AVS 50th International Symposium
    QSA-10 Topical Conference Monday Sessions
       Session QS-MoM

Paper QS-MoM11
Multivariate Analysis for XPS Spectral Imaging@footnote 1@

Monday, November 3, 2003, 11:40 am, Room 320

Session: Advances in Quantitative Surface Analysis
Presenter: D.E. Peebles, Sandia National Laboratories
Authors: D.E. Peebles, Sandia National Laboratories
J.A. Ohlhausen, Sandia National Laboratories
P.G. Kotula, Sandia National Laboratories
Correspondent: Click to Email
The acquisition of complete spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique generates full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data provide a way to sort through this large block of data to determine the chemical component species present as well as their distribution and concentrations, with minimal data acquisition and processing times. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise and improved spatial resolution, which are achieved due to the large number of data points included in the image. In this paper, we will illustrate the signal to noise and spatial resolution obtained from a Magnification Reference Standard at a series of spectral image acquisition times, with a direct comparison of the raw images to the multivariate processed data. We will demonstrate the elemental separation and chemical discrimination possible with Sandia's novel multivariate statistical analysis approach for both limited spectral region acquisition as well as more complete spectral imaging data sets. It will be shown that Sandia's techniques provide efficient methods for deriving physically realistic chemical components without user input other than the spectral data matrix itself. @FootnoteText@ @footnote 1@ This work was completed at Sandia National Laboratories, a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.