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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
LanguageEnglish
Title of host publicationIrish Machine Vision and Image Processing Conference
Pages183 - 189
Number of pages7
Publication statusPublished - 28 Aug 2019

Fingerprint

Learning systems
Inspection
Defects
Crystal defects
Quality control
Semiconductor materials
Industry

Keywords

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

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)
@inproceedings{00167b41385c4e15af8f960766788bd4,
title = "A Machine Learning Approach to Wafer Defect Classification using Bag of Visual Words",
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.",
keywords = "Image Processing, Bag of Visual Words, Machine Learning, Defect Classification",
author = "Terence Sweeney and Sonya Coleman and Dermot Kerr",
year = "2019",
month = "8",
day = "28",
language = "English",
isbn = "978-0-9934207-4-0",
pages = "183 -- 189",
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}

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.

A Machine Learning Approach to Wafer Defect Classification using Bag of Visual Words. / Sweeney, Terence; Coleman, Sonya; Kerr, Dermot.

Irish Machine Vision and Image Processing Conference. 2019. p. 183 - 189.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Machine Learning Approach to Wafer Defect Classification using Bag of Visual Words

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AU - Coleman, Sonya

AU - Kerr, Dermot

PY - 2019/8/28

Y1 - 2019/8/28

N2 - 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.

AB - 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.

KW - Image Processing

KW - Bag of Visual Words

KW - Machine Learning

KW - Defect Classification

M3 - Conference contribution

SN - 978-0-9934207-4-0

SP - 183

EP - 189

BT - Irish Machine Vision and Image Processing Conference

ER -

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