Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22414
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dc.contributor.authorKnapp, Evelyne-
dc.contributor.authorBattaglia, Mattia-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorJenatsch, Sandra-
dc.contributor.authorRuhstaller, Beat-
dc.date.accessioned2021-05-05T12:15:24Z-
dc.date.available2021-05-05T12:15:24Z-
dc.date.issued2021-06-09-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22414-
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractThe optimization of organic semiconductor devices relies on the determination of material and device parameters. However, these parameters are often not directly measurable or accessible and may change depending on the neighboring materials in the layered stack. Once the parameters are known, devices can be optimized in order to maximize a certain target, e.g. the brightness of a LED. Here, we combine the use of machine learning and a semiconductor device modelling tool to extract the material parameters from measurements. Therefore, we train our machine learning model with synthetic training data originating from a semiconductor simulator. In a second step, the machine learning model is applied to a measured data set and determines the underlying material parameters. This novel and reliable method for the determination of material parameters paves the way to further device performance optimization.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectXGBoostde_CH
dc.subjectSynthetic datade_CH
dc.subjectOrganic semiconductorde_CH
dc.subjectParameter extractionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleXGBoost trained on synthetic data to extract material parameters of organic semiconductorsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitute of Computational Physics (ICP)de_CH
dc.identifier.doi10.21256/zhaw-22414-
zhaw.conference.details8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 8th SDSde_CH
zhaw.webfeedComputer Vision, Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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