Evaluation of the machine learning classifier in wafer defects classification
Jessnor Arif Mat Jizat,
Anwar P.P. Abdul Majeed,
Ahmad Fakhri Ab. Nasir,
Zahari Taha,
Edmund Yuen
Affiliations
Jessnor Arif Mat Jizat
Faculty of Manufacturing & Mechatronic Engineering Technology (FTKPM), Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia; Corresponding author.
Anwar P.P. Abdul Majeed
Faculty of Manufacturing & Mechatronic Engineering Technology (FTKPM), Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia; Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, 26600, Malaysia
Ahmad Fakhri Ab. Nasir
Faculty of Manufacturing & Mechatronic Engineering Technology (FTKPM), Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia; Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, 26600, Malaysia
Zahari Taha
Fakultas Teknologi Industri, Universitas Islam Indonesia, Jl. Kaliurang KM. 14,5 Sleman Yogyakarta 55584, Indonesia
Edmund Yuen
Ideal Vision Integration Sdn Bhd, 02-25, Level 2, Setia Spice Canopy, Jln Tun Dr Awang, 11900 Bayan Lepas, Penang, Malaysia
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.
