Examples for the improvements in AES Depth Profiling of Multilayer Thin Film Systems by Application of Factor Analysis Data Evaluation

TL;DR

This paper demonstrates that Factor Analysis significantly enhances AES depth profiling of multilayer thin films by revealing detailed chemical and interfacial information beyond conventional methods.

Contribution

It introduces a user-friendly application of Factor Analysis for AES data, improving the extraction of chemical components and interfacial phases in thin film systems.

Findings

Factor Analysis uncovers stoichiometry information in AES profiles.

It reveals interfacial layer phases not accessible by traditional peak height analysis.

Application to two thin film systems validates the method's effectiveness.

Abstract

Factor Analysis has proved to be a powerful tool for the full exploitation of the chemical information included in the peak shapes and peak positions of spectra measured by AES depth profiling. Due to its ability to extract the number of independent chemical components, their spectra and their depth distributions, its information content exceeds the one of the usual peak-to-peak height evaluation of AES depth profile data. Using modern software with a graphically interactive user interface the analyst is put into a position, where he can work with Factor Analysis on a physically intuitive level despite of all the matrix algebra mathematics which it is based upon. The progress brought about by Factor Analysis to AES depth profiles of thin films is demonstrated by the analysis of two thin film systems. The first one is a Pt/Ti metallisation used as bottom electrode for ferroelectric thin films, the second one is multilayer system where a Ti silicide formation of burried Ti/Si bilayers has been induced. Both examples show that Factor Analysis evaluation of AES depth profile data is capable to give access to stoichiometry information and to reveal interfacial layer phases, information which is hardly obtained from the conventional peak-to-peak height data evaluation.