Low dark current InAs/GaSb type-II superlattice infrared photodetectors with resonant tunnelling filters

InAs/GaSb type-II strained-layer superlattice (SLS) photovoltaic infrared (IR) detectors are currently of great interest for mid- and long-wave IR detection. A novel technique of reducing detector dark current by inserting resonant tunnelling barriers into a conventional InAs/GaSb SLS is investigated. The GaSb/InAs/GaSb resonant tunnelling double barrier heterostructure was designed to be periodically inserted into a conventional InAs/GaSb SLS detector to block thermally excited electrons, while permitting photo-excited electrons to tunnel through. The measured dark current density of the tunnelling InAs/GaSb SLS detector in the entire negative bias range is lower than that of the conventional SLS detector by a factor of about 3.8 at 77 K. At 84 K, the Johnson-noise-limited detectivity of the tunnelling detector, measured at 4 µm, is 18% higher than that of the conventional detector. Both the conventional and the tunnelling SLS detectors demonstrated high-temperature operation, up to 300 K.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58092/2/d6_23_015.pd

Integral Equation Methods for Electrostatics, Acoustics, and Electromagnetics in Smoothly Varying, Anisotropic Media

[EN] We present a collection of well-conditioned integral equation methods for the solution of electrostatic, acoustic, or electromagnetic scattering problems involving anisotropic, inhomogeneous media. In the electromagnetic case, our approach involves a minor modification of a classical formulation. In the electrostatic or acoustic setting, we introduce a new vector partial differential equation, from which the desired solution is easily obtained. It is the vector equation for which we derive a well-conditioned integral equation. In addition to providing a unified framework for these solvers, we illustrate their performance using iterative solution methods coupled with the FFT-based technique of [F. Vico, L. Greengard, M. Ferrando, J. Comput. Phys., 323 (2016), pp. 191-203] to discretize and apply the relevant integral operators.The work of the authors was partially supported by the Spanish Ministry of Science and Innovation under project TEC2016-78028-C3-3-P and the U.S. Department of Energy under grant DE-FG02-86ER53223.Imbert-Gérard, L.; Vico Bondía, F.; Greengard, L.; Ferrando Bataller, M. (2019). Integral Equation Methods for Electrostatics, Acoustics, and Electromagnetics in Smoothly Varying, Anisotropic Media. SIAM Journal on Numerical Analysis. 57(3):1020-1035. https://doi.org/10.1137/18M1187039S1020103557

Space Weathering Experiments on Spacecraft Materials

A project to investigate space environment effects on specific materials with interest to remote sensing was initiated in 2016. The goal of the project is to better characterize changes in the optical properties of polymers found in multi-layered spacecraft insulation (MLI) induced by electron bombardment. Previous analysis shows that chemical bonds break and potentially reform when exposed to high energy electrons like those seen in orbit. These chemical changes have been shown to alter a material's optical reflectance, among other material properties. This paper presents the initial experimental results of MLI materials exposed to various fluences of high energy electrons, designed to simulate a portion of the geosynchronous Earth orbit (GEO) space environment. It is shown that the spectral reflectance of some of the tested materials changes as a function of electron dose. These results provide an experimental benchmark for analysis of aging effects on satellite systems which can be used to improve remote sensing and space situational awareness. They also provide preliminary analysis on those materials that are most likely to comprise the high area-to-mass ratio (HAMR) population of space debris in the geosynchronous orbit environment. Finally, the results presented in this paper serve as a proof of concept for simulated environmental aging of spacecraft polymers that should lead to more experiments using a larger subset of spacecraft materials

Quantum Size Effects on the Chemical Sensing Performance of Two-Dimensional Semiconductors

We investigate the role of quantum confinement on the performance of gas sensors based on two-dimensional InAs membranes. Pd-decorated InAs membranes configured as H2 sensors are shown to exhibit strong thickness dependence, with ~100x enhancement in the sensor response as the thickness is reduced from 48 to 8 nm. Through detailed experiments and modeling, the thickness scaling trend is attributed to the quantization of electrons which favorably alters both the position and the transport properties of charge carriers; thus making them more susceptible to surface phenomena

Single spin asymmetry measurements for π0\pi^0 inclusive productions in p+p↑→π0+Xp+p_{\uparrow} \to \pi^0 + X and \pi^-+\p_{\uparrow}\to \pi^0+X reactions at 70 and 40 GeV respectively

The inclusive $\pi^0$ asymmetries were measured in reactions $p+p\uparrow \to \pi^0+X$ and $\pi^-+p\uparrow \to \pi^0+X$ at 70 and 40 GeV/c respectively. The measurements were made at the central region (for the first reaction) and asymmetry is compatible with zero in the entire measured $p_T$ region. For the second reaction the asymmetry is zero for small $x_F$ region ($-0.4<x_F<-0.1, 0.5<p_T(GeV/c) <1.5$) and increases with growth of $\mid x_F\mid$. Averaged over the interval $-0.8<x_F<-0.4, 1<p_T(GeV/c)<2$ the asymmetry was $-(13.8\pm 3.8)$.Comment: 4 pages, 2 figures; Presented at SPIN-2004 at Trieste, October 10-16,200

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions