Application of electron beams in thermal processing of semiconductor materials and devices

Rapid and effective thermal processing methods using electron beams are described in this paper. Heating times ranging from a fraction of a second to several seconds and temperatures up to 1400°C are attainable. Applications such as the annealing of ion implanted material, both without significant dopant diffusion and with highly controlled diffusion of impurities, are described. The technique has been used successfully to activate source/drain regions for fine geometry NMOS transistors. It is shown that electron beams can produce localised heating of semiconductor substrates and a resolution of approximately 1 μm has been achieved. Electron beam heating has been applied to improving the crystalline quality of silicon-on sapphire used in CMOS device fabrication. Silicon layers with defect levels approaching bulk material have been obtained. Finally, the combination of isothermal and selective annealing is shown to have application in recrystallisation of polysilicon films on an insulating layer. The approach provides the opportunity of producing a silicon-on-insulator substrate with improved crystalline quality compared to silicon-on-sapphire at a potentially lower cost. It is suggested that rapid heating methods are expected to provide a real alternative to conventional furnace processing of semiconductor devices in the development of fabrication technology. © 1984 Benn electronics Publications Ltd, Luton.

Gaussian etched single transverse mode vertical-cavity surface-emitting laser

A continuous Gaussian profile matched to the fundamental mode was etched onto the aperture of a vertical cavity surface emitting laser (VCSEL). Single Gaussian spot emission was achieved over the entire operating current range.

Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.