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Target factor and neural network analyses applied to titanium nitride composition recognition by AES
Author(s) -
Card Jill,
Testoni Anne L.,
Le Tarte Laurie A.
Publication year - 1995
Publication title -
surface and interface analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 90
eISSN - 1096-9918
pISSN - 0142-2421
DOI - 10.1002/sia.740230709
Subject(s) - tin , stoichiometry , titanium nitride , artificial neural network , auger electron spectroscopy , titanium , materials science , auger , analytical chemistry (journal) , nitride , multilayer perceptron , pattern recognition (psychology) , artificial intelligence , chemistry , computer science , nanotechnology , metallurgy , chromatography , physics , layer (electronics) , atomic physics , nuclear physics
This paper details the development of two pattern recognition approaches—modified target factor analysis (TFA) and artificial neural network analysis—applied to Auger electron spectra for precise discrimination of thin‐film TiN x compound composition. Use of Auger electron spectroscopy for the analysis of titanium nitride films offers advantages over other spectroscopic methods in its ability to analyze either blanket‐deposited films or submicron features on patterned wafers and its speed of sample preparation and spectral processing. However, severe overlap of the characteristic Ti LMM and N KLL transitions between 380 and 390 eV prohibit direct stoichiometry measurement. Modified TFA and multilayer perceptron (MLP) neural network approaches are applied to the task of discriminating TiN x stoichiometries to within 2% of a nominal 1:1 composition. The modified TFA procedure succeeded in accurate prediction of AES spectra TiN x stoichiometries in five of six sample groupings. The MLP neural network approach accurately predicted stoichiometries for all samples with reduced sample variance over standard analytical methods. This work combined AES and neural network technologies to improve significantly the precision of the stoichiometric composition recognition capability for TiN x compounds.