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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Voue, Michel
University of Mons
in Cooperation with on an Cooperation-Score of 37%
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article
Rapid ellipsometric imaging characterization of nanocomposite films with an artificial neural network
Abstract
<jats:p>Imaging ellipsometry is an optical characterization tool that is widely used to investigate the spatial variations of the opto-geometrical properties of thin films. As ellipsometry is an indirect method, an ellipsometric map analysis requires a modeling step. Classical methods such as the Levenberg–Marquardt algorithm (LM) are generally too time consuming to be applied on a large data set. In this way, an artificial neural network (ANN) approach was introduced for the analysis of an ellipsometric map. As a proof of concept this method was applied for the characterization of silver nanoparticles embedded in a poly-(vinyl alcohol) film. We demonstrate that the LM and ANN give similar results. However, the time required for the ellipsometric map analysis decreases from 15 days for the LM to 1 s for the ANN. This suggests that the ANN is a powerful tool for fast spectroscopic-ellipsometric-imaging analysis.</jats:p>