Influence of cluster-assembly parameters on the field emission properties of nanostructured carbon films

Supersonic cluster beam deposition has been used to produce films with different nanostructures by controlling the deposition parameters such as the film thickness, substrate temperature and cluster mass distribution. The field emission properties of cluster-assembled carbon films have been characterized and correlated to the evolution of the film nanostructure. Threshold fields ranging between 4 and 10 V/mum and saturation current densities as high as 0.7 mA have been measured for samples heated during deposition. A series of voltage ramps, i.e., a conditioning process, was found to initiate more stable and reproducible emission. It was found that the presence of graphitic particles (onions, nanotube embryos) in the films substantially enhances the field emission performance. Films patterned on a micrometer scale have been conditioned spot by spot by a ball-tip anode, showing that a relatively high emission site density can be achieved from the cluster-assembled material. (C) 2002 American Institute of Physics.

Stress reduction and bond stability during thermal annealing of tetrahedral amorphous carbon

A comprehensive study of the stress release and structural changes caused by postdeposition thermal annealing of tetrahedral amorphous carbon (ta-C) on Si has been carried out. Complete stress relief occurs at 600-700°C and is accompanied by minimal structural modifications, as indicated by electron energy loss spectroscopy, Raman spectroscopy, and optical gap measurements. Further annealing in vacuum converts sp3 sites to sp2 with a drastic change occurring after 1100°C. The field emitting behavior is substantially retained up to the complete stress relief, confirming that ta-C is a robust emitting material. © 1999 American Institute of Physics.

An approximate likelihood method for estimating static parameters in multi-target tracking models

This paper considers a class of dynamic Spatial Point Processes (PP) that evolves over time in a Markovian fashion. This Markov in time PP is hidden and observed indirectly through another PP via thinning, displacement and noise. This statistical model is important for Multi object Tracking applications and we present an approximate likelihood based method for estimating the model parameters. The work is supported by an extensive numerical study.