Antimonide Based Infrared Detectors and Focal Plane Arrays for NASA Applications

Mid infrared imaging (3-25 micrometers) has been an important technological tool for the past 60 years since the first report of infrared detectors in 1950s. There has been a dramatic progress in the development of antimonide based detectors and low power electronic devices in the past decade with new materials like InAsSb, InAs/GaSb superlattices and InAs/InAsSb superlattices demonstrating very good performance. One of the unique aspects of the 6.1A family of semiconductors (InAs, GaSb and AlSb) is the ability to engineer the bandstructure to obtain designer band-offsets. Their group recently moved to The Ohio State University where they are setting up new capability for investigation of the antimonide based materials for infrared detectors and focal plane arrays. Their two areas of focus include developing a “materials to manufacturing” capability for realization of these sensors and exploration of novel application for the infrared sensors and imagers. (

In this talk, Dr. Krishna will describe some of the material science and device physics of the antimonide systems. The use of “unipolar barrier engineering” to realize high performance infrared detectors and focal plane arrays will be discussed. He will define the current status of the technology and what are the current scientific and technical challenges. He will discuss some new ideas such as use of superlattices for single carrier impact ionization to realize low noise avalanche photodiodes and (b) using dielectric resonators to increase the signal to noise ratio of infrared detectors. He will also explore the possibility of realizing next generation infrared imaging systems. Using the concept of a bio-inspired infrared retina, I will make a case for an enhanced functionality in the pixel. The key idea is to engineer the pixel such that it not only has the ability to sense multimodal data such as color, polarization, dynamic range and phase but also the intelligence to transmit a reduced data set to the central processing unit.

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