Automatic detection of photoresist residual layer in lithography using a neural classification approach

Handle URI:
http://hdl.handle.net/10754/562289
Title:
Automatic detection of photoresist residual layer in lithography using a neural classification approach
Authors:
Gereige, Issam; Robert, Stéphane; Eid, Jessica
Abstract:
Photolithography 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.
KAUST Department:
Solar and Photovoltaic Engineering Research Center (SPERC)
Publisher:
Elsevier BV
Journal:
Microelectronic Engineering
Issue Date:
Sep-2012
DOI:
10.1016/j.mee.2012.02.032
Type:
Article
ISSN:
01679317
Appears in Collections:
Articles; Solar and Photovoltaic Engineering Research Center (SPERC)

Full metadata record

DC FieldValue Language
dc.contributor.authorGereige, Issamen
dc.contributor.authorRobert, Stéphaneen
dc.contributor.authorEid, Jessicaen
dc.date.accessioned2015-08-03T09:59:32Zen
dc.date.available2015-08-03T09:59:32Zen
dc.date.issued2012-09en
dc.identifier.issn01679317en
dc.identifier.doi10.1016/j.mee.2012.02.032en
dc.identifier.urihttp://hdl.handle.net/10754/562289en
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.en
dc.publisherElsevier BVen
dc.subjectEllipsometryen
dc.subjectLithographyen
dc.subjectNeural networken
dc.subjectNondestructive testingen
dc.titleAutomatic detection of photoresist residual layer in lithography using a neural classification approachen
dc.typeArticleen
dc.contributor.departmentSolar and Photovoltaic Engineering Research Center (SPERC)en
dc.identifier.journalMicroelectronic Engineeringen
dc.contributor.institutionUniversité de Lyon, F-42023 Saint-Etienne, Franceen
dc.contributor.institutionDIOM EA 3523, Télécom Saint-Etienne, Université de Saint-Etienne, Jean Monnet, F-42023, Franceen
kaust.authorGereige, Issamen
kaust.authorEid, Jessicaen
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