Shortwave Infrared Sensor Research Articles

Suppressing the Dark Current While Improving the Quantum Efficiency in Shortwave Infrared Organic Photodetectors Through Naphthalenediimide‐Based Interlayer

AbstractThe significance of shortwave infrared (SWIR) photodetectors spans across various applications. Nevertheless, the limited spectral response of silicon‐based photodetectors and the high cost associated with materials like germanium (Ge) have impeded the widespread adoption of SWIR sensors, particularly in the realm of consumer electronics. This study explores the transformative impact of incorporating a cross‐linkable naphthalenediimide (c‐NDI) as both an electron transporting layer and a hole blocking layer in organic photodetectors (OPDs). The introduction of c‐NDI as an interlayer leads to substantial enhancements in SWIR OPD performance, particularly in terms of reducing dark current and augmenting external quantum efficiency. These improvements are most notable in ultra‐narrow bandgap SWIR systems, where c‐NDI demonstrates superior hole‐blocking capabilities. Besides, OPDs with c‐NDI interlayers also exhibit exceptional stability over time when compared to OPDs based on zinc oxide interlayer, underscoring c‐NDI‘s versatility as an interlayer. Most importantly, when compared to a commercially available Ge photodetector, c‐NDI‐based OPD demonstrates competitive detectivity, achieving 2.67 × 1011 Jones at a wavelength of 1300 nm. This performance even surpasses that of the Ge photodetector, highlighting the substantial potential of OPDs for SWIR imaging applications.

Estimating Plant Nitrogen by Developing an Accurate Correlation between VNIR-Only Vegetation Indexes and the Normalized Difference Nitrogen Index

Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicators developed in the literature is the Normalized Difference Nitrogen Index (NDNI), which is based on the usage of the spectral bands of 1510 and 1680 nm from the Short-Wave Infrared (SWIR) region of the electromagnetic spectrum. However, the majority of remote sensing sensors, like cameras and/or satellites, do not have an SWIR sensor due to high costs. Many vegetation indexes, like NDVI, EVI and MNLI, have also been developed in the VNIR region to monitor the greenness and health of the crops. However, these indexes are not very well correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as the input and NDNI as the output, both of which are calculated from the same location. After training, an overall correlation of 0.93 was achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy.

62‐2: Novel TFT short‐wave infrared sensor based on colloidal quantum dot technology

We report a new TFT short‐wave infrared (SWIR) sensor based on colloidal quantum dot (CQD) technology. As the name suggests, its function is to detect and image the short‐wave infrared band. This is an industry‐leading TFT colloidal quantum dot SWIR sensor. The active layer is made of colloidal quantum dot material, and the detection band is 0.9~2.5um. The colloidal quantum dots are interconnected with the TFT reading circuit through spin coating, spraying, screen printing or scraping. The new TFT short‐wave infrared sensor of colloidal quantum dots gets rid of the limitation of the indium column flip‐chip bonding process of the traditional indium gallium arsenide CMOS short‐wave infrared focal plane sensor, and the preparation process is simple, the yield is higher, and the cost is low. A new TFT short‐wave infrared sensor based on colloidal quantum dots realizes a short‐wave infrared focal plane array and successfully captures short‐wave infrared images.

Target discrimination in the extended shortwave infrared band (2 to 2.5μm) compared with visible, near-infrared, and SWIR in degraded visual environments

Long-range target identification is well studied in the visible (Vis) and near-infrared (NIR) bands and more recently in the shortwave infrared (SWIR). The longer wavelength of SWIR (1 to 1.7 μm) improves target detection for both long ranges and under challenging atmospheric conditions because it is less limited by scattering and absorption in the atmosphere. For these reasons, SWIR sensors are proliferating on military platforms. The extended shortwave infrared (eSWIR) band spanning from 2 to 2.5 μm is not typically limited by diffraction, and as a result, the band benefits target acquisition both at long ranges and for degraded visual environments (DVEs). Theoretical and experimental data compare eSWIR with Vis, NIR, and SWIR for atmospheric transmission, reflectivity, illumination, and sensor resolution and sensitivity. The experimental setup includes two testbeds, each with four cameras. The first is a wide field of view (FOV) testbed matching FOV at 20 deg for each camera. The second is a narrow FOV telescope testbed to match instantaneous FOV for consistent resolution across all four bands at long ranges. Both the theory and experiment demonstrate advantages of using eSWIR for long-range target identification under DVEs.

A preliminary investigation into the automatic detection of diseased sheep organs using hyperspectral imaging sensors

The post-mortem inspection process of livestock viscera at abattoirs is expensive and laborious, but it is essential for the detection and condemnation of defective edible organs and carcases due to food safety issues. Lesions in hearts, kidneys, livers, lungs, and their associated lymph nodes are amongst the most common offal defects found in abattoirs. Visible (VIS) and short-wave infrared (SWIR) hyperspectral imaging implemented in a multisensory platform were used to differentiate between sheep parenchymatous organs passed as fit (Healthy, n = 42) or not fit (Diseased, n = 47) for human consumption. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to classify organs as healthy or diseased in heart (n = 28), kidney (n = 15), liver (n = 24), and lung (n = 22). PLS-DA produced equal or greater classification accuracy and sensitivity than RF for all organs except for lung when the VIS sensor was used (means 84.4% and 78.3%, respectively). Livers and hearts (86.9%) showed higher accuracy than lungs and kidneys (75.9%). Limited differences occurred between VIS and SWIR sensors, although a single sensor tended to be more accurate than a combination of both. SWIR outperformed VIS in accuracy across all organs (84.8% vs. 76.3%), and the combination of VIS and SWIR was also accurate (83.0%). The use of hyperspectral imaging is an attractive proposition for the meat processing industry as a non-invasive imaging technology to detect defects in offal, and it can also provide automatic detection, saving time and labour costs.

Automated classification of heat sources detected using SWIR remote sensing

The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale.

Comparative long-time visible and shortwave infrared night illumination measurements.

During the last decade, the shortwave infrared (SWIR) spectral range has become a contender for night vision applications. For this application, it is necessary to compare SWIR and visible (VIS) sensor performance, which requires knowledge of illumination in these two ranges. Fraunhofer Institute of Optronics, System Technologies, and Image Exploitation initiated comparative long-time assessment of VIS and SWIR illumination including cloud coverage in 2019. A first analysis based on three months of operation is presented, showing preliminary results of the VIS/SWIR comparison, highlighting the problems of artificial light influence, and connecting the illumination to environmental effects.

Uncooled Short-Wave Infrared Sensor Based on PbS Quantum Dots Using ZnO NPs.

Shortwave infrared (SWIR) sensors have attracted interest due to their usefulness in applications like military and medical equipment. SWIR sensors based on various materials are currently being studied. However, most SWIR detectors need additional optical filters and cooling systems to detect specific wavelengths. In order to overcome these limitations, we proposed a solution processed SWIR sensor that can operate at room temperature using lead chloride (PbS) QDs as a photoactive layer. Additionally, we adapted zinc oxide (ZnO) nanoparticles (NPs) as an electron transport layer (ETL) to improve the sensitivity of a PbS SWIR sensor. In this study, PbS SWIR sensors with and without a ZnO NPs layer were fabricated and their current–voltage (I–V) characteristics were measured. The on/off ratio of the PbS SWIR sensor with ZnO NPs was 2.87 times higher than that of the PbS SWIR sensor without ZnO NPs at the maximum current difference. The PbS SWIR sensor with ZnO NPs showed more stable current characteristics than that without ZnO NPs because of the ZnO NPs’ high electron mobility and proper lowest unoccupied molecular orbital (LUMO) level.

Design and fabrication of large-area tunable MOEMS-based shortwave infrared Fabry-Pérot filters

In this letter, the authors report the micro-opto-electro-mechanical system-based large-area tunable Fabry-Pérot filters (FPF) with an aperture size of up to 300 μm. The sputtered zinc oxide layer is used as the sacrificial layer during the releasing process. The fabricated FPFs exhibit a tuning range of 188 nm in the near-infrared wavelength range when the voltage is biased from 0 to 7.5 V. The stiffness of supporting arms of the electrostatic actuator is calculated to be 2799 N/m from the analyses of capacitance-voltage measurements. The designed FPFs can be used in shortwave infrared (SWIR) spectral analyses with suitable SWIR sensors.

Short-Wave Infrared Sensor by the Photothermal Effect of Colloidal Gold Nanorods.

Photodetection in the short-wave infrared (SWIR) spectrum is a challenging task achieved often by costly low bandgap compound semiconductors involving highly toxic elements. In this work, an alternative low-cost approach is reported for SWIR sensors that rely on the plasmonic-induced photothermal effect of solution-processed colloidal gold nanorods (Au NRs). A series of uniform solution-processed Au NRs of various aspect ratios are prepared exhibiting a strong and well-defined longitudinal localized surface plasmon resonance (L-LSPR) maximum from 900 nm to 1.3 µm. A hybrid device structure is fabricated by applying Au NRs on the surface of a thermistor. Under a monochromatic illumination, hybrid Au-NR/thermistor devices exhibit a clear photoresponse in the form of photoinduced resistance drop in the wavelength window from 1.0 to 1.8 µm. The photoresponsivity of such hybrid devices reaches a maximum value of 4.44 × 107 Ω W-1 at λ = 1.4 µm (intensity = 0.28 mW cm-2 ), a wavelength in agreement with the L-LSPR of the Au NRs applied. Colloidal Au NRs, capable to perform fast conversion between photon absorption and thermal energy, thus open an interesting avenue for alternative low-cost SWIR photodetection.

Long-Term Vicarious Calibration of GOSAT Short-Wave Sensors: Techniques for Error Reduction and New Estimates of Radiometric Degradation Factors

This work describes the radiometric calibration of the short-wave infrared (SWIR) bands of two instruments aboard the Greenhouse gases Observing SATellite (GOSAT), the Thermal And Near infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). Four vicarious calibration campaigns (VCCs) have been performed annually since June 2009 at Railroad Valley, NV, USA, to estimate changes in the radiometric response of both sensors. While the 2009 campaign ( VCC2009) indicated significant initial degradation in the sensors compared to the prelaunch values, the results presented here show that the stability of the sensors has improved with time. The largest changes were seen in the 0.76 μm oxygen A-band for TANSO-FTS and in the 0.380 and 0.674 μm bands for TANSO-CAI. This paper describes techniques used to optimize the vicarious calibration of the GOSAT SWIR sensors. We discuss error reductions, relative to previous work, achieved by using higher quality and more comprehensive in situ measurements and proper selection of reference remote sensing products from the Moderate Resolution Imaging Spectroradiometer used in radiative transfer calculations to model top-of-the-atmosphere radiances. In addition, we present new estimates of TANSO-FTS radiometric degradation factors derived by combining the new vicarious calibration results with the time-dependent model provided by Yoshida (2012), which is based on analysis of on-board solar diffuser data. We conclude that this combined model provides a robust correction for TANSO-FTS Level 1B spectra. A detailed error budget for TANSO-FTS vicarious calibration is also provided.

Change detection of soils under small-scale laboratory conditions using imaging spectroscopy sensors

Change detection techniques aim to identify changes between two or more images taken at different times. In this paper, we explore the capabilities of identifying changes in an unsupervised manner between different soil types using two laboratory HySpex imaging spectroscopy sensors in the visible near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges. The experiment was carried under controlled laboratory conditions with the same lighting and no atmospheric distortions. The 69 selected soil samples covered the arid and semiarid climate zones of Israel. The well-known change vector analysis technique was used to generate the difference image, and several thresholding methods were tested to generate the final binary change map. The performance capabilities of the VNIR, SWIR and combined VNIR–SWIR sensors were examined. Our study demonstrates that changes in different soil types can be identified using imaging spectroscopy sensors; the SWIR sensor generated better change detection capabilities than the VNIR sensor, and the combination of the two sensors did not outperform the SWIR sensor alone. Results showed that it is important to combine a spectral domain thresholding approach with a spatial domain thresholding approach. The benefit of combining these approaches is a low false-alarm rate with a relatively high probability of detection. Although the change experiment was conducted under almost perfect conditions without any atmospheric or lighting differences, the change detection techniques did not detect all soil type changes and changes between spectrally similar soils remain undetected. The results of this study can be further extended to other spatial scales and can provide a foundation for soil change detection using upcoming imaging spectroscopy satellite platforms that acquire spatial–spectral–temporal information.