Critical look at AlGaAs/GaAs multiple-quantum-well infrared detectors for thermal imaging applications

Details Critical look at AlGaAs/GaAs multiple-quantum-well infrared detectors for thermal imaging applications Critical look at AlGaAs/GaAs multiple-quantum-well infrared detectors for thermal imaging applications

Nov 1991

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1991spie.1541...24a&link_type=abstract

Proc. SPIE Vol. 1541, p. 24-37, Infrared Sensors: Detectors, Electronics, and Signal Processing, T. S. Jayadev; Ed.

Physics

Optics

Scientific paper

In the last five years, photoconductive infrared detectors using intersubband transitions in AlGaAs/GaAs multiple-quantum-well structures have attained more than adequate single-pixel detectivity for thermal imaging. Because AlGaAs/GaAs materials technology can potentially deliver a high yield of extremely uniform large-area detector arrays, lower-cost LWIR thermal imagers may indeed be possible. In this paper, we ask whether arrays of these detectors are suitable for staring LWIR thermal imagers. At first look they do not appear particularly attractive because of excessive dark-current pattern noise. For practical operating temperatures above 68 K (approximately the lowest temperature for single-stage cryocoolers), total dark- current variation must be reduced by more than an order of magnitude to attain a practical noise-equivalent temperature difference (NETD). Reducing dark current is therefore the most effective way to improve staring array performance. Several other measures can also improve NETD. Optimizing bias voltage for minimum NETD rather than for maximum detectivity can improve NETD by a factor of 2. Integrated micro-optics can reduce detector area, reducing pattern noise by reducing dark current. A multiple-quantum-well chopper can be used to improve correction for dark-current nonuniformity. We conclude that a practical thermal imager can probably be made with AlGaAs/GaAs MQW technology, but success will require careful modeling of all relevant factors.

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