Room-Temperature 15% Efficient Mid-Infrared HgTe Colloidal Quantum Dot Photodiodes

Mid-infrared HgTe colloidal quantum dot photovoltaic devices previously achieved background-limited infrared photodetection at cryogenic temperatures but also decreased from 20 to 1% efficiency from 150 to 300 K. The reduced quantum efficiency was tentatively attributed to the carrier diffusion length being much shorter than the device thickness of ∼400 nm at room temperature. Here, the carrier diffusion length is measured and is found to peak at 215 nm at 200 K and decrease only to 180 nm at 295 K. It is therefore not the cause of the much larger reduced quantum efficiency. Instead, it is shown that the efficiency drops due to the series resistance. With the device size reduced to 50 by 50 μm, the room-temperature quantum efficiency reaches 10 and 15% for HgTe colloidal quantum dot devices with 2400 cm^-1 (4.2 μm) and 2675 cm^-1 (3.7 μm) cutoff, respectively. These small-area devices achieve background-limited photodetection at 150 K and a detectivity above 10⁹ Jones at room temperature with a cutoff at 2675 cm^-1 (3.7 μm).

Interband Cascade Photonic Integrated Circuits on Native III-V Chip

We describe how a midwave infrared photonic integrated circuit (PIC) that combines lasers, detectors, passive waveguides, and other optical elements may be constructed on the native GaSb substrate of an interband cascade laser (ICL) structure. The active and passive building blocks may be used, for example, to fabricate an on-chip chemical detection system with a passive sensing waveguide that evanescently couples to an ambient sample gas. A variety of highly compact architectures are described, some of which incorporate both the sensing waveguide and detector into a laser cavity defined by two high-reflectivity cleaved facets. We also describe an edge-emitting laser configuration that optimizes stability by minimizing parasitic feedback from external optical elements, and which can potentially operate with lower drive power than any mid-IR laser now available. While ICL-based PICs processed on GaSb serve to illustrate the various configurations, many of the proposed concepts apply equally to quantum-cascade-laser (QCL)-based PICs processed on InP, and PICs that integrate III-V lasers and detectors on silicon. With mature processing, it should become possible to mass-produce hundreds of individual PICs on the same chip which, when singulated, will realize chemical sensing by an extremely compact and inexpensive package.

Extremely Robust Remote-Target Detection Based on Carbon Dioxide-Double Spikes in Midwave Spectral Imaging

Infrared ship-target detection for sea surveillance from the coast is very challenging because of strong background clutter, such as cloud and sea glint. Conventional approaches utilize either spatial or temporal information to reduce false positives. This paper proposes a completely different approach, called carbon dioxide-double spike (CO₂-DS) detection in midwave spectral imaging. The proposed CO₂-DS is based on the spectral feature where a hot CO₂ emission band is broader than that which is absorbed by normal atmospheric CO₂, which generates CO₂-double spikes. A directional-mean subtraction filter (D-MSF) detects each CO₂ spike, and final targets are detected by joint analysis of both types of detection. The most important property of CO₂-DS detection is that it generates an extremely low number of false positive caused by background clutter. Only the hot CO₂ spike of a ship plume can penetrate atmosphere, and furthermore, there are only ship CO₂ plume signatures in the double spikes of different spectral bands. Experimental results using midwave Fourier transform infrared (FTIR) in a remote sea environment validate the extreme robustness of the proposed ship-target detection.

Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment

Remote measurements of thermal radiation are very important for analyzing the solar effect in various environments. This paper presents a novel real-time remote temperature estimation method by applying a deep learning-based regression method to midwave infrared hyperspectral images. A conventional remote temperature estimation using only one channel or multiple channels cannot provide a reliable temperature in dynamic weather environments because of the unknown atmospheric transmissivities. This paper solves the issue (real-time remote temperature measurement with high accuracy) with the proposed surface temperature-deep convolutional neural network (ST-DCNN) and a hyperspectral thermal camera (TELOPS HYPER-CAM MWE). The 27-layer ST-DCNN regressor can learn and predict the underlying temperatures from 75 spectral channels. Midwave infrared hyperspectral image data of a remote object were acquired three times a day (10:00, 13:00, 15:00) for 7 months to consider the dynamic weather variations. The experimental results validate the feasibility of the novel remote temperature estimation method in real-world dynamic environments. In addition, the thermal stealth properties of two types of paint were demonstrated by the proposed ST-DCNN as a real-world application.

Thermal Imaging with Plasmon Resonance Enhanced HgTe Colloidal Quantum Dot Photovoltaic Devices

Thermal imaging in the midwave infrared plays an important role for numerous applications. The key functionality is imaging devices in the atmospheric window between 3 and 5 μm, where disturbance from fog, dust, and other atmospheric influence could be avoided. Here, we demonstrate sensitive thermal imaging with HgTe colloidal quantum dot (CQD) photovoltaic detectors by integrating the HgTe CQDs with plasmonic structures. The responsivity at 5 μm is enhanced 2- to 3-fold over a wide range of operating temperatures from 295 to 85 K. A detectivity of 4 × 10¹¹ Jones is achieved at cryogenic temperature. The noise equivalent temperature difference is 14 mK at an acquisition rate of 1 kHz for a 200 μm pixel. Thermal images are captured with a single-pixel scanning imaging system.