Publication Date:
2021-07-03
Description:
X‐ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and α‐sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.
Description:
Artificial neural networks trained with simulated data are shown to correctly and quickly determine film parameters from experimental X‐ray reflectivity curves.
Keywords:
548
;
X‐ray reflectivity
;
machine learning
;
organic semi‐conductors
;
neural networks
Type:
article
