Dogan-Surmeier, SusanneGruber, FlorianBieder, SteffenSchlenz, PatrickPaulus, MichaelAlbers, ChristianSchneider, EricThiering, NicolaMaurer, ChristianTolan, MetinWollmann, PhilippCornelius, SteffenSternemann, Christian2024-07-192024-07-192023-06-08http://hdl.handle.net/2003/4260610.17877/DE290R-24441Large 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.enJournal of physics / D, Applied physics;56(36)https://creativecommons.org/licenses/by/4.0/hyperspectral imagingx-ray reflectivitymachine learningthickness predictionthin films530Article