arXiv subjects

A. Benhadjira

Publications and source records attributed to A. Benhadjira.

2 recordsLinked to original sources

arXiv2026

Spatially resolved elastic strain and lattice rotation at threading dislocations in HgCdTe/CdZnTe epilayers by dark-field X-ray microscopy

Threading dislocations (TDs) propagating from a Cd$_{1-y}$Zn$_{y}$Te (CZT) substrate into a liquid-phase-epitaxy Hg$_{1-x}$Cd$_{x}$Te (MCT) epilayer set the minority-carrier lifetime and dark-current floor of mid-wave infrared focal-plane arrays, yet at device-grade densities their local strain fields have been accessible only through topography, which conflates lattice tilt and elastic strain. We apply dark-field X-ray microscopy in reflection geometry to a \SI{7}{\micro\metre}-thick (111) MCT/CZT epilayer. Shallow Bragg angle and absorption makes the signal layer dominated while the numerical aperture of the objective keeps the layer and substrate rocking curves convolved, so weak-beam images on either side of the rocking curve and their difference image the correlated defects in a single frame: dot-like substrate TDs and the elongated, in-plane island features they nucleate in the layer. Kernel average misorientation resolves each TD as a \SI{7}{\micro\metre} signature, the layer thickness, alongside axial strain lobes of $\pm(4$ to $5)\times10^{-5}$.

cond-mat.mtrl-sci↗

arXiv2026

Deep Learning-Assisted Weak Beam Identification in Dark-Field X-ray Microscopy

Dislocations control the mechanical behavior of crystalline materials, yet their quantitative characterization in bulk has remained elusive. Transmission Electron Microscopy provides atomic-scale resolution but is restricted to thin foils, limiting relevance to structural performance. Dark-field X-ray microscopy (DFXM) has recently opened access to three-dimensional, non-destructive imaging of dislocations in macroscopic crystals. A critical bottleneck, however, is the reliable identification of weak- versus strong-beam conditions. Weak-beam imaging enhances dislocation contrast, while strong-beam conditions are dominated by multiple scattering and obscure interpretation. Current practice depends on manual classification by specialists, which is subjective, slow, and incompatible with the scale of modern experiments. Here, we introduce a deep learning framework that automates this task using a lightweight convolutional neural network trained on small, hand-labeled datasets. By enabling robust, rapid, and scalable identification of imaging conditions, this approach supports scalable DFXM analysis, unlocking statistically significant studies of dislocation dynamics in bulk material

cond-mat.mtrl-sci↗