Shortwave Infrared Research Articles

Efficient Infrared-Detecting Organic Semiconductors Featuring a Tetraheterocyclic Core with Reduced Ionization Potential.

Infrared organic semiconductors are crucial in organic optoelectronics, yet high-performance materials with photoresponse beyond 1.1 μm (the limit of crystalline silicon) remain scarce due to the limit of building blocks including strong electron-donating units. Here, we report an asymmetric tetraheterocycle (TPCT) with a reduced ionization potential of 6.18 eV relative to those reported dithiophene-based electron-donating blocks, and TPCT-2F and TPCTO-2F constructed with TPCT as the core exhibit absorption onset up to 1 μm and 1.4 μm, respectively. Especially, TPCTO-2F possesses a narrow bandgap of 1.00 eV and displays a small Urbach energy of 22.0 meV comparable to or even lower than those of some typical inorganic short-wave infrared (SWIR) semiconductors (13-44 meV). The organic photodetectors (OPDs) based on TPCT-2F achieve a peak detectivity (D*) of 2.2×1013 Jones at 810 nm under zero bias, among the highest values for reported OPDs and on par with commercial silicon photodetectors. Impressively, TPCTO-2F-based OPDs demonstrate a wide response from 0.3 to 1.4 μm and high D* comparable to germanium photodetector at wavelengths <1.3 μm with a maximum D* of 2.3×1011 Jones at 1.06 μm in SWIR region.

Tracking Water Transport with Short-Wave Infrared: Kinetic Phase Diagrams, Dissolution, and Drying.

Short-wave infrared (SWIR) imaging has been extensively used in defense applications but remains underutilized in the study of soft materials and the broader consumer product industry. Water molecules absorb around ∼1450 nm, making moisture-rich objects appear black, whereas surfactants and other common molecules in consumer products do not absorb and provide a good contrast. This experimental study showcases the varied capabilities of SWIR imaging in tracking water transport in soft material systems by analyzing dissolution dynamics, tracking phase transitions (when combined with cross-polarized optical imaging), and monitoring drying kinetics in the surfactant and polymer solutions. The dynamic phase evolution to equilibria of a binary aqueous solution of a nonionic surfactant hexaethylene glycol monododecyl ether (C12E6) is presented. The influence of confined hydration in dynamic-diffusive interfacial transport capillaries was investigated by tracking the micellar to hexagonal phase transition concentration (C*). The effects of varying concentrations of an industrially relevant additive─monovalent common salt (NaCl) on the radial (2D) dissolution of lamellar-structured concentrated sodium lauryl ether sulfate (70 wt % SLE1S) pastes was studied. An equation was developed to estimate the radial dissolution coefficients based on total dissolution time and surfactant concentrations in the sample and solvent. Water loss was investigated by tracking the drying of aqueous poly(vinyl) alcohol films. In situ monitoring of drying kinetics is used to draw correlations between the solution viscosity and drying time. SWIR imaging has already revealed previously inaccessible insights into surfactant hydration and holds the potential to become a turnkey method in tracking water transport, enabling better quality control and product stability analysis.

Shortwave infrared fusion for snow surface traversability mapping

Estimating the mechanical properties of snow from imagery is an essential part of over-snow vehicle autonomy. However, snow surfaces that differ widely in strength, traction, and motion resistance tend to appear a uniform bright white in visible or broadband infrared imagery, and it is difficult to determine where an oversnow vehicle should operate from imagery alone. In this work we determine the optimal fusion of filtered broadband shortwave infrared (SWIR) imagery to separate snow types with different mechanical properties by appearance. We demonstrate vastly increased discrimination skill in distinguishing snow types using a small number of SWIR cameras in both field and laboratory settings, and also identify sources of environmental context that can improve lookahead sensing for oversnow vehicles. Overall, we show that a small set of inexpensive SWIR filters is a powerful tool for over-snow autonomy and motion planning.

End-to-end hybrid infrared imaging system design with thermal analysis

The hybrid refractive-diffractive optical system exhibits strong capabilities in achromatic and athermal imaging, as well as in information encoding. This paper presents a novel end-to-end design framework for refractive-diffractive hybrid optical imaging systems. Utilizing a differential hybrid ray-tracing model, the framework simultaneously optimizes optical and neural network parameters. It allows for the design of diffractive optical elements (DOE) on aspheric substrates, enhancing flexibility and enabling applications in infrared optics. The integrated thermal analysis facilitates the development of athermal hybrid optical systems by combining them with an advanced restoration network. When applied to a single-lens short-wave infrared (SWIR) system, this approach outperforms traditional discrete design methods in both simulations and experiments, demonstrating its significant potential for future optical applications.

Infrared signature of aero-engine exhaust plume’s potential core and aircraft surface from direct bottom view

Abstract Low-flying aircraft are susceptible to attacks by ground-launched infrared (IR)-guided man portable air defence system (MANPADS) and surface-to-air missiles (SAM). When seen from direct below, a dual band sensor can lock on to either exhaust plume or aircraft surfaces. Based on the magnitude of the IR signature, the missile can use any one source for the terminal guidance. In this study, the IR signature of the aircraft surface and potential plume core is analysed and compared from direct bottom view in different IR bands. In the Long Wave Infrared (LWIR) band, the surface emission is higher and in the Medium Wave Infrared (MWIR) band the plume emission is higher. The plume (MWIR) emission is higher than the surface (LWIR) emission for low Mach numbers, but as the Mach number increases the plume (MWIR) to surface (LWIR) emission ratio decreases, and at supersonic Mach numbers the surface LWIR signature is higher than the plume MWIR signature. The plume MWIR to surface LWIR ratio further depends on the engine power, altitude of operation and the emissivity of the aircraft surface. In the reheat mode, plume MWIR emission is always higher than the surface LWIR emission. The dual band IR detector can be a combination of short wave infrared (SWIR)-MWIR, SWIR-LWIR, and the MWIR-LWIR band. The MWIR-LWIR dual band combination is the best suited combination of IR windows for a dual band IR sensor/detector for aircraft application.

Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques

Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (RecallM and AccuracyM equal to 1.00), followed by PCA-KNN (RecallM of 0.98–1.00 and AccuracyM equal to 1.00). Poorer performances were obtained by PLS-DA (RecallM of 0.68–0.72 and AccuracyM equal to 0.95) and PCA-DA (RecallM of 0.66–0.70 and AccuracyM equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies.

Generation of guide star catalog for the all-time three-FOV star sensor applied on airborne platforms

The guide star catalog plays a crucial role in the star sensor, impacting the system’s star identification speed and attitude measurement accuracy. Currently, the generation method for the guide star catalog is well developed for star sensors used on satellites. However, the inter-FOV guide star catalog for the all-time three-FOV star sensor still faces challenges such as excessive storage requirements, non-uniform distribution of guide stars, and incompleteness. Aiming at these problems, this paper proposes a generation method for the inter-FOV guide star catalog. Initially, a method for estimating instrument magnitudes in the short-wave infrared (SWIR) band is proposed and validated. Then, a theoretical model of the all-time three-FOV star sensor is established, and its extreme detection magnitudes are analyzed. Subsequently, the guide stars are initially selected using the improved spherical spiral method (ISSM) based on their uniformity and brightness, followed by the combination of the generalized regression neural network (GRNN) and the Monte Carlo method to generate the guide star catalog. Finally, it is verified by simulation experiments that the proposed method improves the uniformity and completeness while reducing the storage capacity.

Shortwave Infrared Spectroscopy and Geochemical Characteristics of White Mica-Group Minerals in the Sinongduo Low-Sulfide Epithermal Deposit, Tibet, China

Shortwave Infrared Spectroscopy and Geochemical Characteristics of White Mica-Group Minerals in the Sinongduo Low-Sulfide Epithermal Deposit, Tibet, China

Photodetectors of the Short-Wave IR Spectrum Range, Intended for Space Monitoring

Photodetectors of the Short-Wave IR Spectrum Range, Intended for Space Monitoring

Remote Mapping of Bedrock for Future Cosmogenic Nuclide Exposure Dating Studies in Unvisited Areas of Antarctica

Cosmogenic nuclide exposure dating is an important technique for reconstructing glacial histories. Many of the most commonly applied cosmogenic nuclides are extracted from the mineral quartz, meaning sampling of felsic (silica-rich) rock is often preferred to sampling of mafic (silica-poor) rock for exposure dating studies. Fieldwork in remote regions such as Antarctica is subject to time constraints and considerable logistical challenges, making efficient sample recovery critical to successful research efforts. Remote sensing offers an effective way to map the geology of large areas prior to fieldwork and expedite the sampling process. In this study, we assess the viability of multispectral remote sensing to distinguish felsic from mafic rock outcrops at visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths using both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and very high-resolution Worldview-3 (WV-3) imagery. We applied a combination of spectral mapping and ground truth from spectral measurements of 17 rock samples from Mount Murphy in the Amundsen Sea sector of West Antarctica. Using this approach, we identified four dominant rock types which we used as a basis for felsic–mafic differentiation: felsic granites and gneisses, and mafic basalts and fragmental hydrovolcanic rocks. Supervised classification results indicate WV-3 performs well at differentiating felsic and mafic rock types and that ASTER, while coarser, could also achieve satisfactory results and be used in concert with more targeted WV-3 image acquisitions. Finally, we present a revised felsic–mafic geological map for Mt Murphy. Overall, our results highlight the potential of spectral mapping for preliminary reconnaissance when planning future cosmogenic nuclide sampling campaigns in remote, unvisited areas of the polar regions.

Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing

The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing. Replacing optical probes with contactless short-wave infrared (SWIR) hyperspectral cameras allows efficient collection of thousands of absorption signals in a handful of images. This high repetition allows for effective denoising of each spectrum, so interpretable linear models can quantify metabolites. To illustrate, an interpretable linear model called L-SLR is trained using small datasets obtained with a SWIR HSI camera to quantify fructose, viable cell density (VCD), glucose, and lactate. The performance of this model is also compared to other existing linear models, namely Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF). Using only 50% of the dataset for training, reasonable test performance of mean absolute error (MAE) and correlations (r2) are achieved for glucose (r2 = 0.88, MAE = 37 mg/dL), lactate (r2 = 0.93, MAE = 15.08 mg/dL), and VCD (r2 = 0.81, MAE = 8.6 × 105 cells/mL). Further, these models are also able to handle quantification of a metabolite like fructose in the presence of high background concentration of similar metabolite with almost identical chemical interactions in water like glucose. The model achieves reasonable quantification performance for large fructose level (100–1000 mg/dL) quantification (r2 = 0.92, MAE = 25.1 mg/dL) and small fructose level (< 60 mg/dL) concentrations (r2 = 0.85, MAE = 4.97 mg/dL) in complex media like Fetal Bovine Serum (FBS). Finally, the model provides sparse interpretable weight matrices that hint at the underlying solution changes that correlate to each cell parameter prediction.

The Short-Wave Infrared (SWIR) Spectral Exploration Identification and Indicative Significance of the Yixingzhai Gold Deposit, Shanxi Province

The Yixingzhai gold deposit is the largest gold deposit in Shanxi Province and develops three types of mineralization: porphyry, quartz vein, and breccia. Spectral characteristic parameters of muscovite are studied by short-wave infrared (SWIR) spectral, and the exploration significance is discussed. The Al-OH wavelength of muscovite associated with porphyry mineralization gradually becomes shorter from the periphery (&gt;2206 nm) to the center (2201–2205 nm), and the crystallinity (&gt;2.6) gradually increases. In quartz vein mineralization, the wavelength gradually increases from the periphery (&lt;2203 nm) to the center (2210–2211 nm), while the crystallinity does not change significantly and in a small value (&lt;1.5). The wavelength variation range of breccia mineralization is 2198~2214 nm and is concentrated in 2201~2204 nm near the center, while the overall crystallinity is lesser than 5.5 and concentrated around 1–2.2 near the center. The wavelength and crystallinity of muscovite are mainly affected by Tschermak substitution and temperature. When the contents of Si, Fe, and Mg are low and AlVI is high, the wavelength tends toward the short-wave (SW) direction, while the opposite tends toward the long-wave (LW) direction. The high crystallinity (4.1–8.4) of muscovite can be used as an indicator of porphyry gold mineralization and also provides an important indicator to explore similar types of gold deposits.

GeSnC based Multi-Quantum Well Heterojunction p-i-n Photodetectors for Short Wave Infrared (SWIR) Spectrum

GeSnC based Multi-Quantum Well Heterojunction p-i-n Photodetectors for Short Wave Infrared (SWIR) Spectrum

Characterization of Biological Absorption Spectra Spanning the Visible to the Short-Wave Infrared.

For noninvasive light-based physiological monitoring, optimal wavelengths of individual tissue components can be identified using absorption spectroscopy. However, because of the lack of sensitivity of hardware at longer wavelengths, absorption spectroscopy has typically been applied for wavelengths in the visible (VIS) and near-infrared (NIR) range from 400 to 1,000 nm. Hardware advancements in the short-wave infrared (SWIR) range have enabled investigators to explore wavelengths in the ~1,000 nm to 3,000 nm range in which fall characteristic absorption peaks for lipid, protein, and water. These molecules are difficult to visualize in the VIS-NIR and can provide label-free sources of biological contrast. Furthermore, lower SWIR absorption has been observed for melanin, the primary chromophore responsible for skin pigmentation. In vivooptical devices like clinically standard pulse oximeters have been found to have reduced accuracy in people with darkly pigmented skin, possibly because of the stronger melanin absorption in the VIS range. Thus, error associated with skin pigmentation could be reduced by using devices operating in the SWIR. Optical instrument design is facilitated by the understanding of the absorption properties of core tissue components from the VIS to the SWIR range. This article describes protocols and instrumentation for obtaining VIS-SWIR absorption spectra of common tissue absorbers: oxygenated hemoglobin, deoxygenated hemoglobin, melanin, water, and lipid.

High performance n-MoSe2/p-Ge-GeSn MQW/n-Ge heterojunction phototransistor for extended short-wave infrared photodetection

Abstract Germanium tin (GeSn) is promising to realize monolithic short-wave infrared (SWIR) photodetectors. However, the large dark current and insufficient responsivity resulted from high density of surface states and deteriorated crystal quality of high-Sn fraction GeSn thin films pose significant challenges for their practical application. Herein, fully strained GeSn-Ge multiple-quantum-wells (MQWs) bipolar heterojunction phototransistor (HPT) with an n-MoSe2/p-Ge van der Waals (vdW) heterostructure serving as emitter-base junction is proposed for suppressing the dark current and enhancing the responsivity in SWIR band. The fully strained GeSn-Ge MQWs are strategically employed as the absorption region to extend the response wavelength up to 2000 nm and significantly reduce dark current density. The developed HPT achieves a low dark current density of 7.87 mA/cm2 at 2 V and a commendable responsivity of 1.3 A/W at 1550 nm along with rapid rising/falling times of 81.3 μs/73.1 μs. Furthermore, the phototransistor operates in a self-powered mode, showcasing a high specific detectivity of 2.6 × 1010 Jones. These results suggest that the proposed GeSn-Ge MQWs-based mix-dimensional vdW HPT can effectively suppress dark current and enhance detectivity for advanced SWIR imaging applications.&amp;#xD;

Enhancing kelp forest detection in remote sensing images using crowdsourced labels with Mixed Vision Transformers and ConvNeXt segmentation models

ABSTRACT Kelp forests, as foundation species, are vital to marine ecosystems, providing essential food and habitat for numerous organisms. This study explores the integration of crowdsourced labels with advanced artificial intelligence models to develop a fast and accurate kelp canopy detection pipeline using Landsat imagery. Building on the success of a machine learning competition, where this approach ranked third and performed consistently well on both local validation and public and private leaderboards, the research highlights the effectiveness of combining Mixed Vision Transformers (MIT) with ConvNeXt models. Training these models on various image sizes significantly enhanced the accuracy of the ensemble results. U-Net emerged as the best segmentation architecture, with UpperNet also contributing to the final ensemble. Key Landsat bands, such as ShortWave InfraRed (SWIR1) and Near-InfraRed (NIR), were crucial while altitude data were used in postprocessing to eliminate false positives on land. The methodology achieved a high detection rate, accurately identifying about three out of four pixels containing kelp canopy while keeping false positives low. Despite the medium resolution of Landsat satellites, their extensive historical coverage makes them effective for studying kelp forests. This work also underscores the potential of combining machine learning models with crowdsourced data for effective and scalable environmental monitoring. All running code for training all models and inference can be found at https://github.com/IoannisNasios/Kelp_Forests.

Identification and Classification of Multi‐Species Biofilms on Polymeric Surfaces Using Hyperspectral Imaging

ABSTRACTBiofilm‐associated contamination poses significant challenges to the food industry, particularly in ensuring effective sanitization and reliable detection. This study explores the use of hyperspectral imaging (HSI) in the shortwave infrared (SWIR) range for non‐destructive detection and classification of biofilms on thermoplastic polyurethane (TPU) surfaces. Multi‐species biofilms composed of Comamonas sp., Raoultella sp., and Escherichia coli were formed at 10°C and 25°C and biofilm protein and polysaccharide contents were determined. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS‐DA) were used to differentiate among four classes of TPU coupons, including blank (BLANK), control (CTRL), intermediate‐strength biofilms formed at 10°C (S10), and strong biofilms formed at 25°C (S25). PCA successfully clustered samples based on spectral profiles of the classes, identifying significant wavelength regions at 1451 and 1926 nm, which correlated with the water, protein, and polysaccharide content of multi‐species biofilms. PLS‐DA provided a classification accuracy ranging from 68% to 100%, with the highest classification accuracy (100%) observed for BLANK and biofilm‐contaminated (S25) TPU coupons and the lowest accuracy (68%) for CTR. Additionally, Partial Least Squares Regression (PLSR) was employed to predict the protein content of biofilms, achieving reliable predictions both in calibration ( of 0.81) and external validation ( of 0.72). These findings demonstrate the potential of HSI to detect and classify biofilm‐infected TPU coupons utilizing wavebands associated with proteins, polysaccharides and water. Hence, HSI can be used as a rapid and non‐destructive alternative to traditional methods for biofilm detection, including chemical‐based methods such as BioDetect (SANI MARC) and fluorescence‐based imaging methods like BACTISCAN.

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.

Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands. Sensitivity analyses were also conducted to identify the most sensitive bands for seed protein estimation. Convolutional neural networks (CNNs) with attention mechanisms were proposed along with traditional machine learning models based on feature engineering including Random Forest (RF) and Support Vector Machine (SVM) regression for comparative analysis. Additionally, the CNN classification approach was used to estimate low, medium, and high protein concentrations because this type of classification is more applicable for breeding efforts. Our results showed that the proposed CNN with attention mechanisms predicted wheat protein content with R2 values of 0.70 and 0.65 for ventral and dorsal seed orientations, respectively. Although, the R2 of the CNN approach was lower than of the best performing feature-based method, RF (R2 of 0.77), end-to-end prediction capabilities with CNN hold great promise for the automation of wheat protein estimation for breeding. The CNN model achieved better classification of protein concentrations between low, medium, and high protein contents, with an R2 of 0.82. This study's findings highlight the significant potential of hyperspectral imaging and machine learning techniques for advancing precision breeding practices, optimizing seed sorting processes, and enabling targeted agricultural input applications.

Review of Short-Wavelength Infrared Flip-Chip Bump Bonding Process Technology.

Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm. These advancements stimulate new prospects across a wide array of fields including life sciences, medical diagnostics, defense, surveillance, security, free-space optics (FSO), thermography, agriculture, food inspection, and LiDAR applications. In this review, we begin by introducing monolithic SWIR image sensors and hybrid SWIR image sensors and indicate that flip-chip bump bonding technology remains the predominant integration method for hybrid SWIR image sensors owing to its outstanding performance, adaptable integration with innovative epitaxial SWIR materials, long-term stability, and long-term reliability. Subsequently, we comprehensively summarize recent advancements in epitaxial thin-film SWIR sensors, encompassing FPAs and flip-chip bump bonding technology for epitaxial InGaAs and Ge (Sn) thin-film SWIR sensors. Finally, a summary and outlook regarding the development of InGaAs and Ge (Sn) SWIR sensors are provided and discussed. The ongoing evolution of epitaxial thin-film SWIR sensors with flip-chip bump bonding technology is poised to foster new applications in both academic and industry fields.

Nanophotonic structures energized short-wave infrared quantum dot photodetectors and their advancements in imaging and large-scale fabrication techniques.

Short-wave infrared (SWIR) photodetectors (PDs) have a wide range of applications in the field of information and communication. Especially in recent years, with the increasing demand for consumer electronics, conventional semiconductor-based PDs alone are unable to cope with the ever-increasing market. Colloidal quantum dots (QDs) have attracted great interest due to their low fabrication cost, solution processability, and promising optoelectronic properties. In addition to advancements in synthesis methods and surface ligand engineering, the photoelectronic performance of QD-based SWIR PDs has been greatly improved due to developments in nanophotonic structural engineering, such as microcavities, localized and propagating surface plasmon resonant structures, and gratings for specific and high-performance detection application. The improvement in the performance of photoconductors, photodiodes, and phototransistors also enhances the performance of SWIR imaging sensors where they have been realized and demonstrated promising potential due to the direct integration of QD PDs with CMOS substrates. In addition, flexible manipulation of the QDs has been realized, thanks to their solution-processable capability. Therefore, a variety of large-scale production process methods have been examined including blade coating, flexible microcomb printing, ink-jet printing, spray deposition, etc. which can effectively reduce the cost and promote commercial application in consumer electronics. Finally, the current challenges and future development prospects of QD-based PDs are reviewed and could provide guidance for future design of the QDs PDs.