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Artificial neural network algorithm for analysis of rutherford backscattering data

Barradas1, Vieira

  • 1Instituto Tecnologico e Nuclear, Reactor, Estrada Nacional 10, 2686-953 Sacavem, Portugal and Centro de Fisica Nuclear da Universidade de Lisboa, Avenida Prof. Gama Pinto 2, 1699 Lisboa Codex, Portugal.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|November 23, 2000
PubMed
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An artificial neural network (ANN) was developed to interpret Rutherford backscattering (RBS) data for thin film analysis. This AI approach provides instantaneous results for compositional depth profiling, improving upon traditional methods.

Area of Science:

  • Materials Science
  • Physics
  • Data Science

Background:

  • Rutherford backscattering (RBS) is a quantitative technique for thin film analysis.
  • The inverse RBS problem, determining sample structure from data, is ill-posed.
  • Skilled analysts rely on experience to interpret complex RBS spectra.

Purpose of the Study:

  • To develop an artificial neural network (ANN) for interpreting RBS data.
  • To apply the ANN to determine the compositional depth profile of Ge-implanted Si.
  • To compare ANN performance with traditional analysis methods.

Main Methods:

  • Training an ANN with thousands of simulated RBS spectra.
  • Applying the trained ANN to experimental RBS data from unknown samples.
  • Quantitative comparison of ANN results with traditional analysis.

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Main Results:

  • The ANN achieved excellent quantitative results, comparable to traditional methods.
  • ANN analysis is instantaneous after a time-consuming training phase.
  • The ANN successfully distinguished between two experimentally challenging data classes.

Conclusions:

  • ANNs offer a powerful, rapid alternative for RBS data analysis.
  • The developed ANN enables automated on-line data analysis.
  • ANNs can facilitate automated on-line optimization of experimental conditions.