428g Surface Effects on Oxygen Defects In TiO2 for Nanoelectronic Materials

Alice G. Hollister, Chemical and Biomolecular Engineering, University of Illinois, Urbana - Champaign, 105 Roger Adams Lab, 600 S. Matthews, Urbana, IL 61801 and Edmund G. Seebauer, Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL 61820.

The technologically useful properties of a solid often depend upon the types and concentrations of the defects it contains, particularly in semiconductors. Native point defects influence the effectiveness of nanoelectronic materials for devices in applications that include high voltage electronics, light emitting diodes, solid-state electrolyte sensors, and solar power generators. Recent work in our laboratory has shown that semiconductor surfaces can serve as especially efficient pathways for the generation and annihilation of point defects. At the nanoscale, such surface pathways become particularly important due to the large ratio of surface area to volume. A significant degree of control over the surface pathways has been demonstrated via the variation of surface bonding state: for example, many dangling bonds versus only a few.

We have employed measurements of isotopic self-diffusion to indirectly monitor the concentrations and behavior of native point defects in metal oxides such as titania that find use as nano-electronic materials. In particular, experiments employing secondary ion mass spectroscopy have yielded concentration profiles of the defect-mediated self-diffusion of isotopic oxygen into titania. Surface chemical bonding state greatly influences the observed diffusion rates, which increase when the surface is held essentially atomically clean in high vacuum. Such surfaces appear to promote the formation of a highly mobile defect within titania, although several distinct mechanisms are possible. We have developed three potential models for the mechanism. Simulations based on continuum equations for each of the three defect mechanisms have permitted the application of formal model discrimination methods to identify the most likely mechanism.