High photoluminescence quantum efficiency InGaN multiple quantum well structures emitting at 380 nm

In this paper we report the design of high room temperature photoluminescence internal efficiency InGaN-based quantum well structures emitting in the near ultraviolet at 380 nm. To counter the effects of nonradiative recombination the quantum wells were designed to have a large indium fraction, high barriers, and a small quantum well thickness. To minimize the interwell and interbarrier thickness fluctuations we used Al0.2In0.005Ga0.795N barriers, where the inclusion of the small fraction of indium was found to lead to fewer structural defects and a reduction in the layer thickness fluctuations. This approach has led us to achieve, for an In0.08Ga0.92N/Al0.2In0.005Ga0.795N multiple quantum well structure with a well width of 1.5 nm, a photoluminescence internal efficiency of 67% for peak emission at 382 nm at room temperature. (c) 2007 American Institute of Physics.

Optical and micro-structural properties of high photoluminescence efficiency InGaN/AlInGaN quantum well structures

We have studied the optical properties of a series of InGaN/AlInGaN 10-period multiple quantum wells (MQW) with differing well thickness grown by metal-organic vapor-phase epitaxy that emit at around 380 nm. The aim of this investigation was to optimise the room temperature internal quantum efficiency, thus the quantum well (QW) thicknesses were accordingly chosen so that the overlap of the electron/hole wave function was maximised. At low temperature, we observed a reduction of the photo luminescence decay time with decreasing well width in line with the theoretical predictions. For a structure with well thicknesses of 1.5 nm, we measured a photoluminescence internal quantum efficiency of 67% at room temperature with a peak emission wavelength of 382 nm. (c) 2006 Elsevier B.V. All rights reserved.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.