Beam propagation modelling of tapered waveguide lasers

A dynamic beam propagation model allows design optimization of high power low divergence tapered waveguide lasers. The model is extended to include spatially-resolved temperature profiles and a temperature dependent gain. Using this model, design parameters such as the optimum facet reflectivity, taper angle, and waveguide dimension can be calculated for low far-field divergence and high continuous wave power.

Mechanism of polarization pinning in vertical cavity surface emitting lasers using focused ion beam etching

Photoluminescence experiments have identified strain as the origin for polarization pinning in vertical cavity surface emitting lasers post-processed by focused ion beam etching. Theoretical models were applied to deduce the strain in devices. Post-annealing was used to optimize polarization pinning.

Nanostructured carbon films from supersonic cluster beam deposition: Structure and morphology

Nanostructured carbon thin films have been grown by deposition of cluster beams produced by a supersonic expansion. Due to separation effects typical of supersonic beams, films with different nanostructures can be grown by the simple intercepting of different regions of the cluster beam with a substrate. Films show a low-density porous structure, which has been characterized by Raman and Brillouin spectroscopy. Film morphology suggests that growth processes are similar to those occurring in a ballistic deposition regime.

High power laser with integrated lens using focused ion beam etching

In this study, a collimating lens is introduced at the output facet of a tapered waveguide laser to compensate for the divergence of the optical mode. The collimating lens is shown to enhance the laser efficiency while simultaneously reducing the far field divergence.

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.

Bistable composite slit tubes. I. A beam model

Composite slit tubes with a circular cross-section show an interesting variety in their large-deformation behaviour, that depends on the layup of the surface that is used: tubes made from many antisymmetric laminae are bistable, and have a compact coiled configuration, tubes made from similar, but symmetric, laminae do not have a compact coiled state, and indeed may not be bistable, while tubes made from an isotropic sheet are not bistable. A simple model is presented here that is able to distinguish between these behaviours; it assumes that the cross-section remains circular, but allows twist, which is shown to be the key to making the distinction between the behaviours described. © 2004 Elsevier Ltd. All rights reserved.

Focused ion beam etching of GaN

We have investigated the use of focused ion beam (FIB) etching for the fabrication of GaN-based devices. Although work has shown that conventional reactive ion etching (RIE) is in most cases appropriate for the GaN device fabrication, the direct write facility of FIB etching - a well-established technique for optical mask repair and for IC failure analysis and repair - without the requirement for depositing an etch mask is invaluable. A gallium ion beam of about 20nm diameter was used to sputter GaN material. The etching rate depends linearly on the ion dose per area with a slope of 3.5×10 -4μm3/pC. At a current of 3nA, for example, this corresponds to an etch rate of 1.05μm3/s. Good etching qualities have been achieved with a side wall roughness significantly below 0.1μm. Changes in the roughness of the etched surface plane stay below 8nm.

Free space adaptive optical interconnect, using a ferroelectric liquid crystal SLM for beam steering

A free-space, board-to-board, adaptive optical interconnect demonstrator has been developed. Binary phase gratings displayed on a Ferroelectric Liquid Crystal Spatial Light Modulator are used to maintain data transfer at 1.25Gbps, given varying optical misalignment © 2005 Optical Society of America.

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.