Relative performance analysis of IR FPA technologies from the perspective of system level performance

The majority of high performance infrared systems today utilize FPAs composed of intrinsic direct band gap semiconductor photon detectors such as MCT or InSb. Quantum well detector technologies such as QWIPs, QDIPs, and SLS photodetectors are potentially lower cost alternatives to MCT and InSb, but the relative performance of these technologies has not been sufficiently high to allow widespread adoption outside of a handful of applications. While detectors are often evaluated using figures of merit such as NETD or D*, these metrics, which include many underlying aspects such as spectral quantum efficiency, dark current, well size, MTF, and array response uniformity, may be far removed from the performance metrics used to judge performance of a system in an operationally relevant scenario. True comparisons of performance for various detector technologies from the perspective of end-to-end system performance have rarely been conducted, especially considering the rapid progress of the newer quantum well technologies. System level models such as the US Army's Night Vision Integrated Performance Model (NV-IPM) can calculate image contrast and spatial frequency content using data from the target/background, intervening atmosphere, and system components. This paper includes results from a performance parameter sensitivity analysis using NV-IPM to determine the relative importance of various FPA performance parameters to the overall performance of a long range imaging system. Parameters included are: QE, dark current density, quantum well capacity, downstream readout noise, well fill, image frame rate, frame averaging, and residual fixed pattern noise. The state-of-the art for XBn, QWIP, and SLS detector technologies operating in the MWIR and LWIR bands will be surveyed to assess performance of quantum structures compared to ma and InSb. The intent is to provide a comprehensive assessment of quantum detector performance and to identify areas where increased research could provide the most benefit to overall system level performance. (C) 2017 Elsevier B.V. All rights reserved.