Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22414
Publication type: Conference paper
Type of review: Peer review (publication)
Title: XGBoost trained on synthetic data to extract material parameters of organic semiconductors
Authors: Knapp, Evelyne
Battaglia, Mattia
Stadelmann, Thilo
Jenatsch, Sandra
Ruhstaller, Beat
et. al: No
DOI: 10.1109/SDS51136.2021.00015
10.21256/zhaw-22414
Proceedings: Proceedings of the 8th SDS
Pages: 46
Pages to: 51
Conference details: 8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021
Issue Date: 9-Jun-2021
Publisher / Ed. Institution: IEEE
ISBN: 978-1-6654-3874-2
Language: English
Subjects: XGBoost; Synthetic data; Organic semiconductor; Parameter extraction
Subject (DDC): 006: Special computer methods
Abstract: 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.
Further 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/22414
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Centre for Artificial Intelligence (CAI)
Institute of Computational Physics (ICP)
Appears in collections:Publikationen School of Engineering

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