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dc.contributor.authorPodstránský, J.
dc.contributor.authorKnápek, A.
dc.date.accessioned2023-01-02T10:25:52Z
dc.date.available2023-01-02T10:25:52Z
dc.date.issued2022
dc.identifier.citationElectroscope. 2022, č. 1.cs
dc.identifier.issn1802-4566
dc.identifier.urihttp://hdl.handle.net/11025/50795
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plzni, Fakulta elektrotechnickács
dc.rights© Electroscope. All rights reserved.en
dc.subjectvady odolnostics
dc.subjectumělá inteligencecs
dc.subjectzpracování obrazucs
dc.subjectautomatizacecs
dc.titleAutomated defectoscopy of thin poly (methyl methacrylate) layersen
dc.typearticleen
dc.typečlánekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn the electron beam lithography process, one of the initial steps is to coat the substrate (i.e., the silicon wafer) with a thin layer of polymer resist. During the coating process, defects in the thin layer can occur, which can affect the exposure and therefore the functionality of the final nanostructure. By checking the quality of the deposited polymer layer prior to exposure, these defect sites can be avoided. This process can be done manually using a visible-light microscope, but it is a time-consuming process and subject to a possible human error. In the framework of this project, a fully automated device has been developed that can detect and identifies these using computer vision. It is a scanning device that, by combining three stepper motors and an optical camera, takes images of the desired area of the wafer and then analyses these with the help of artificial intelligence. The user is then provided with a document in which the size, position and type of each defect found is recorded.en
dc.subject.translateddefects in resisten
dc.subject.translatedartificial intelligenceen
dc.subject.translatedimage processingen
dc.subject.translatedautomatizationen
dc.type.statusPeer-revieweden
Appears in Collections:Číslo 1 (2022)
Číslo 1 (2022)

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