Characterization of GaN Nanorods Fabricated Using Ni Nanomasking and Reactive Ion Etching: A Top-Down Approach

Large thermal mismatch between GaN surface and sapphire results in compressive stress in Gallium Nitride (GaN) layer which degrades the device performance. Nanostructuring the GaN can reduce this stress leading to reduction in Quantum Confined Stark Effect. Aligned GaN nanorods based nanodevices have potential applications in electronics and optoelectronics. This paper describes the fabrication of GaN nanorods using Ni nanomasking and reactive ion etching. The morphology of GaN nanorods was studied by field emission scanning electron microscopy. The optical properties of GaN nanorods were studied by Cathodoluminescence (CL) spectroscopy. CL results revealed the existence of characteristic band-edge luminescence and yellow band luminescence. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3099

First principles phase diagram calculations for the wurtzite-structure systems AlN–GaN, GaN–InN, and AlN–InN

First principles phase diagram calculations were performed for the wurtzite-structure quasibinary systems AlN–GaN, GaN–InN, and AlN–InN. Cluster expansion Hamiltonians that excluded, and included, excess vibrational contributions to the free energy, Fvib, were evaluated. Miscibility gaps are predicted for all three quasibinaries, with consolute points, (XC,TC), for AlN–GaN, GaN–InN, and AlN–InN equal to (0.50, 305 K), (0.50, 1850 K), and (0.50, 2830 K) without Fvib, and (0.40, 247 K), (0.50, 1620 K), and (0.50, 2600 K) with Fvib, respectively. In spite of the very different ionic radii of Al, Ga, and In, the GaN–InN and AlN–GaN diagrams are predicted to be approximately symmetric

Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with an additional autoencoder component, where recurrent neural networks (RNNs) are used for each component of the model in order to generate synthetic data to improve classification accuracy for a highly imbalanced medical device dataset. In addition to the medical device dataset, we also evaluate the GAN-AE performance on two additional datasets and demonstrate the application of GAN-AE to a sequence-to-sequence task where both synthetic sequence inputs and sequence outputs must be generated. To evaluate the quality of the synthetic data, we train encoder-decoder models both with and without the synthetic data and compare the classification model performance. We show that a model trained with GAN-AE generated synthetic data outperforms models trained with synthetic data generated both with standard oversampling techniques such as SMOTE and Autoencoders as well as with state of the art GAN-based models

Electrical Properties of Atomic Layer Deposited Aluminum Oxide on Gallium Nitride

We report on our investigation of the electrical properties of metal/Al2O3/GaN metal-insulator-semiconductor (MIS) capacitors. We determined the conduction band offset and interface charge density of the alumina/GaN interface by analyzing capacitance-voltage characteristics of atomic layer deposited Al2O3 films on GaN substrates. The conduction band offset at the Al2O3/GaN interface was calculated to be 2.13 eV, in agreement with theoretical predications. A non-zero field of 0.93 MV/cm in the oxide under flat-band conditions in the GaN was inferred, which we attribute to a fixed net positive charge density of magnitude 4.60x1012 cm-2 at the Al2O3/GaN interface. We provide hypotheses to explain the origin of this charge by analyzing the energy band line-up.Comment: 8 pages, 4 figures, Applied Physics Letter