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To date, it has remained challenging to achieve N-polar AlN, which is of great importance for high power, high frequency, and high temperature electronics, acoustic resonators and filters, ultraviolet (UV) optoelectronics, and integrated photonics. Here, we performed a detailed study of the molecular beam epitaxy and characterization of N-polar AlN on C-face 4H-SiC substrates. The N-polar AlN films grown under optimized conditions exhibit an atomically smooth surface and strong excitonic emission in the deep UV with luminescence efficiency exceeding 50% at room temperature. Detailed scanning transmission electron microscopy (STEM) studies suggest that most dislocations are terminated/annihilated within ∼200 nm AlN grown directly on the SiC substrate due to the relatively small (1%) lattice mismatch between AlN and SiC. The strain distribution of AlN is further analyzed by STEM and micro-Raman spectroscopy, and its impact on the temperature-dependent deep UV emission is elucidated.

 

Interface engineering in heterostructures at the atomic scale has been a central research focus of nanoscale and quantum material science. Despite its paramount importance, the achievement of atomically ordered heterointerfaces has been severely limited by the strong diffusive feature of interfacial atoms in heterostructures. In this work, we first report a strong dependence of interfacial diffusion on the surface polarity. Near-perfect quantum interfaces can be readily synthesized on the semipolar plane instead of the conventionalc-plane of GaN/AlN heterostructures. The chemical bonding configurations on the semipolar plane can significantly suppress the cation substitution process as evidenced by first-principles calculations, which leads to an atomically sharp interface. Moreover, the surface polarity of GaN/AlN can be readily controlled by varying the strain relaxation process in core–shell nanostructures. The obtained extremely confined, interdiffusion-free ultrathin GaN quantum wells exhibit a high internal quantum efficiency of ~75%. Deep ultraviolet light-emitting diodes are fabricated utilizing a scalable and robust method and the electroluminescence emission is nearly free of the quantum-confined Stark effect, which is significant for ultrastable device operation. The presented work shows a vital path for achieving atomically ordered quantum heterostructures for III-nitrides as well as other polar materials such as III-arsenides, perovskites, etc.

 

N-polar AlGaN is an emerging wide-bandgap semiconductor for next-generation high electron mobility transistors and ultraviolet light emitting diodes and lasers. Here, we demonstrate the growth and characterization of high-quality N-polar AlGaN films on C-face 4H-silicon carbide (SiC) substrates by molecular beam epitaxy. On optimization of the growth conditions, N-polar AlGaN films exhibit a crack free, atomically smooth surface (rms roughness ∼ 0.9 nm), and high crystal quality with low density of defects and dislocations. The N-polar crystallographic orientation of the epitaxially grown AlGaN film is unambiguously confirmed by wet chemical etching. We demonstrate precise compositional tunability of the N-polar AlGaN films over a wide range of Al content and a high internal quantum efficiency ∼74% for the 65% Al content AlGaN film at room temperature. Furthermore, controllable silicon (Si) doping in high Al content (65%) N-polar AlGaN films has been demonstrated with the highest mobility value ∼65 cm2/V-s observed corresponding to an electron concentration of 1.1 × 1017 cm−3, whereas a relatively high mobility value of 18 cm2/V-s is sustained for an electron concentration of 3.2 × 1019 cm−3, with an exceptionally low resistivity value of 0.009 Ω·cm. The polarity-controlled epitaxy of AlGaN on SiC presents a viable approach for achieving high-quality N-polar III-nitride semiconductors that can be harnessed for a wide range of emerging electronic and optoelectronic device applications.

 

Hurricanes are one of the most catastrophic natural forces with potential to inflict severe damages to properties and loss of human lives from high winds and inland flooding. Accurate long-term forecasting of the trajectory and intensity of advancing hurricanes is therefore crucial to provide timely warnings for civilians and emergency responders to mitigate costly damages and their life-threatening impact. In this paper, we present a novel online learning framework called JOHAN that simultaneously predicts the trajectory and intensity of a hurricane based on outputs produced by an ensemble of dynamic (physical) hurricane models. In addition, JOHAN is designed to generate accurate forecasts of the ordinal-valued hurricane intensity categories to ensure that their severity level can be reliably communicated to the public. The framework also employs exponentially-weighted quantile loss functions to bias the algorithm towards improving its prediction accuracy for high category hurricanes approaching landfall. Experimental results using real-world hurricane data demonstrated the superiority of JOHAN compared to several state-of-the-art learning approaches. 

Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier scores to detect the anomalies. However, due to the high complexity brought upon by the over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Our experimental results also show the resiliency of the framework to missing values compared to other baseline methods.