Predictive modeling of infrared detectors and material systems
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Detectors sensitive to thermal and reflected infrared radiation are widely used for night-vision, communications, thermography, and object tracking among other military, industrial, and commercial applications. System requirements for the next generation of ultra-high-performance infrared detectors call for increased functionality such as large formats (> 4K HD) with wide field-of-view, multispectral sensitivity, and on-chip processing. Due to the low yield of infrared material processing, the development of these next-generation technologies has become prohibitively costly and time consuming. In this work, it will be shown that physics-based numerical models can be applied to predictively simulate infrared detector arrays of current technological interest. The models can be used to a priori estimate detector characteristics, intelligently design detector architectures, and assist in the analysis and interpretation of existing systems. This dissertation develops a multi-scale simulation model which evaluates the physics of infrared systems from the atomic (material properties and electronic structure) to systems level (modulation transfer function, dense array effects). The framework is used to determine the electronic structure of several infrared materials, optimize the design of a two-color back-to-back HgCdTe photodiode, investigate a predicted failure mechanism for next-generation arrays, and predict the systems-level measurables of a number of detector architectures.