Evaluation and modeling of lanthanum diffusion in TiN/La 2O 3/HfSiON/SiO 2/Si high-k stacks

In this study, TiN/La 2O 3/HfSiON/SiO 2/Si gate stacks with thick high-k (HK) and thick pedestal oxide were used. Samples were annealed at different temperatures and times in order to characterize in detail the interaction mechanisms between La and the gate stack layers. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurements performed on these samples show a time diffusion saturation of La in the high-k insulator, indicating an La front immobilization due to LaSiO formation at the high-k/interfacial layer. Based on the SIMS data, a technology computer aided design (TCAD) diffusion model including La time diffusion saturation effect was developed. © 2012 American Institute of Physics.

Spatial determination of gold catalyst residue used in the production of ZnO nanowires by SIMS depth profiling analysis

In this paper we demonstrate how secondary ion mass spectrometry (SIMS) can be applied to ZnO nanowire structures for gold catalyst residue determination. Gold plays a significant role in determining the structural properties of such nanowires, with the location of the gold after growth being a strong indicator of the growth mechanism. For the material investigated here, we find that the gold remains at the substrate-nanowire interface. This was not anticipated as the usual growth mechanism associated with catalyst growth is of a vapour-liquid-solid (VLS) type. The results presented here favour a vapour-solid (VS) growth mechanism instead. Copyright © 2007 John Wiley & Sons, Ltd.

APPROXIMATE GAUSS-MARKOV REALISATION OF MULTIVARIATE STOCHASTIC PROCESSES.

Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks to determine a finite-dimensional linear time-invariant system which, when driven by white noise, will produce an output whose spectral density is approximately PHI ( omega ), and an approximate spectral factor of PHI ( omega ). The author employs the Anderson-Faurre theory in his analysis.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.