Volume 14, 2023
Special Issue on ‘WCPEC-8: State of the Art and Developments in Photovoltaics’, edited by Alessandra Scognamiglio, Robert Kenny, Shuzi Hayase and Arno Smets
|Number of page(s)||9|
|Section||Optics of Thin Films, TCOs|
|Published online||24 January 2023|
A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers
Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg im Breisgau, Germany
* e-mail: email@example.com
Received in final form: 17 November 2022
Accepted: 21 December 2022
Published online: 24 January 2023
Epitaxially-grown wafers on top of sintered porous silicon are a material-efficient wafer production process, that is now being launched into mass production. This production process makes the material-expensive sawing procedure obsolete since the wafer can be easily detached from its seed substrate. With high-throughput inline production processes, fast and reliable evaluation processes are crucial. The quality of the porous layers plays an important role regarding a successful detachment. Therefore, we present a fast and non-destructive investigation algorithm of thin, porous silicon layers. We predict the layer parameters directly from inline reflectance data by using a convolutional neural network (CNN), which is inspired by a comprehensive optical modelling approach from literature. There, a numerical fitting approach on reflection curves calculated with a physical model is performed. By adding the physical model to the CNN, we create a hybrid model, that not only predicts layer parameters, but also recalculates reflection curves. This allows a consistency check for a self-supervised network optimization. Evaluation on experimental data shows a high similarity with Scanning Electron Microscopy (SEM) measurements. Since parallel computation is possible with the CNN, 30.000 samples can be evaluated in roughly 100 ms.
Key words: Hybrid model / self-consistent / porous silicon / thin films / reflectometry
© A. Wörnhör et al., Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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