
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