File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Simultaneous photometric correction and defect detection in semiconductor manufacturing

TitleSimultaneous photometric correction and defect detection in semiconductor manufacturing
Authors
KeywordsChange detection
Derivative model
Image registration
Linear dependence change detector
Phase Correlation Method (PCM)
Shading model
Statistical change detection
Wronskian change detection model
Issue Date2006
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
Proceedings Of Spie - The International Society For Optical Engineering, 2006, v. 6070 How to Cite?
AbstractThis paper reports on an image processing algorithm for simultaneous photometric correction and defect detection in semiconductor manufacturing. We note that this problem has some resemblance to change detection in real time image analysis. In particular, the changes between the two images are analogous to the defects in our machine vision system. We therefore applied several detection methods and examined their applicability to defect detection. We first performed a sub-pixel image registration, using a phase correlation method together with a singular value decomposition factorization of the correlation matrix to compute the necessary alignment. We then tested a few change detection methods, including the shading model, derivative model, statistical change detection, linear dependence change detector and Wronskian change detection model. We subjected this system to our collection of raw data acquired from an industrial system, and we evaluated the different methods with respect to the detection accuracy, robustness, and speed of the system. We have promising results at this stage, especially in detecting the blob and line defects that are most commonly found, and when the lighting variation is within a certain threshold. © 2006 SPIE-IS&T.
Persistent Identifierhttp://hdl.handle.net/10722/99578
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorShen, Yen_HK
dc.contributor.authorLam, EYen_HK
dc.date.accessioned2010-09-25T18:36:04Z-
dc.date.available2010-09-25T18:36:04Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings Of Spie - The International Society For Optical Engineering, 2006, v. 6070en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/99578-
dc.description.abstractThis paper reports on an image processing algorithm for simultaneous photometric correction and defect detection in semiconductor manufacturing. We note that this problem has some resemblance to change detection in real time image analysis. In particular, the changes between the two images are analogous to the defects in our machine vision system. We therefore applied several detection methods and examined their applicability to defect detection. We first performed a sub-pixel image registration, using a phase correlation method together with a singular value decomposition factorization of the correlation matrix to compute the necessary alignment. We then tested a few change detection methods, including the shading model, derivative model, statistical change detection, linear dependence change detector and Wronskian change detection model. We subjected this system to our collection of raw data acquired from an industrial system, and we evaluated the different methods with respect to the detection accuracy, robustness, and speed of the system. We have promising results at this stage, especially in detecting the blob and line defects that are most commonly found, and when the lighting variation is within a certain threshold. © 2006 SPIE-IS&T.en_HK
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_HK
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_HK
dc.subjectChange detectionen_HK
dc.subjectDerivative modelen_HK
dc.subjectImage registrationen_HK
dc.subjectLinear dependence change detectoren_HK
dc.subjectPhase Correlation Method (PCM)en_HK
dc.subjectShading modelen_HK
dc.subjectStatistical change detectionen_HK
dc.subjectWronskian change detection modelen_HK
dc.titleSimultaneous photometric correction and defect detection in semiconductor manufacturingen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailLam, EY:elam@eee.hku.hken_HK
dc.identifier.authorityLam, EY=rp00131en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.640138en_HK
dc.identifier.scopuseid_2-s2.0-33645654202en_HK
dc.identifier.hkuros117401en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33645654202&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6070en_HK
dc.identifier.spage133en_HK
dc.identifier.epage142en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridShen, Y=12804295400en_HK
dc.identifier.scopusauthoridLam, EY=7102890004en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats