Processing of massive Rutherford Back-scattering Spectrometry data by artificial neural networks

TL;DR

This paper demonstrates that artificial neural networks significantly improve the efficiency and accuracy of processing large Rutherford Backscattering Spectrometry datasets, outperforming human evaluation and automatic fitting routines.

Contribution

It introduces an ANN-based method for processing massive RBS data, showing superior performance over traditional manual and automatic approaches.

Findings

ANN outperforms human evaluation in accuracy.

ANN is more efficient than automatic fit routines.

Validated on 500 spectra from stellarator W7-X.

Abstract

Rutherford Backscattering Spectrometry (RBS) is an important technique providing elemental information of the near surface region of samples with high accuracy and robustness. However, this technique lacks throughput by the limited rate of data processing and is hardly routinely applied in research with a massive number of samples (i.e. hundreds or even thousands of samples). The situation is even worse for complex samples. If roughness or porosity is present in those samples the simulation of such structures is computationally demanding. Fortunately, Artificial Neural Networks (ANN) show to be a great ally for massive data processing of ion beam data. In this paper, we report the performance comparison of ANN against human evaluation and an automatic fit routine running on batch mode. 500 spectra of marker layers from the stellarator W7-X were used as study case. The results showed ANN as more accurate than humans and more efficient than automatic fits.