Ion beam analysis and big data: How data science can support next-generation instrumentation

Abstract With a growing demand for accurate ion beam analysis on a large number of samples, it becomes an issue of how to ensure the quality standards and consistency over hundreds or thousands of samples. In this sense, a virtual assistant that checks the data quality, emitting certificates of quality, is highly desired. Even the processing of a massive number of spectra is a problem regarding the consistency of the analysis. In this work, we report the design and first results of a virtual layer under implementation in our laboratory. It consists of a series of systems running in the cloud that perform the mentioned tasks and serves as a virtual assistant for member staff and users. We aim to bring the concept of the Internet of Things and artificial intelligence closer to the laboratory to support a new generation of instrumentation.

[1]  C. Detavernier,et al.  On the growth kinetics of Ni(Pt) silicide thin films , 2013 .

[2]  Vieira,et al.  Artificial neural network algorithm for analysis of rutherford backscattering data , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  T. F. Silva,et al.  Multivariate analysis applied to particle-induced X-ray emission mapping , 2018 .

[4]  C. P. Dhard,et al.  Material erosion and deposition on the divertor of W7-X , 2020 .

[5]  T. F. Silva,et al.  MultiSIMNRA: A computational tool for self-consistent ion beam analysis using SIMNRA , 2016, 1803.01946.

[6]  N. P. Barradas,et al.  Error performance analysis of artificial neural networks applied to Rutherford backscattering , 2001 .

[7]  K. Temst,et al.  Artificial neural networks for instantaneous analysis of real-time Rutherford backscattering spectra , 2010 .

[8]  F. Watt,et al.  The application of micro-PIXE simulation code in the quantitative analysis of environmental samples , 1999 .

[9]  C. Pascual-Izarra,et al.  LibCPIXE: A PIXE simulation open-source library for multilayered samples , 2006, 0707.2438.

[10]  C. Jeynes,et al.  Ion Beam Analysis: A Century of Exploiting the Electronic and Nuclear Structure of the Atom for Materials Characterisation , 2011 .

[11]  C. Ryan,et al.  PIXE-quantified AXSIA: Elemental mapping by multivariate spectral analysis , 2006 .

[12]  M. Mayer Improved physics in SIMNRA 7 , 2014 .

[13]  Gustavo F. Trindade,et al.  Elemental mapping of large samples by external ion beam analysis with sub-millimeter resolution and its applications , 2018 .