Formation of high-quality Ag-based ohmic contacts to p-type GaN

Low resistance and high reflectance ohmic contacts on p-type GaN were achieved using an Ag-based metallization scheme. Oxidation annealing was the key to achieve ohmic behavior of Ag-based contacts on p-type GaN. A low contact resistivity of similar to 5x10(-5) Omega cm(2) could be achieved from Me (=Ni, Ir, Pt, or Ru)/Ag (50/1200 angstrom) contacts after annealing at 500 degrees C for 1 min in O(2) ambient. Oxidation annealing promoted the out-diffusion of Ga atoms from the GaN layer, and Ga atoms dissolved in the in-diffused Ag layer with the formation of Ag-Ga solid solution, resulting in ohmic contact formation. Using Ru/Ni/Au (500/200/500 angstrom) overlayers on the Me/Ag contacts, the excessive incorporation of oxygen molecules into the contact interfacial region, and the out-diffusion and agglomeration of Ag, were effectively prevented during oxidation annealing. As a result, a high reflectance of 87.2% at the 460 nm wavelength and a smooth surface morphology could be obtained simultaneously. (C) 2008 The Electrochemical Society.open111618sciescopu

SRZoo: An integrated repository for super-resolution using deep learning

Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However, their implementations and evaluations are dispersed in terms of various deep learning frameworks and various evaluation criteria. In this paper, we propose an integrated repository for the super-resolution tasks, named SRZoo, to provide state-of-the-art super-resolution models in a single place. Our repository offers not only converted versions of existing pre-trained models, but also documentation and toolkits for converting other models. In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place. It also brings the opportunity of extension to advanced image-based researches and other image processing models. The software, documentation, and pre-trained models are publicly available on GitHub.Comment: Accepted in ICASSP 2020, code available at https://github.com/idearibosome/srzo