Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-11T01:18:42.047Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of Factor Analysis and Artificial Neural Network in Electron Spectroscopic Depth Profiling

Published online by Cambridge University Press:  30 July 2003

Henning Bubert  and
Tamara Niebuhr
Henning Bubert
Affiliation:
Institut für Spektrochemie und Angewandte Spektroskopie (ISAS), Bunsen-Kirchhoff-Str. 11, D-44139 Dortmund, Germany
Tamara Niebuhr
Affiliation:
Institut für Spektrochemie und Angewandte Spektroskopie (ISAS), Bunsen-Kirchhoff-Str. 11, D-44139 Dortmund, Germany
Get access

Abstract

Depth profiling has been performed by using Auger electron spectrometry and X-ray photoelectron spectrometry in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and partly by applying artificial neural network in order to determine the compositional layering of different thin-films such as TiNx on Ti, Cr2O3/CrN sandwich layer, and copper oxide on Cu. Both multivariate statistical methods applied to the same data sets lead to results that agree well within statistical deviations provided that the structure of the artificial neural network is constructed appropriate to the actual problem.

Keywords

Factor analysisartificial neural networkelectron spectroscopydepth profilingtitanium nitridechromium oxide/nitridecopper oxide
Type
Research Article
Information
Microscopy and Microanalysis , Volume 2 , Issue 1 , February 1996 , pp. 35 - 41
Copyright
1996 Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)