Bias-Tunable Quantum Well Infrared Photodetector
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Published:2024-03-20
Issue:6
Volume:14
Page:548
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ISSN:2079-4991
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Container-title:Nanomaterials
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language:en
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Short-container-title:Nanomaterials
Author:
Biswal Gyana1ORCID, Yakimov Michael1, Tokranov Vadim1, Sablon Kimberly2, Tulyakov Sergey3, Mitin Vladimir3, Oktyabrsky Serge1
Affiliation:
1. College of Nanotechnology, Science and Engineering, University at Albany, Albany, NY 12203, USA 2. Texas A&M University, College Station, TX 77843, USA 3. School of Engineering and Applied Sciences, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
Abstract
With the rapid advancement of Artificial Intelligence-driven object recognition, the development of cognitive tunable imaging sensors has become a critically important field. In this paper, we demonstrate an infrared (IR) sensor with spectral tunability controlled by the applied bias between the long-wave and mid-wave IR spectral regions. The sensor is a Quantum Well Infrared Photodetector (QWIP) containing asymmetrically doped double QWs where the external electric field alters the electron population in the wells and hence spectral responsivity. The design rules are obtained by calculating the electronic transition energies for symmetric and antisymmetric double-QW states using a Schrödinger–Poisson solver. The sensor is grown and characterized aiming detection in mid-wave (~5 µm) to long-wave IR (~8 µm) spectral ranges. The structure is grown using molecular beam epitaxy (MBE) and contains 25 periods of coupled double GaAs QWs and Al0.38Ga0.62As barriers. One of the QWs in the pair is modulation-doped to provide asymmetry in potential. The QWIPs are tested with blackbody radiation and FTIR down to 77 K. As a result, the ratio of the responsivities of the two bands at about 5.5 and 8 µm is controlled over an order of magnitude demonstrating tunability between MWIR and LWIR spectral regions. Separate experiments using parameterized image transformations of wideband LWIR imagery are performed to lay the framework for utilizing tunable QWIP sensors in object recognition applications.
Funder
United States Army Research Office United States Air Force Research Laboratory
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