3 research outputs found

    Simulation of Al0.85Ga0.15As0.56Sb0.44 avalanche photodiodes

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    Al0.85Ga0.15As0.56Sb0.44 is a promising avalanche material for near infrared avalanche photodiodes (APDs) because they exhibit very low excess noise factors. However electric field dependence of ionization coefficients in this material have not been reported. We report a Simple Monte Carlo model for Al0.85Ga0.15As0.56Sb0.44, which was validated using reported experimental results of capacitance-voltage, avalanche multiplication and excess noise factors from five APDs. The model was used to produce effective ionization coefficients and threshold energies between 400–1200 kV.cm-1 at room temperature, which are suitable for use with less complex APD simulation models

    Extremely low excess noise avalanche photodiode with GaAsSb absorption region and AlGaAsSb avalanche region

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    An extremely low noise Separate Absorption and Multiplication Avalanche Photodiode (SAM-APD), consisting of a GaAs0.52Sb0.48 absorption region and an Al0.85Ga0.15As0.56Sb0.44 avalanche region, is reported. The device incorporated an appropriate doping profile to suppress tunneling current from the absorption region, achieving a large avalanche gain, ∼130 at room temperature. It exhibits extremely low excess noise factors of 1.52 and 2.48 at the gain of 10 and 20, respectively. At the gain of 20, our measured excess noise factor of 2.48 is more than three times lower than that in the commercial InGaAs/InP SAM-APD. These results are corroborated by a Simple Monte Carlo simulation. Our results demonstrate the potential of low excess noise performance from GaAs0.52Sb0.48/Al0.85Ga0.15As0.56Sb0.44 avalanche photodiodes

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease
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