Autor(en): 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
Titel: Towards in-line real-time characterization of roll-to-roll produced ZTO/Ag/ITO thin films by hyperspectral imaging
Sprache (ISO): en
Zusammenfassung: 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.
Schlagwörter: 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
Erscheinungsdatum: 2023-06-08
Rechte (Link): https://creativecommons.org/licenses/by/4.0/
Enthalten in den Sammlungen:Experimentelle Physik I

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