Evaluation of the Transfer Learning Models in Wafer Defects Classification

Jessnor Arif Mat Jizat*, Anwar P.P. Abdul Majeed, Ahmad Fakhri Ahmad, Zahari Taha, Edmund Yuen, Shi Xuen Lim

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationRecent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
EditorsAhmad Fakhri Ab. Nasir, Ahmad Najmuddin Ibrahim, Ismayuzri Ishak, Nafrizuan Mat Yahya, Muhammad Aizzat Zakaria, Anwar P. P. Abdul Majeed
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9789813345966
Publication statusPublished - 2022
Externally publishedYes
EventInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 - Gambang, Malaysia
Duration: 6 Aug 20206 Aug 2020

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020


  • Transfer learning
  • VGG16
  • Wafer defect detection


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