Dislocations in AlGaN: Core Structure, Atom Segregation, and Optical Properties

Abstract

We conducted a comprehensive investigation of dislocations in AlGaN. Using aberration-corrected scanning transmission electron microscopy and energy dispersive X-ray spectroscopy, the atomic structure and atom distribution at the dislocation core have been examined. We report that the core configuration of dislocations in AlGaN is consistent with that of other materials in the III-Nitride system. However, we observed that the dissociation of mixed-type dislocations is impeded by alloying GaN with AlN, which is confirmed by our experimental observation of Ga and Al atom segregation in the tensile and compressive parts of the dislocations, respectively. Investigation of the optical properties of the dislocations shows that the atom segregation at dislocations has no significant effect on the intensity recorded by cathodoluminescence in the vicinity of the dislocations. These results are in contrast with the case of dislocations in InGaN where segregation of In and Ga atoms also occurs but results in carrier localization limiting non-radiative recombination at the dislocation. This study therefore sheds light on why InGaN-based devices are generally more resilient to dislocations than their AlGaN-based counterparts.

Growth Temperature Dependency of High Al Content AlGaN Epilayers on AlN/Al2O3 Templates

“…These dark spots cannot be distinguished clearly on the surface of sample A because of the rough surface. These dark spots highlight the non-radiative behavior of the dislocations in AlGaN [18]. Based on their different sizes dark spots can be classified as pure-edge type (small), pure-screw type (large) and mixed-type (middle) threading dislocations [19].…”

A machine perspective of atomic defects in scanning transmission electron microscopy

“…These developments have long been mainstays to understand the structure‐property correlation of materials by providing various types of microscopy data. Extracting useful structural information from these data in a systematic way is essential to underpin the physics of a material system whether in 2D materials or other systems . In data analytics of STEM, machine learning techniques, such as dimension reduction, clustering analysis, deep learning, and so on, show remarkable results for structural defect identification.…”

“…Extracting useful structural information from these data in a systematic way is essential to underpin the physics of a material system whether in 2D materials or other systems. 113,114 In data analytics of STEM, machine learning techniques, such as dimension reduction, clustering analysis, deep learning, and so on, show remarkable results for structural defect identification. New routines based on machine learning are able to handle a large volume of data while retaining high accuracy.…”

Thermodynamic photoinduced disorder in AlGaN nanowires

“…70,73 However, in our present case, entropy generation was nearly constant above 200 K. We hypothesize that surface defect states dominantly contributed to entropy generation above this temperature, and conjecture that the Al 0.18 Ga 0.82 N alloy composition fluctuations, 74 which are intrinsic in localized states that are present in only very small sections (<2 nm), 75 contributed to the lower degrees of disorder-induced uncertainty related to the energy of states involved in thermionic transitions at higher temperatures because of the effects of atomic ordering. [76][77][78] To support this hypothesis, we fitted the following stretched-exponential decay model to our TRPL decay curves…”