We investigate exciton states theoretically in strained GaN/AlN quantum dots with wurtzite ~WZ! and zinc-blende ~ZB! crystal structures, as well as strained WZ GaN/AlGaN quantum dots. We show that the strain field significantly modifies the conductionand valence-band edges of GaN quantum dots. The piezoelectric field is found to govern excitonic properties of WZ GaN/AlN quantum dots, while it h...

Erbium-doped GaN (GaN:Er) epilayers were synthesized by metal organic chemical vapor deposition. GaN:Er waveguides were fabricated based on four different GaN:Er layer structures: GaN:Er/GaN/Al2O3, GaN:Er/GaN/AlN/Al2O3, GaN:Er/GaN/Al(0.75)Ga(0.25)N/AlN/Al2O3, and GaN/GaN:Er/GaN/Al2O3. Optical loss at 1.54 μm in these waveguide structures has been measured. It was found that the optical attenuat...

in present work, we have calculated the electronic properties including density of states (dos) and electron density for gan, inn and in wurtzite phase for x=0.5. the study is based on density functional theory (dft) with full potential linearized augmented plane wave method (fp-lapw) by generalized gradient approximation (gga-pbesol) for calculating electronic properties. in this report we con...

Softmax GAN is a novel variant of Generative Adversarial Network (GAN). The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross-entropy loss in the sample space of one single batch. In the adversarial learning of N real training samples and M generated samples, the target of discriminator training is to distribute all the probability mass to th...

Replacing SiC substrates with the highest thermal conductivity material available, diamond (κ up to 2000 W/mK), will result in significantly lower thermal resistance AlGaN/GaN HEMTs. In this work we combine Raman thermography and thermal simulation to assess the thermal resistance of state-of-the-art GaN-ondiamond HEMTs. INTRODUCTION The RF output power density achievable for GaN-based high ele...

In this project we try to explore the possibility of using Conditional Adversarial Networks (Conditional GAN) to enhance images. Conditional Adversarial Networks can learn the image-to-image translation and adapt the translation to future images. We try to use Conditional GAN to learn the translation between images from original images and enhanced images and automatically translate original im...