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

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-05
Main Authors: Renato da S Guimarães, Silva, Tiago F, Rodrigues, Cleber L, Tabacniks, Manfredo H, Bach, Simon, Burwitz, Vassily V, Hiret, Paul, Mayer, Matej
Format: Article
Language:English
Subjects:
Artificial neural networks
Backscattering
Data processing
Ion beams
Neural networks
Scientific imaging
Spectrometry
Spectroscopy
Stellarators
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2205.05774