Evaluation of the Transfer Learning Models in Wafer Defects Classification

• Main Authors: Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen
• Format: Conference or Workshop Item
• Language: English
• Published: Springer Nature 2022
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/36763/1/Evaluation%20of%20the%20Transfer%20Learning%20Models%20in%20Wafer%20Defects%20Classification%20%281%29.pdf; http://umpir.ump.edu.my/id/eprint/36763/; https://doi.org/10.1007/978-981-33-4597-3_78
• Institution: Universiti Malaysia Pahang

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