Atomic-scale mapper for superlattice photodetectors analysis.

In this work, a new Python-based tool for atomic-scale mapping of high-angle annular dark-field (HAADF) and annular bright-field (ABF) scanning transmission electron microscopy (STEM) images using the Z-contrast method is introduced, aimed to help in the analysis of superlattice layers' composition, and in the determination of material of interfaces. The operation principle of the program, as well as specific examples of use, are explained in many details. Good customization flexibility using the user-friendly graphical user interface (GUI), allows the processing of a wide range of images, demonstrating a decent accuracy of coordinates extraction and performance.

Comparative advantages of a type-II superlattice barrier over an AlGaSb barrier for enhanced performance of InAs/GaSb LWIR nBn photodetectors.

The barrier layer in InAs/GaSb LWIR nBn detector is usually composed of AlGaSb alloy, which has a non-negligible valence band offset and is sensitive to chemical solutions. In this work, we investigated a type-II superlattice (T2SL) barrier that is homogeneous with the T2SL absorber layer in order to resolve these drawbacks of the AlGaSb barrier. The lattice mismatch of the T2SL barrier was smaller than that of the AlGaSb barrier.

3D Stackable Synaptic Transistor for 3D Integrated Artificial Neural Networks.

Although they have attracted enormous attention in recent years, software-based and two-dimensional hardware-based artificial neural networks (ANNs) may consume a great deal of power. Because there will be numerous data transmissions through a long interconnection for learning, power consumption in the interconnect will be an inevitable problem for low-power computing. Therefore, we suggest and report 3D stackable synaptic transistors for 3D ANNs, which would be the strongest candidate in future computing systems by minimizing power consumption in the interconnection.