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2019-11-08Zeitschriftenartikel DOI: 10.1107/S1600576719013311
Fast fitting of reflectivity data of growing thin films using neural networks
dc.contributor.authorGreco, Alessandro
dc.contributor.authorStarostin, Vladimir
dc.contributor.authorKarapanagiotis, Christos
dc.contributor.authorHinderhofer, Alexander
dc.contributor.authorGerlach, Alexander L.
dc.contributor.authorPithan, Linus
dc.contributor.authorLiehr, Sascha
dc.contributor.authorSchreiber, Frank
dc.contributor.authorKowarik, Stefan
dc.date.accessioned2022-07-07T12:02:06Z
dc.date.available2022-07-07T12:02:06Z
dc.date.issued2019-11-08none
dc.date.updated2020-10-12T13:05:28Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/25659
dc.description.abstractX-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and α-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectX‐ray reflectivityeng
dc.subjectmachine learningeng
dc.subjectorganic semi‐conductorseng
dc.subjectneural networkseng
dc.subject.ddc540 Chemie und zugeordnete Wissenschaftennone
dc.titleFast fitting of reflectivity data of growing thin films using neural networksnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/25659-5
dc.identifier.doi10.1107/S1600576719013311none
dc.identifier.doihttp://dx.doi.org/10.18452/24972
dc.type.versionpublishedVersionnone
local.edoc.container-titleJournal of applied crystallographynone
local.edoc.pages6none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameInternational Union of Crystallographynone
local.edoc.container-publisher-place5 Abbey Square, Chester, Cheshire CH1 2HU, Englandnone
local.edoc.container-volume52none
local.edoc.container-issue6none
local.edoc.container-firstpage1342none
local.edoc.container-lastpage1347none
dc.description.versionPeer Reviewednone
dc.identifier.eissn1600-5767

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