Speaker
Description
Solving inverse problems is the basis of the analysis of scattering experiments. The difficulty stems from the fact that the real-space structure has to be retrieved from reciprocal space information. With respect to thin films and interfaces, grazing incidence small- angle X-ray scattering (GISAXS) is a powerful tool for accessing their nanoscale structure formation. GISAXS allows for experiment in real time with high time resolution and high statistical relevance[1]. The two-dimensional scattering pattern is governed by the distorted-wave Born approximation (DWBA) - refraction and reflection effects have to be considered, adding further to complexity in data analysis. Hence, a model-based approach using simulations for the GISAXS pattern is necessary for elucidating the nanostructure[2,3].
Sputter deposition is an industrially-relevant method for fabricating metal-polymer nanocomposites[3]. The time resolution down to the sub-millisecond scale combined with in situ sputter deposition yields a large amount of data that requires careful analysis[4]. One way to extract quantitative information is to use a data base of model simulations of the sample. While fundamental assumptions about the system must be made in order to establish the simulations[5], the choice of appropriate inputs leads to a good approximation of the GISAXS data. A key issue is finding the simulation that best represents the system at each stage of the experiment.
Neural networks (NNs) are used to predict the behavior of a system through mathematical modeling. In our case, we use as preprocessing a background and intensity thresholding following Parente et al. [6] with the thresholding factor β being the only variable in the preprocessing stage. Additionally, we tested different network architectures using non-linear activations functions ReLU (R) and Leaky ReLU (L) in different compositions. We present the results of a multilayer perceptron and a convolutional NN (CNN) concerning the structure and morphology of the cluster growth of gold in silicon during sputter deposition. Especially the prediction of the percolation threshold is discussed.
[1] S. Liang, M. Schwartzkopf, S. V. Roth, P. Müller-Buschbaum, Nanoscale Adv. 2022, 4, 2533.
[2] Q. Chen, C. J. Brett, A. Chumakov, M. Gensch, M. Schwartzkopf, V. Körstgens, L. D. Söderberg, A. Plech, P. Zhang, P. Müller-Buschbaum, S. V Roth, ACS Appl. Nano Mater. 2021, 4, 503.
[3] S. V Roth, H. Walter, M. Burghammer, C. Riekel, B. Lengeler, C. Schroer, M. Kuhlmann, T. Walther, A. Sehrbrock, R. Domnick, P. Müller-Buschbaum, Appl. Phys. Lett. 2006, 88, 021910.
[4] M. Schwartzkopf, A. Hinz, O. Polonskyi, T. Strunskus, F. C. Löhrer, V. Körstgens, P. Müller-Buschbaum, F. Faupel, S. V. Roth, ACS Appl. Mater. Interfaces 2017, 9, 5629.
[5] M. Schwartzkopf, A. Buffet, V. Körstgens, E. Metwalli, K. Schlage, G. Benecke, J. Perlich, M. Rawolle, A. Rothkirch, B. Heidmann, G. Herzog, P. Müller-Buschbaum, R. Röhlsberger, R. Gehrke, N. Stribeck, S. V Roth, Nanoscale 2013, 5, 5053.
[6] M. Teixeira Parente, G. Brandl, C. Franz, U. Stuhr, M. Ganeva, A. Schneidewind, Nat. Commun. 2023, 14, 2246.
