This project focused on developing a novel, scalable, and economic growth technique for bulk gallium nitride (GaN), a critical material for next-generation high-temperature power electronics. Large area, high-quality bulk GaN is required as a substrate material in order to grow highly efficient bipolar transistors for inverters and power conditioning. Attempting to grow GaN in bulk by traditional precipitation methods forces extreme thermodynamic and kinetic conditions, putting these techniques at the extremes of experimental science, which is unsuitable for large-area, cost-effective substrate growth. The Electrochemical Solution Growth (ESG) technique is a novel concept that addresses these issues in a unique way, and was developed at Sandia National Laboratories (SNL), in part under this program. The crucial step in demonstrating the technique’s feasibility was to deposit high-quality GaN on a seed crystal. The bulk of SNL’s activities were focused on developing conditions for deposition of GaN on a seed crystal (a thin film of GaN grown by metal organic chemical vapor phase deposition (MOCVD) on c-axis oriented sapphire) in a molten salt electrolyte solution using a rotating disk reactor (RDR) ESG apparatus. This project was actively funded from FY08 to FY12 by the Energy Storage Program and GaN Initiative for Grid Applications (GIGA) program of the Office of Electricity Delivery and Energy Reliability (OE) in the U.S. Department of Energy (DOE). Some activities focused on silicon doping of GaN occurred in FY13 but only through the use of carryover funds.
Multi-objective optimization is used to find a nondominated set of solutions for two conflicting performance metrics or objective functions. These functions are dependent variables in the system, controlled by a set of independent variables called decision variables. The decision variables represent the inputs to the problem, chosen by the system designer, and are values listed in the solution set. In this study, a multi-objective genetic algorithm compared insulated-gate bipolar transistor (IGBT) failure rate to filter and cooling system costs. This study demonstrated the use of multi-objective optimization for energy storage systems. IGBT failure rate was compared to its associated filter and cooling-system costs as part of the DC-AC inverter power electronics system in a battery energy storage system (BESS). The independent or decision variables were determined to be switching frequency and thermal resistance of a heat sink, Rsink. The final results indicated that high values of switching frequency increased the effects of Rsink. Future work will add additional objective functions and decision variables to the study to optimize additional components in the power electronics system and BESS.