Outlier detection in lithography: benchmarking and the creation of a new algorithm

Detalhes bibliográficos
Autor(a) principal: Brown, Monika Rose
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/100950
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Outlier detection in lithography: benchmarking and the creation of a new algorithmOutlier detectionAnomaly detectionLithographyEnsemble algorithmInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsOverlay is the measure of a lithography machine’s success at printing layers accurately atop one another. Poor overlay in semiconductor manufacturing can cause problems in a semiconductor chip’s electrical current and, in the worst case, can render the chips unusable. A new algorithm is proposed for detecting outliers in lithography calibration wafers using an ensemble of well-known statistical methods. Detecting outliers on these calibration wafers and removing them from the data fed to the lithography machine contributes to improving overlay accuracy for semiconductor manufacturers as these wafers are used to set proper alignment on a lithography machine. Through simulation, the new ensemble algorithm along with other outlier algorithms are benchmarked against a lithography firm’s current outlier detection algorithm. The new ensemble algorithm shows outperformance on every criterion tested.Bação, Fernando José Ferreira LucasNazarian, AlexeiRUNBrown, Monika Rose2023-06-02T00:30:38Z2020-06-022020-06-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/100950TID:202501124enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:47:17Zoai:run.unl.pt:10362/100950Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:27.959189Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Outlier detection in lithography: benchmarking and the creation of a new algorithm
title Outlier detection in lithography: benchmarking and the creation of a new algorithm
spellingShingle Outlier detection in lithography: benchmarking and the creation of a new algorithm
Brown, Monika Rose
Outlier detection
Anomaly detection
Lithography
Ensemble algorithm
title_short Outlier detection in lithography: benchmarking and the creation of a new algorithm
title_full Outlier detection in lithography: benchmarking and the creation of a new algorithm
title_fullStr Outlier detection in lithography: benchmarking and the creation of a new algorithm
title_full_unstemmed Outlier detection in lithography: benchmarking and the creation of a new algorithm
title_sort Outlier detection in lithography: benchmarking and the creation of a new algorithm
author Brown, Monika Rose
author_facet Brown, Monika Rose
author_role author
dc.contributor.none.fl_str_mv Bação, Fernando José Ferreira Lucas
Nazarian, Alexei
RUN
dc.contributor.author.fl_str_mv Brown, Monika Rose
dc.subject.por.fl_str_mv Outlier detection
Anomaly detection
Lithography
Ensemble algorithm
topic Outlier detection
Anomaly detection
Lithography
Ensemble algorithm
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2020
dc.date.none.fl_str_mv 2020-06-02
2020-06-02T00:00:00Z
2023-06-02T00:30:38Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/100950
TID:202501124
url http://hdl.handle.net/10362/100950
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dc.language.iso.fl_str_mv eng
language eng
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