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dc.contributor.authorGereige, Issam
dc.contributor.authorRobert, Stéphane
dc.contributor.authorEid, Jessica
dc.date.accessioned2015-08-03T09:59:32Z
dc.date.available2015-08-03T09:59:32Z
dc.date.issued2012-09
dc.identifier.issn01679317
dc.identifier.doi10.1016/j.mee.2012.02.032
dc.identifier.urihttp://hdl.handle.net/10754/562289
dc.description.abstractPhotolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.
dc.publisherElsevier BV
dc.subjectEllipsometry
dc.subjectLithography
dc.subjectNeural network
dc.subjectNondestructive testing
dc.titleAutomatic detection of photoresist residual layer in lithography using a neural classification approach
dc.typeArticle
dc.contributor.departmentKAUST Solar Center (KSC)
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.identifier.journalMicroelectronic Engineering
dc.contributor.institutionUniversité de Lyon, F-42023 Saint-Etienne, France
dc.contributor.institutionDIOM EA 3523, Télécom Saint-Etienne, Université de Saint-Etienne, Jean Monnet, F-42023, France
kaust.personGereige, Issam
kaust.personEid, Jessica


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