IR sensors continue to be a powerful tool for a broad range of sensing applications including night vision, surveillance and other mission functions. Sensors are often exposed to challenging scenarios such as targeting under extreme conditions including detection under extremely high temperature and speed, such as in hypersonic applications. Thermal protection is vital for sensor performance under unfavorable conditions. Transparent ceramic is one of the window materials used against thermal impact due to its excellent optical transmission, transparency, and durability under extreme conditions. In this paper, we focus on investigating the behavior of Aluminum Oxynitride (AlON) optical ceramics at high temperatures. AlON has been reported to have transparency and transmission over 80% from the UV all the way to the mid-wave IR cutting off around 5μm. A 25.4mm × 25.4mm × 6mm square and 1.5mm × 1.5mm × 10.16mm AlON samples were heated up to ~907K. A 50W CO2 laser was used as a heating source for the material under test (MUT). Significant thermal distribution was measured using a long-wave IR thermal camera to observe the MUT surface. In addition, heating results show that there was severe thermal stress in the MUT. We are currently optimizing the optical beam dimensions and projection shape towards a sample in order to minimize the stress and heat towards 1273K. Finally, we validated our experimental results with thermo-optic simulations and modeling.
We show that a previously derived LCR model for a plasmonic waveguide can be generalized to a model for hyperbolic metamaterials (HMMs). An analysis of previous work and a generalization into a multilayer structure is presented. The physical significance and practical applications are discussed.
We explain the design of one dimensional Hyperbolic Metamaterials (HMM) using a genetic algorithm (GA) and provide sample applications including the realization of negative refraction. The design method is a powerful optimization approach to find the optimal performance of such structures, which “naturally” finds HMM structures that are globally optimized for specific applications. We explain how a fitness function can be incorporated into the GA for different metamaterial properties.