Die defect classification using image processing

This work presents die defect classification using image processing. The detection of the flaw is based on the defect features in the die. Each unique defect or feature structure is defined from samples that has been collected by Visual Inspection Inspectors. The defects are then grouped into user d...

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Main Author:	Maniam, Darmadevaindra
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/53921/1/DarmadevaindraManiamMFKE2015.pdf
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id	my-utm-ep.53921
record_format	uketd_dc
spelling	my-utm-ep.539212020-10-08T03:32:14Z Die defect classification using image processing 2015-06 Maniam, Darmadevaindra TK Electrical engineering. Electronics Nuclear engineering This work presents die defect classification using image processing. The detection of the flaw is based on the defect features in the die. Each unique defect or feature structure is defined from samples that has been collected by Visual Inspection Inspectors. The defects are then grouped into user definition categories such as blob, pin hole, underfill and die crack.This work also describes the image processing algorithms utilized to perform defect classification. The defect classification was developed from MATLAB program.It is aimed at locating the Region of Interest of the die from the image and extract it. The extracted image is then used to classify or recognize the specific classification category of the defect.Total samples that is being used in this project is 67 die samples. The results obtained from this work shows the overall accuracy of 94% for die defect detection and 87% for defect classification. 2015-06 Thesis http://eprints.utm.my/id/eprint/53921/ http://eprints.utm.my/id/eprint/53921/1/DarmadevaindraManiamMFKE2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85629 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Maniam, Darmadevaindra Die defect classification using image processing
description	This work presents die defect classification using image processing. The detection of the flaw is based on the defect features in the die. Each unique defect or feature structure is defined from samples that has been collected by Visual Inspection Inspectors. The defects are then grouped into user definition categories such as blob, pin hole, underfill and die crack.This work also describes the image processing algorithms utilized to perform defect classification. The defect classification was developed from MATLAB program.It is aimed at locating the Region of Interest of the die from the image and extract it. The extracted image is then used to classify or recognize the specific classification category of the defect.Total samples that is being used in this project is 67 die samples. The results obtained from this work shows the overall accuracy of 94% for die defect detection and 87% for defect classification.
format	Thesis
qualification_level	Master's degree
author	Maniam, Darmadevaindra
author_facet	Maniam, Darmadevaindra
author_sort	Maniam, Darmadevaindra
title	Die defect classification using image processing
title_short	Die defect classification using image processing
title_full	Die defect classification using image processing
title_fullStr	Die defect classification using image processing
title_full_unstemmed	Die defect classification using image processing
title_sort	die defect classification using image processing
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/53921/1/DarmadevaindraManiamMFKE2015.pdf
_version_	1747817658129055744

Die defect classification using image processing

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