A Machine Learning Approach to Wafer Defect Classification using Bag of Visual Words

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Abstract

Automated Inspection has become a vital part of quality control during semiconductor wafer production. However, current automated processes take a large amount of time to set-up and also require significant computational resources in order to achieve good accuracy, with often only binary classification (Pass/Fail) facilitated. This paper presents an alternative to current industry inspection practices by using local image features and machine learning to detect and classify defects upon semiconductor wafers in order to differentiate between different types of defects.
Original languageEnglish
Title of host publicationIrish Machine Vision and Image Processing Conference
Pages183 - 189
Number of pages7
Publication statusPublished - 28 Aug 2019

Keywords

  • Image Processing
  • Bag of Visual Words
  • Machine Learning
  • Defect Classification

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  • Cite this

    Sweeney, T., Coleman, S., & Kerr, D. (2019). A Machine Learning Approach to Wafer Defect Classification using Bag of Visual Words. In Irish Machine Vision and Image Processing Conference (pp. 183 - 189)