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An emerging device architecture for infrared imaging is the curvedfocal-plane array which benefits from several optical advantages over thetraditional flat design. However, the curving process introduces additionalstrain in the active region which must be taken into account. Type-IIsuperlattices, a promising alternative to traditional bulk materials for use ininfrared photodetectors, is a candidate material for use in these devices, butthe transport properties of these highly heterogeneous materials are notstraightforward and can be affected by different material conditions, such assuperlattice disorder and external strain. We present a comprehensive study ofthe internal QE calculated for a curved device that incorporates finite elementanalysis (FEA) modeling, nonequilibirium Green's functions (NEGF) calculations,and Gaussian Process (GP) regression. FEA is used for predicting the strainconfiguration throughout the active region induced by the curving procedure ofthe device. NEGF is used to calculate the vertical hole mobility for a selectset of strain configurations, from which the internal quantum efficiency of thedevice is approximated to predict performance under strained conditions. Thenthis data set is used to train a GP model that maps the quantum efficiency QEpredictions onto the spatial coordinates of the curved device, based on thestrain configuration predicted using FEA. This analysis is performed for idealand disordered SLs to understand both the fundamental and practical limitationsof the performance of these materials in curved devices.

A Machine-Learning-Accelerated Quantum Transport Study on the Effects of Superlattice Disorder and Strain in a Mid-wave Infrared Curved Sensor