Continuous time-varying biasing approach for spectrally tunable infrared detectors

In a recently demonstrated algorithmic spectral-tuning technique by Jang et al. [Opt. Express 19, 19454-19472, (2011)], the reconstruction of an object’s emissivity at an arbitrarily specified spectral window of interest in the long-wave infrared region was achieved. The technique relied upon forming a weighted superposition of a series of photocurrents from a quantum dots-in-a-well (DWELL) photodetector operated at discrete static biases that were applied serially. Here, the technique is generalized such that a continuously varying biasing voltage is employed over an extended acquisition time, in place using a series of fixed biases over each sub-acquisition time, which totally eliminates the need for the post-processing step comprising the weighted superposition of the discrete photocurrents. To enable this capability, an algorithm is developed for designing the time-varying bias for an arbitrary spectral-sensing window of interest. Since continuous-time biasing can be implemented within the readout circuit of a focal-plane array, this generalization would pave the way for the implementation of the algorithmic spectral tuning in focal-plane arrays within in each frame time without the need for on-sensor multiplications and additions. The technique is validated by means of simulations in the context of spectrometry and object classification while using experimental data for the DWELL under realistic signal-to-noise ratios

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

Multispectral Classification With Bias-Tunable Quantum Dots-in-a-Well Focal Plane Arrays

Mid-wave and long-wave infrared (IR) quantum-dots-in-a-well (DWELL) focal plane arrays (FPAs) are promising technology for multispectral (MS) imaging and sensing. The DWELL structure design provides the detector with a unique property that allows the spectral response of the detector to be continuously, albeit coarsely, tuned with the applied bias. In this paper, a MS classification capability of the DWELL FPA is demonstrated. The approach is based upon: 1) imaging an object repeatedly using a sequence of bias voltages in the tuning range of the FPA and then 2) applying a classification algorithm to the totality of readouts, over multiple biases, at each pixel to identify the “class” of the material. The approach is validated for two classification problems: separation among different combinations of three IR filters and discrimination between rocks. This work is the first demonstration of the MS classification capability of the DWELL FPA

Demonstration of a Bias Tunable Quantum Dots-in-a-well Focal Plane Array

Infrared detectors based on quantum wells and quantum dots have attracted a lot of attention in the past few years. Our previous research has reported on the development of the first generation of quantum dots-in-a-well (DWELL) focal plane arrays, which are based on InAs quantum dots embedded in an InGaAs well having GaAs barriers. This focal plane array has successfully generated a two-color imagery in the mid-wave infrared (i.e. 3–5 μm) and the long-wave infrared (i.e. 8–12 μm) at a fixed bias voltage. Recently, the DWELL device has been further modified by embedding InAs quantum dots in InGaAs and GaAs double wells with AlGaAs barriers, leading to a less strained InAs/InGaAs/GaAs/AlGaAs heterostructure. This is expected to improve the operating temperature while maintaining a low dark current level. This paper examines 320 × 256 double DWELL based focal plane arrays that have been fabricated and hybridized with an Indigo 9705 read-out integrated circuit using Indium-bump (flip-chip) technology. The spectral tunability is quantified by examining images and determining the transmittance ratio (equivalent to the photocurrent ratio) between mid-wave and long-way infrared filter targets. Calculations were performed for a bias range from 0.3 to 1.0 V. The results demonstrate that the mid-wave transmittance dominates at these low bias voltages, and the transmittance ratio continuously varies over different applied biases. Additionally, radiometric characterization, including array uniformity and measured noise equivalent temperature difference for the double DWELL devices is computed and compared to the same results from the original first generation DWELL. Finally, higher temperature operation is explored. Overall, the double DWELL devices had lower noise equivalent temperature difference and higher uniformity, and worked at higher temperature (70 K and 80 K) than the first generation DWELL device

Data Compressive Paradigm for Multispectral Sensing Using Tunable DWELL Mid-infrared Detectors

While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELL’s bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELL’s non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (\u3e30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated

Demonstration of Bias-Controlled Algorithmic Tuning of Quantum Dots in a Well (DWELL) MidIR Detectors

The quantum-confined Stark effect in intersublevel transitions present in quantum-dots-in-a-well (DWELL) detectors gives rise to a midIR spectral response that is dependent upon the detector\u27s operational bias. The spectral responses resulting from different biases exhibit spectral shifts, albeit with significant spectral overlap. A postprocessing algorithm was developed by Sakoglu that exploited this bias-dependent spectral diversity to predict the continuous and arbitrary tunability of the DWELL detector within certain limits. This paper focuses on the experimental demonstration of the DWELL-based spectral tuning algorithm. It is shown experimentally that it is possible to reconstruct the spectral content of a target electronically without using any dispersive optical elements for tuning, thereby demonstrating a DWELL-based algorithmic spectrometer. The effects of dark current, detector temperature, and bias selection on the tuning capability are also investigated experimentally

Impact of Land Cover and Leaf Area Index on BVOC Emissions over the Korean Peninsula

Biogenic volatile organic compound (BVOCs) emissions are the largest VOC emission source globally, and are precursors to ozone and secondary organic aerosols, both of which are strong, short-lived climate pollutants. BVOC emissions are usually estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), which requires Plant Functional Types (PFTs) and Leaf Area Indexes (LAIs) as inputs. Herein, the effects of refined input data on regional BVOC emission estimates are analyzed. For LAIs, lower resolution MODerate-resolution Imaging Spectroradiometer (MODIS), and higher spatio-temporal resolution Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) LAI were generated. For PFTs, local land cover maps were developed, in addition to MODIS PFT. In South Korea, annual emissions of isoprene and monoterpenes in 2015 were estimated as 384 and 160 Gg/year, respectively, using STARFM LAI and Local PFT (Case 4). For North Korea, 340 Gg/year isoprene and 72 Gg/year monoterpenes emissions were estimated using STARFM LAI and MODIS PFT. These estimates were 14–110% higher than when using MODIS LAI and MODIS PFT (Case 1). Inter-comparison with satellite-based inverse isoprene emission estimates from GlobEmission shows 32% (North Korea) to 34% (South Korea) overestimation in bottom-up data. Our new vegetation inputs improve MEGAN performance and resulting BVOC emission estimations. Performance of Weather Research and Forecasting (WRF) meteorological modeling requires improvement, especially for solar radiation, to avoid overestimation of isoprene emissions

Development of the Northeast Asia Emission Inventory Using the CREATE Emissions Inventory Framework

Due to its rapid economic and population growth, Northeast Asia has emerged as the most emitting region in the world, contributing significantly to global air pollution. To address this issue, our study aimed to develop and enhance the emission inventory for Northeast Asia, building upon the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE) framework previously developed. The primary objective of this research was to provide a comprehensive and up-to-date emission inventory for the region, which plays a crucial role in understanding air pollution processes and forming effective policy directions. To achieve this, we integrated the existing CREATE emission framework with the latest information on activities, emission factors, atmospheric policies, and reduction efficiencies specific to Northeast Asia. For activity data, we utilized official national data from Korea and the most recent energy statistical data provided by international organizations. Our focus was on eight significant air pollutants: CO, SO2, NOx, NMVOC, NH3, PM2.5, and PM10. The developed inventory is designed to be efficiently integrated with air quality model processing systems like SMOKE, KU-EPS, and others, facilitating further analysis and research in this critical area. This new CREATE inventory has been used to compile project-based regional emission inventories, such as KORUS-AQ, LTP, and SIJAQ. In this presentation, we will present the enhancements made to the CREATE emissions framework and present the outcomes of the emissions achieved through the updated inventory

Extracardiac Fontan with T-shape conduit in non-confluent pulmonary arteries

A 34 months-old male patient with double inlet right ventricle with nonconfluent pulmonary arteries who underwent successful extracardiac fenestarated Fontan procedure using pre-designed T-shaped PTFE vascular graft after multi-step rehabilitation of the diminutive hilar pulmonary arteries. At first we performed 6 mm confluent pulmonary artery vascular graft implantation with 4 mm BT shunt at patient's 4 weeks old. At 9 months of patient, we upsized the confluent pulmonary arterial graft to 8 mm with bidirectional cavopulmonary connection, and, at 34 months, we performed extracardiac conduit Fontan procedure with pre-designed T-shape conduit including the confluent pulmonary arterial portion at last. Patient shows excellent functional status and development