Related papers: Deep Learning Enabled Strain Mapping of Single-Ato…
Here, we evaluate multislice electron ptychography as a tool to carry out depth-resolved atomic resolution characterization of point defects, using silicon carbide as a case study. Through multislice electron scattering simulations and…
Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. In this work, we introduce end-to-end training of the…
The use of differential phase contrast (DPC) in scanning transmission electron microscopy (STEM) has shown much promise for directly investigating the functional properties of a material system, leveraging the natural coupling between the…
Structural defects in 2D materials offer an effective way to engineer new material functionalities beyond conventional doping in semiconductors. Specifically, deep in-gap defect states of chalcogen vacancies have been associated with…
Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications. Scanning electron nanodiffraction…
Typical Raman spectra of transition metal dichalcogenides (TMDs) display two prominent peaks, E2g and A1g, that are well separated from each other. We find that these modes are degenerate in bulk WSe2 yielding one single Raman peak. As the…
Electronic charge transfer at the atomic scale can reveal fundamental information about chemical bonding, but is far more challenging to directly image than the atomic structure. The charge density is dominated by the atomic nuclei, with…
Crystal structure imperfections in solids often act as efficient carrier trapping centers which, when suitably isolated, act as sources of single photon emission. The best known examples of such attractive imperfections are wellwidth or…
Strain is ubiquitous in solid-state materials, but despite its fundamental importance and technological relevance, leveraging externally applied strain to gain control over material properties is still in its infancy. In particular, strain…
We demonstrate that the sub-atomically focused beam of a scanning transmission electron microscope (STEM) can be used to controllably manipulate individual dopant atoms in a 2D graphene lattice. We demonstrate the manipulation of adsorbed…
This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep…
Scanning precession electron diffraction (SPED) is a powerful technique for investigating strain. While extensive literature exists analysing strain under high convergence angle conditions there are few systematic studies describing work…
Two-dimensional (2D) materials are strongly affected by the dielectric environment including substrates, making it an important factor in designing materials for quantum and electronic technologies. Yet, first-principles evaluation of…
The association of scanning transmission electron microscopy (STEM) and the detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of…
Imperfections in the crystal structure, such as point defects, can strongly modify the optical and transport properties of materials. Here, we study the effect of point defects on the optical and DC conductivities of single layers of…
First isolated in 2004, graphene monolayers display unique properties and promising technological potential in next generation electronics, optoelectronics, and energy storage. The simple yet effective methodology, mechanical exfoliation…
The application of ultrathin two-dimensional (2D) perovskites in near-infrared and visible-range optoelectronics has been limited owing to their inherent wide bandgaps, large excitonic binding energies and low optical absorption at higher…
In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain.…
This proposes a novel ensemble deep learning-based model to accurately classify, detect and localize different defect categories for aggressive pitches and thin resists (High NA applications).In particular, we train RetinaNet models using…
Two-dimensional (2D) semiconductors are widely recognized as attractive channel materials for low-power electronics. However, an unresolved challenge is the integration of high-quality, ultrathin high-\k{appa} dielectrics that fully meet…