The use of optical input/output (I/O) interconnects, in addition to electrical I/Os, is a promising approach for achieving high-bandwidth, chip-to-board communications required for future high-performance gigascale chip-based systems. While numerous efforts are underway to investigate the integration of optoelectronics and silicon microelectronics, virtually no work has been reported relating to testing of such chips. The objective of this research is to explore methods that enable wafer-level testing of gigascale chips having electrical and optical I/O interconnects. A major challenge in achieving this is to develop probe modules which would allow high-precision, temporary interconnection of a multitude of electrical and optical I/Os, in a chip-size area, to automated test equipment. A probe module would need to do this in a rapid, step-and-repeat manner across all the chips on the wafer. In this work, two candidate probe modules were devised, batch-fabricated on Si using microfabrication techniques, and successfully demonstrated. The first probe module consists of compliant electrical probes (10^3 probes/cm^2) fabricated alongside grating-in-waveguide optical probes. The second module consists of micro-opto-electro-mechanical-systems (MOEMS)-based microsocket probes (10^4 probes/cm^2) to interface a chip with polymer pillar-based electrical and optical I/Os. High-density through-wafer interconnects are an essential attribute in both probe substrates for transferring electrical and optical signals to the substrate back-side. Fabrication and characterization of metal-clad, metal-filled, and polymer-filled through-wafer interconnects as well as process integration with probe substrate fabrication are described and numerous possible redistribution schemes are explicated. Chips with optical and electrical I/Os are an emerging technology, and one that test engineers are likely to encounter in the near future. The contributions of this thesis are to help understand and address the issues relating to joint electrical and optical testing during manufacturing.

Indiana University-Purdue University Indianapolis (IUPUI) / Adaptive optics-optical coherence tomography (AO-OCT) is a non-invasive method of imaging the human retina in vivo. It can be used to visualize microscopic structures, making it incredibly useful for the early detection and diagnosis of retinal disease. The research group at Indiana University has a novel multi-camera AO-OCT system capable of 1 MHz acquisition rates. Until this point, a method has not existed to process data from such a novel system quickly and accurately enough on a CPU, a GPU, or one that can scale to multiple GPUs automatically in an efficient manner. This is a barrier to using a MHz AO-OCT system in a clinical environment. A novel approach to processing AO-OCT data from the unique multi-camera optics system is tested on multiple graphics processing units (GPUs) in parallel with one, two, and four camera combinations. The design and results demonstrate a scalable, reusable, extensible method of computing AO-OCT output. This approach can either achieve real time results with an AO-OCT system capable of 1 MHz acquisition rates or be scaled to a higher accuracy mode with a fast Fourier transform of 16,384 complex values.

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.