Optical Characterization of Indium Gallium Nitride for Application in High-Efficiency Solar Photovoltaic Cells

The semiconductor alloy indium gallium nitride (InxGa1-xN) offers substantial potential in the development of high-efficiency multi-junction photovoltaic devices due to its wide range of direct band gaps, strong absorption and other optoelectronic properties. This work uses a variety of characterization techniques to examine the properties of InxGa1-xN thin films deposited in a range of compositions by a novel plasma-enhanced evaporation deposition system. Due to the high vapour pressure and low dissociation temperature of indium, the indium incorporation and, ultimately, control of the InxGa1-xN composition was found to be influenced to a greater degree by deposition temperature than variations in the In:Ga source rates in the investigated region of deposition condition space. Under specific deposition conditions, crystalline films were grown in an advantageous nano-columnar microstructure with deposition temperature influencing column size and density. The InxGa1-xN films were determined to have very strong absorption coefficients with band gaps indirectly related to indium content. However, the films also suffer from compositional inhomogeneity and In-related defect complexes with strong phonon coupling that dominates the emission mechanism. This, in addition to the presence of metal impurities, harms the alloy’s electronic properties as no significant photoresponse was observed. This research has demonstrated the material properties that make the InxGa1-xN alloy attractive for multi-junction solar cells and the benefits/drawbacks of the plasma-enhanced evaporation deposition system. Future work is needed to overcome significant challenges relating to crystalline quality, compositional homogeneity and the optoelectronic properties of In-rich InxGa1-xN films in order to develop high-performance photovoltaic devices.

Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.