AVS 49th International Symposium
    Applied Surface Science Tuesday Sessions
       Session AS-TuP

Paper AS-TuP15
Mathematical Topographical Correction of XPS Images using Multivariate Statistical Methods

Tuesday, November 5, 2002, 5:30 pm, Room Exhibit Hall B2

Session: Topics in Applied Surface Science
Presenter: K. Artyushkova, Kent State University
Authors: K. Artyushkova, Kent State University
S. Pylypenko, Kent State University
J.E. Fulghum, Kent State University
Correspondent: Click to Email
For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background subtraction is frequently utilized to minimize topographic effects so that images represent concentration variations in the sample. For this purpose, background images are recorded at slightly lower and/or higher binding energies than the main peak for all species of interest. Background-corrected images result from subtracting a background energy map from one acquired at the peak energy. This procedure may significantly increase the data acquisition time. Multivariate statistical methods can assist in resolving topographical and chemical information from images. Principal Component Analysis (PCA) is one method for identification of the highest correlation/variation between the images. Topography which is common to all of the images will be resolved in the 1st most significant component. The score of this component contains spatial information about the topography of the surface, while the loading is a quantitative representation of the topography contribution to each elemental/chemical image. Reconstructing the data using the score and loading for the 1st component will provide mathematical background images. These images, which contain the topographical information for all elemental/chemical images, can be used to correct the images for topography in the same way the experimental background images are used, thereby reducing the time required for data acquisition. The mathematical background correction scheme is developed and validated by comparing results to the experimental background correction for three samples with differing degrees of topography. The first example is a very rough, fossilized sample, the second is a patterned sample with roughness on the order of the XPS sampling depth and the third is a flat polymer blend sample. This work has been partially supported by NSF CHE-0113724.