High Indium Concentration InGaN/GaN Grown on Sapphire Substrate by MOCVD

The InGaN system provides the opportunity to fabricate light emitting devices over the whole visible and ultraviolet spectrum due to band-gap energies E[subscript g] varying between 3.42 eV for GaN and 1.89 eV for InN. However, high In content in InGaN layers will result in a significant degradation of the crystalline quality of the epitaxial layers. In addition, unlike other III-V compound semiconductors, the ratio of gallium to indium incorporated in InGaN is in general not a simple function of the metal atomic flux ratio, f[subscript Ga]/f[subscript In]. Instead, In incorporation is complicated by the tendency of gallium to incorporate preferentially and excess In to form metallic droplets on the growth surface. This phenomenon can definitely affect the In distribution in the InGaN system. Scanning electron microscopy, room temperature photoluminescence, and X-ray diffraction techniques have been used to characterize InGaN layer grown on InN and InGaN buffers. The growth was done on c-plane sapphire by MOCVD. Results showed that green emission was obtained which indicates a relatively high In incorporation.

Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.