Schottky barrier heights of tantalum oxide, barium strontium titanate, lead titanate, and strontium bismuth tantalate

The Schottky barrier heights of various metals on the high permitivity oxides tantalum pentoxide, barium strontium titanate, lead zirconate titanate, and strontium bismuth tantalate have been calculated as a function of the metal work function. It is found that these oxides have a dimensionless Schottky barrier pinning factor S of 0.28-0.4 and not close to 1 because S is controlled by Ti-O-type bonds not Sr-O-type bonds, as assumed in earlier work. The band offsets on silicon are asymmetric with a much smaller offset at the conduction band, so that Ta2O5 and barium strontium titanate are relatively poor barriers to electrons on Si. © 1999 American Institute of Physics.

Schottky barrier heights of tantalum oxide, barium strontium titanate, lead zirconate titanate and strontium bismuth tantalate

Schottky barrier heights of various metals on tantalum pentoxide, barium strontium titanate, lead zirconate-titanate and strontium bismuth tantalate have been calculated as a function of metal work function. These oxides have a dimensionless Schottky barrier pinning factor, S, of 0.28 - 0.4 and not close to 1, because S is controlled by the Ti-O type bonds not Sr-O type bonds, as assumed previously. Band offsets on silicon are asymmetric with much smaller offset at the conduction band, so that Ta2O5 and barium strontium titanate (BST) are relatively poor barriers to electrons on Si.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.