Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-22414Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Knapp, Evelyne | - |
| dc.contributor.author | Battaglia, Mattia | - |
| dc.contributor.author | Stadelmann, Thilo | - |
| dc.contributor.author | Jenatsch, Sandra | - |
| dc.contributor.author | Ruhstaller, Beat | - |
| dc.date.accessioned | 2021-05-05T12:15:24Z | - |
| dc.date.available | 2021-05-05T12:15:24Z | - |
| dc.date.issued | 2021-06-09 | - |
| dc.identifier.isbn | 978-1-6654-3874-2 | de_CH |
| dc.identifier.uri | https://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.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | IEEE | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | XGBoost | de_CH |
| dc.subject | Synthetic data | de_CH |
| dc.subject | Organic semiconductor | de_CH |
| dc.subject | Parameter extraction | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | XGBoost trained on synthetic data to extract material parameters of organic semiconductors | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Centre for Artificial Intelligence (CAI) | de_CH |
| zhaw.organisationalunit | Institute of Computational Physics (ICP) | de_CH |
| dc.identifier.doi | 10.1109/SDS51136.2021.00015 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-22414 | - |
| zhaw.conference.details | 8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 51 | de_CH |
| zhaw.pages.start | 46 | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.title.proceedings | Proceedings of the 8th SDS | de_CH |
| zhaw.webfeed | Machine Perception and Cognition | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.webfeed | ZHAW digital | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Knapp-etal_XGBoost-trained-on-synthetic-data_SDS.pdf | Accepted Version | 1.37 MB | Adobe PDF |  View/Open |
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Knapp, E., Battaglia, M., Stadelmann, T., Jenatsch, S., & Ruhstaller, B. (2021). XGBoost trained on synthetic data to extract material parameters of organic semiconductors [Conference paper]. Proceedings of the 8th SDS, 46–51. https://doi.org/10.1109/SDS51136.2021.00015
Knapp, E. et al. (2021) ‘XGBoost trained on synthetic data to extract material parameters of organic semiconductors’, in Proceedings of the 8th SDS. IEEE, pp. 46–51. Available at: https://doi.org/10.1109/SDS51136.2021.00015.
E. Knapp, M. Battaglia, T. Stadelmann, S. Jenatsch, and B. Ruhstaller, “XGBoost trained on synthetic data to extract material parameters of organic semiconductors,” in Proceedings of the 8th SDS, Jun. 2021, pp. 46–51. doi: 10.1109/SDS51136.2021.00015.
KNAPP, Evelyne, Mattia BATTAGLIA, Thilo STADELMANN, Sandra JENATSCH und Beat RUHSTALLER, 2021. XGBoost trained on synthetic data to extract material parameters of organic semiconductors. In: Proceedings of the 8th SDS. Conference paper. IEEE. 9 Juni 2021. S. 46–51. ISBN 978-1-6654-3874-2
Knapp, Evelyne, Mattia Battaglia, Thilo Stadelmann, Sandra Jenatsch, and Beat Ruhstaller. 2021. “XGBoost Trained on Synthetic Data to Extract Material Parameters of Organic Semiconductors.” Conference paper. In Proceedings of the 8th SDS, 46–51. IEEE. https://doi.org/10.1109/SDS51136.2021.00015.
Knapp, Evelyne, et al. “XGBoost Trained on Synthetic Data to Extract Material Parameters of Organic Semiconductors.” Proceedings of the 8th SDS, IEEE, 2021, pp. 46–51, https://doi.org/10.1109/SDS51136.2021.00015.
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