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ML-reflectivity

DOI

Fast fitting of reflectivity curves using machine learning.

This code was used in the following scientific publication:

[1] Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, & S. Kowarik (2019). J. Appl. Cryst., in print.

A co-developed version of this software with a graphical user interface can be found at kowarik-labs/AI-reflectivity.

Disclaimer

This repository mainly serves as a public archive for the code used in [1] and is not yet optimized for the use by other researchers. Future updates will improve the functionality and usability of the program.

Main dependencies

To be able to run the code, a python 3.7 installation with the following dependencies is required:

  • keras
  • tensorflow
  • numpy
  • tqdm
  • csv
  • datetime
  • configobj
  • matplotlib

Usage

Provided a correct python installation is available, the code can be run from a suitable shell as-is using the configuration and neural network archtitecture used in [1]. The trained network can be tested on the provided real-time X-ray reflectivity dataset "DIP_330K.txt" of a growing diindenoperylene film on a silicon wafer with a native oxide layer (published in [1]).

Generate the reflectivity curves for training

$ python generate_training_data.py

Define neural network and execute training

$ python training.py

Predict parameters of the test file "DIP_330K.txt" and plot fitted curves

$ python prediction.py

Authors

  • Alessandro Greco (Institut für Angewandte Physik, University of Tübingen)
  • Vladimir Starostin (Institut für Angewandte Physik, University of Tübingen)
  • Christos Karapanagiotis (Institut für Physik, Humboldt Universität zu Berlin)
  • Alexander Hinderhofer (Institut für Angewandte Physik, University of Tübingen)
  • Alexander Gerlach (Institut für Angewandte Physik, University of Tübingen)
  • Linus Pithan (ESRF The European Synchrotron)
  • Sascha Liehr (Bundesanstalt für Materialforschung und -prüfung (BAM))
  • Frank Schreiber (Institut für Angewandte Physik, University of Tübingen)
  • Stefan Kowarik (Bundesanstalt für Materialforschung und -prüfung (BAM) and Institut für Physik, Humboldt Universität zu Berlin)