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.

Fast Object Recognition in Noisy Images Using Simulated Annealing

A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.