Adaptive Scanning Probe Microcopies

This work is comprised of two major sections. In the first section we develop multivariate image classification techniques to distinguish and identify surface electronic species directly from multiple-bias scanning tunneling microscope (STM) images. Multiple measurements at each site are used to distinguish and categorize inequivalent electronic or atomic species on the surface via a computerized classification algorithm. Then, comparison with theory or other suitably chosen experimental data enables the identification of each class. We demonstrate the technique by analyzing dual-polarity constant-current topographs of the Ge(111) surface. Just two measurements, negativeand positive-bias topography height, permit pixels to be separated into seven different classes. Labeling four of the classes as adatoms, first-layer atoms, and two inequivalent rest-atom sites, we find excellent agreement with the c(2×8) structure. The remaining classes are associated with structural defects and contaminants. This work represents a first step toward developing a general electronic/chemical classification and identification tool for multivariate scanning probe microscopy imagery. In the second section we report measurements of the diffusion of Si dimers on the Si(001) surface at temperatures between room temperature and 128 C using a novel atom-tracking technique that can resolve every diffusion event. The atom tracker employs lateral-positioning feedback to lock the STM probe tip into position above selected atoms with sub-Angstrom precision. Once locked the STM tracks the position of the atoms as they migrate over the crystal surface. By tracking individual atoms directly, the ability of the instrument to measure dynamic events is increased by a factor of ~1000 over conventional STM imaging techniques.