The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation

Lim Shi Xuen, Ismail Mohd Khairuddin, Mohd Azraai Mohd Razman, Jessnor Arif Mat Jizat, Edmund Yuen, Haochuan Jiang, Eng Hwa Yap, Anwar P. P. Abdul Majeed*

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection is often labor-intensive based that is prone to error owing to a myriad of issue. Hence, there is push toward automatic defect detection in the industry. This work shall investigate the efficacy of a transfer learning pipeline that consists of different pre-trained DenseNet convolutional neural network models in which its fully connected layer is swapped with different support vector machine (SVM) models in classifying the defect state of a wafer whether it passes or fail. The optimal hyperparameters are identified via the grid search technique. It was shown from the present investigation that the features extracted via the DenseNet121 transfer learning model with a linear-based SVM model with a C and gamma parameter of 0.01, respectively, could yield a validation and test classification accuracy of 93% and 86%, respectively on a stratified 60:20:20 data split ratio. The result from the present study demonstrates that the proposed pipeline is able to classify the defect level of the wafer well.

Original languageEnglish
Title of host publicationRobot Intelligence Technology and Applications 7 - Results from the 10th International Conference on Robot Intelligence Technology and Applications
EditorsJun Jo, Han-Lim Choi, Marde Helbig, Hyondong Oh, Jemin Hwangbo, Chang-Hun Lee, Bela Stantic
PublisherSpringer Science and Business Media Deutschland GmbH
Pages304-309
Number of pages6
ISBN (Print)9783031268885
DOIs
Publication statusPublished - 2023
Event10th International Conference on Robot Intelligence Technology and Applications, RiTA 2022 - Gold Coast, Australia
Duration: 7 Dec 20229 Dec 2022

Publication series

NameLecture Notes in Networks and Systems
Volume642 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference10th International Conference on Robot Intelligence Technology and Applications, RiTA 2022
Country/TerritoryAustralia
CityGold Coast
Period7/12/229/12/22

Keywords

  • DenseNet
  • Transfer learning
  • Wafer inspection

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