# Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks

arXiv: https://arxiv.org/abs/1910.02898 · HTML: https://mlsanity.com/paper/1910.02898/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data](https://mlsanity.com/paper/2202.10983/) (2022)
- [Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem](https://mlsanity.com/paper/2307.05364/) (2023)
- [Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates](https://mlsanity.com/paper/2107.12156/) (2021)

## Abstract (arXiv)

X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. In this study, we show how a simple artificial neural network model can be used to predict 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 dataset of 372 XRR curves, we show that a simple fully connected model can already provide good predictions with a mean absolute percentage error of 8-18 % when compared to the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.