Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen: https://doi.org/10.21256/zhaw-22414
Publikationstyp: Konferenz: Paper
Art der Begutachtung: Peer review (Publikation)
Titel: XGBoost trained on synthetic data to extract material parameters of organic semiconductors
Autor/-in: Knapp, Evelyne
Battaglia, Mattia
Stadelmann, Thilo
Jenatsch, Sandra
Ruhstaller, Beat
et. al: No
DOI: 10.21256/zhaw-22414
Tagungsband: Proceedings of the 8th SDS
Angaben zur Konferenz: 8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021
Erscheinungsdatum: 9-Jun-2021
Verlag / Hrsg. Institution: IEEE
Sprache: Englisch
Schlagwörter: XGBoost; Synthetic data; Organic semiconductor; Parameter extraction
Fachgebiet (DDC): 006: Spezielle Computerverfahren
Zusammenfassung: The 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.
Weitere Angaben: © 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/22414
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: School of Engineering
Organisationseinheit: Centre for Artificial Intelligence (CAI)
Institute of Computational Physics (ICP)
Enthalten in den Sammlungen:Publikationen School of Engineering

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
2021_Knapp-etal_XGBoost-trained-on-synthetic-data_SDS.pdfAccepted Version1.37 MBAdobe PDFMiniaturbild
Öffnen/Anzeigen


Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.