Characterization of the temperature sensitivity of gain and recombination mechanisms in 1.3-μm AlGaInAs MQW lasers

The potential of 1.3-μm AlGaInAs multiple quantum-well (MQW) laser diodes for uncooled operation in high-speed optical communication systems is experimentally evaluated by characterizing the temperature dependence of key parameters such as the threshold current, transparency current density, optical gain and carrier lifetime. Detailed measurements performed in the 20°C-100°C temperature range indicate a localized T0 value of 68 K at 98°C for a device with a 2.8μm ridge width and 700-μm cavity length. The transparency current density is measured for temperatures from 20°C to 60°C and found to increase at a rate of 7.7 A·cm -2 · °C-1. Optical gain characterizations show that the peak modal gain at threshold is independent of temperature, whereas the differential gain decreases linearly with temperature at a rate of 3 × 10-4 A-1·°C-1. The differential carrier lifetime is determined from electrical impedance measurements and found to decrease with temperature. From the measured carrier lifetime we derive the monomolecular (A), radiative (B), and nonradiative Auger (C) recombination coefficients and determine their temperature dependence in the 20 °C-80 °C range. Our study shows that A is temperature independent, B decreases with temperature, and C exhibits a less pronounced increase with temperature. The experimental observations are discussed and compared with theoretical predictions and measurements performed on other material systems. © 2005 IEEE.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.