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dc.contributor.authorSimsek, Ergun
dc.date.accessioned2020-03-25T17:30:54Z
dc.date.available2020-03-25T17:30:54Z
dc.date.issued2020-02-25
dc.description.abstractSynthetically generated multi-angle reflectometry data is used to train a neural network based learning system to estimate the refractive index of atomically thin layered materials in the visible part of the electromagnetic spectrum. Unlike previously developed regression based optical characterization methods, the prediction is achieved via classification by using the probabilities of each input element belonging to a label as weighting coefficients in a simple analytical formula. Various types of activation functions and gradient descent optimizers are tested to determine the optimum combination yielding the best performance. For the verification of the proposed method's accuracy, four different materials are studied. In all cases, the maximum error is calculated to be less than 0.3%. Considering the highly dispersive nature of the studied materials, this result is a substantial improvement in terms of accuracy and efficiency compared to traditional approaches.en_US
dc.description.urihttps://iopscience.iop.org/article/10.1088/2632-2153/ab6d5fen_US
dc.format.extent8 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2cx1r-uqiv
dc.identifier.citationSimsek, Ergun; Determining optical constants of 2D materials with neural networks from multi-angle reflectometry data; Machine Learning: Science and Technology 1,1 (2020); https://iopscience.iop.org/article/10.1088/2632-2153/ab6d5fen_US
dc.identifier.urihttps://doi.org/10.1088/2632-2153/ab6d5f
dc.identifier.urihttp://hdl.handle.net/11603/17642
dc.language.isoen_USen_US
dc.publisherIOP Publishingen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 3.0 Unported (CC BY 3.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/*
dc.titleDetermining optical constants of 2D materials with neural networks from multi-angle reflectometry dataen_US
dc.typeTexten_US


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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Except where otherwise noted, this item's license is described as This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.