Persistent Link:
http://hdl.handle.net/10150/276831
Title:
IC defect detection using color information and image processing
Author:
Yang, Hsien-Min, 1957-
Issue Date:
1988
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Most current commercial automated IC inspection systems use gray-level or binary images for IC defect detection in spite of the fact that color permits defect detection where gray-level information is insufficient. Three color image processing techniques including the spectral-spatial clustering, principal components, and hue-saturation-value (HSV) color features have been investigated to evaluate the usefulness of color for IC defect detection. The AMOEBA spectral-spatial clustering algorithm, an un-supervised color segmentation approach, with a sequence of image processing procedures resulted in segmentation results with high accuracy and discriminated successfully an isolated and homogeneous defect with an unique color signature. The principal components transformation and the HSV color features, two color enhancement/separation algorithms, have proven useful for enhancing and isolating weak spectral signatures in the defect regions. The results of this investigation into the use of color are promising.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Integrated circuits -- Defects.; Integrated circuits -- Inspection.; Color computer graphics.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Schowengerdt, Robert

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleIC defect detection using color information and image processingen_US
dc.creatorYang, Hsien-Min, 1957-en_US
dc.contributor.authorYang, Hsien-Min, 1957-en_US
dc.date.issued1988en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractMost current commercial automated IC inspection systems use gray-level or binary images for IC defect detection in spite of the fact that color permits defect detection where gray-level information is insufficient. Three color image processing techniques including the spectral-spatial clustering, principal components, and hue-saturation-value (HSV) color features have been investigated to evaluate the usefulness of color for IC defect detection. The AMOEBA spectral-spatial clustering algorithm, an un-supervised color segmentation approach, with a sequence of image processing procedures resulted in segmentation results with high accuracy and discriminated successfully an isolated and homogeneous defect with an unique color signature. The principal components transformation and the HSV color features, two color enhancement/separation algorithms, have proven useful for enhancing and isolating weak spectral signatures in the defect regions. The results of this investigation into the use of color are promising.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectIntegrated circuits -- Defects.en_US
dc.subjectIntegrated circuits -- Inspection.en_US
dc.subjectColor computer graphics.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSchowengerdt, Roberten_US
dc.identifier.proquest1335395en_US
dc.identifier.oclc21708636en_US
dc.identifier.bibrecord.b17300447en_US
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