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

Paper AS-TuA3
Topographic Effects in SIMS Imaging

Tuesday, November 5, 2002, 2:40 pm, Room C-106

Session: Imaging in Surface Analysis
Presenter: S. Rangarajan, University of Utah
Authors: S. Rangarajan, University of Utah
B.J. Tyler, University of Utah
Correspondent: Click to Email
One of the most notable of these challenges in TOF-SIMS imaging is differentiating between chemical and topographical effects. The intensity of ion signals depends not only on the surface composition but also upon the surface height and inclination (topography) and the material beneath the surface (matrix). In many cases, the intensity variations due to the structure of the sample can obscure features associated with surface chemistry. Images of surfaces with strong topographic features, including fibers and spherical particles have been presented. Topographic effects include the influences of the height of topographic features, the incident angle between the beam and the surface and variations in the electric field associated with topographic features. We have explored the influence of these topographic features on the absolute and relative ion intensities on conducting and insulating surfaces. Data from both TRIFT and reflectron systems will be presented. When images are generated by rastering the ion beam, topography can cause severe distortions in the image. Additional, particles can create field lines that result in repressed ion emission causing a halo surrounding the particles. Typically, researchers have assumed that topography effects only the absolute intensity of ions but will not significantly alter the relative intensity of peaks in the spectrum. Our results suggest that this assumption is incorrect in many cases. Several data processing methods have been used to compensate for topographic effects in images and there effectiveness will be discussed. Multivariate statistics can help reduce some but not all of these effects on the images. Results will be presented using principle components analysis and mixture models to process images with confounding chemical and topographical features.