Evaluation of the machine learning classifier in wafer defects classification

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


In this paper, an evaluation of machine learning classifiers to be applied in wafer defect detection is described. The objective is to establish the best machine learning classifier for Wafer Defect Detection application. k-Nearest Neighbours (k-NN), Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine were evaluated with 3 defects categories and one non-defect category. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. Each image went through the embedding process by InceptionV3 algorithms before the evaluated classifier classifies the images.

Original languageEnglish
Pages (from-to)535-539
Number of pages5
JournalICT Express
Issue number4
Publication statusPublished - Dec 2021
Externally publishedYes


  • Logistic Regression
  • Stochastic Gradient Descend
  • Wafer defect detection


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