Authors: | Dogan-Surmeier, Susanne Gruber, Florian Bieder, Steffen Schlenz, Patrick Paulus, Michael Albers, Christian Schneider, Eric Thiering, Nicola Maurer, Christian Tolan, Metin Wollmann, Philipp Cornelius, Steffen Sternemann, Christian |
Title: | Towards in-line real-time characterization of roll-to-roll produced ZTO/Ag/ITO thin films by hyperspectral imaging |
Language (ISO): | en |
Abstract: | Large area manufacturing processes of thin films such as large-area vacuum roll-to-roll coating of dielectric and gas permeation barrier layers in industry require a precise control of e.g. film thickness, homogeneity, chemical compositions, crystallinity and surface roughness. In order to determine these properties in real time, hyperspectral imaging is a novel, cost-efficient, and fast tool as in-line technology for large-area quality control. We demonstrate the application of hyperspectral imaging to characterize the thickness of thin films of the multilayer system ZTO/Ag/ITO produced by roll-to-roll magnetron sputtering on 220 mm wide polyethylene terephthalate substrate. X-ray reflectivity measurements are used to determine the thickness gradients of roll-to-roll produced foils with sub nanometer accuracy that serve as ground truth data to train a machine learning model for the interpretation of the hyperspectral imaging spectra. Based on the model, the sub-layer thicknesses on the complete substrate foil area were predicted which demonstrates the capabilities of this approach for large-scale in-line real-time quality control for industrial applications. |
Subject Headings: | hyperspectral imaging x-ray reflectivity machine learning thickness prediction thin films |
URI: | http://hdl.handle.net/2003/42606 http://dx.doi.org/10.17877/DE290R-24441 |
Issue Date: | 2023-06-08 |
Rights link: | https://creativecommons.org/licenses/by/4.0/ |
Appears in Collections: | Experimentelle Physik I |
Files in This Item:
File | Description | Size | Format | |
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Dogan-Surmeier_2023_J._Phys._D__Appl._Phys._56_365102.pdf | DNB | 20.33 MB | Adobe PDF | View/Open |
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