Low-light Applications Research Articles

Boosting conversion efficiency by bandgap engineering of ecofriendly antimony trisulfide indoor photovoltaics via a modeling approach

With the exponential growth of the Internet of Things (IoT), indoor photovoltaics (IPVs) have emerged as a pivotal technology for powering low-power devices, drawing heightened interest due to their adaptability to indoor environments. The Photovoltaic Conversion Efficiency (PCE) of IPV cells is critically dependent on their ability to match the indoor spectrum with the device's response characteristics. In this realm, Antimony Trisulfide (Sb2S3), characterized by its wide bandgap and high absorption coefficient, emerges as a promising candidate for low-light applications. Our study focuses on the modeling and numerical analysis of Sb2S3 thin-film IPV cells by wxAMPS software, aiming to refine both the device structure and its photoelectric performance for effective indoor light harvesting. In a strategic shift from conventional CdS materials, we utilized SnO2—known for its high transmissivity, non-toxicity, and wide bandgap—as the electron transport layer (ETL) in Sb2S3 IPV cells. This substitution notably enhanced the short-wave response, elevating the spectral response from 45 % to 80 % at 400 nm. Additionally, we introduced a bandgap-tunable ZnOS buffer layer. This innovation proved instrumental in rectifying the band alignment mismatch between SnO2 and Sb2S3 layer, thereby optimizing interfacial electron transport properties. The integration of the ZnOS buffer layer effectively improved the fill factor from 40.0 % to 64.7 % of the Sb2S3 IPV cell by solving the band mismatch problem. The resulting optimized Sb2S3 IPV cell demonstrated exceptional response characteristics across the full visible spectrum (400–750 nm) and showed notable photoelectric performance under both fluorescent lamps (FLs) and light-emitting diodes (LEDs). Moreover, a detailed analysis was conducted on the performance differences of the device under indoor light sources compared to solar spectrum conditions, along with the underlying mechanisms. Finally, the Sb2S3 IPV cell achieved a peak theoretical efficiency of 46.25 % under cold white FL lighting, a testament to the optimal match between the device structure and this specific emission power spectrum. This modeling research not only underscores the feasibility of employing antimony-based photovoltaic technologies in indoor settings but also offers theoretical guidance for further advancements in this domain.

Nickel-Based Two-Electron Redox Shuttle for Dye-Sensitized Solar Cells in Low Light Applications

Nickel-Based Two-Electron Redox Shuttle for Dye-Sensitized Solar Cells in Low Light Applications

A Novel Progressive Enhancement of Low Light Raw Images

Low-light imaging on mobile devices is often difficult due to the issue of not enough light passing through the small aperture, resulting in poor quality images. Most previous work on low-resolution images has focused on a single task, such as illumination, color correction, or noise removal; Noise removal function based on short and long images of the camera model. This technique is less efficient and general in real-world environments that require special camera integration and restoration. In this paper, we propose a lighting system that can integrate illumination variation, color correction, and noise removal to solve this problem. Considering the difficulty of obtaining model-specific data and the maximum content of the resulting image, we created two branches: the coefficient estimation branch and the integration branch. While the computer estimator operates in the sparsely resolved space and estimates the coefficients developed by pairwise learning, the collaborative system operates in the fully resolved space, providing step-by-step integration and denoising. Compared to existing methods, our framework does not need to remember as much information when switching to another camera model, which reduces the effort required to benefit our usage pattern. Through extensive testing, we demonstrate its great potential in real-world low-light applications.

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Room-temperature waveguide-coupled silicon single-photon avalanche diodes

Single photon detection is important for a wide range of low-light applications, including quantum information processing, spectroscopy, and light detection and ranging (LiDAR). A key challenge in these applications has been to integrate single-photon detection capability into photonic circuits for the realization of complex photonic microsystems. Short-wavelength (λ < 1.1 μm) integrated photonics platforms that use silicon (Si) as photodetectors offer the opportunity to achieve single-photon avalanche diodes (SPADs) that operate at or near room temperature. Here, we report the first waveguide-coupled Si SPAD. The device is monolithically integrated in a Si photonic platform and operates in the visible spectrum. The device exhibited a single photon detection efficiency of >6% for wavelengths of 488 and 532 nm with an excess voltage of <20% of the breakdown voltage. The dark count rate was below 100 kHz at room temperature, with the possibility of improving by approximately 35% by reducing the temperature to −5 °C.

In Situ Formation of 2D Perovskite Seeding for Record-Efficiency Indoor Perovskite Photovoltaic Devices.

With 40% efficiency under room light intensity, perovskite solar cells (PSCs) will be promising power supplies for low-light applications, particularly for Internet of Things (IoT) devices and indoor electronics, shall they become commercialized. Herein, β-alaninamide hydrochloride (AHC) is utilized to spontaneously form a layer of 2D perovskite nucleation seeds for improved film uniformity, crystallization quality, and solar cell performance. It is found that the AHC addition indeed improves film quality as demonstrated by better uniformity, lower trap density, smaller lattice stress, and, as a result, a 10-fold increase in charge carrier lifetime. Consequently, not only does the small-area (0.09 cm2 ) PSCs achieve a power conversion efficiency of 42.12%, the large-area cells (1.00 cm2 , and 2.56 cm2 ) attain efficiency as high as 40.93%, and 40.07% respectively. All of these are the highest efficiency values for indoor photovoltaic cells with similar sizes, and more importantly, they represent the smallest efficiency loss due to area scale-up. This work provides a new method to fabricate high-performance indoor PSCs (i-PSCs) for IoT devices with great potential in large-area printing technology.

Resonant InGaAs Detectors and Emitters for Passive and Active Imaging Application

In this work, we design an InGaAs avalanche photodiode (APD) with a higher photocurrent to dark current ratio than a conventional APD. The improvement is based on optical confinement in an optical resonator, which increases the optical intensity in the detector volume and thus its quantum efficiency. With optical confinement, the absorbing layer can be 20 times thinner than the conventional APD, reducing the intrinsic generation-recombination dark current by the same amount and still be able to produce 90.9% of the photocurrent. The photocurrent to intrinsic dark current ratio can thus be increased theoretically by 18.2 times in concomitant with a lower operating voltage. Higher photocurrent can also be obtained using a thicker absorber. Besides APDs, the optical resonator structure can also be applied to p-i-n photodiodes for similar improvement. To further increase the detection sensitivity, we design an efficient InGaAsP light emitting diode (LED) having a similar resonator geometry. The resonator geometry enables more collimated emission and increases the optical power theoretically by more than 36 times compared to a conventional LED. Integrating the resonant APD and LED together will yield a powerful SWIR transceiver for various low light, LIDAR, and 3-dimensional imaging applications.

A novel silicon photomultiplier readout architecture for low-light applications[This manuscript has been authored by UT-Battelle, LLC, under contractDE-AC05-00OR22725 with the U.S. Department of Energy (DOE). TheU.S. government retains and the publisher, by accepting the articlefor publication, acknowledges that the U.S. government retains acnonexclusive, paid-up, irrevocable, worldwide license to publish orreproduce the published form of this manuscript, or allow others to

In this article we describe the photon detection readout electronics for the nEDM@SNS experiment. The chosen “photon counting” architecture, which utilizes high-efficiency silicon photomultipliers (SiPMs) and is appropriate for low-light applications, allows the use of a relatively high SiPM operating voltage. This maximizes photon detection efficiency and minimizes gain/efficiency voltage-dependence while eliminating direct optical cross-talk.

Synthesis and analysis of a hybrid high step-up DC/DC converter with a single inductor

For low power lighting applications using Light-Emitting Diodes (LEDs), we propose a new hybrid single inductor boost converter in this paper. Unlike traditional drivers, the proposed LED source driver has a topology that fuses a Boost converter (BC) and a hybrid Cockcroft–Walton/ Dickson converter (HCWDC). The fusion of BC and HCWDC allows for high gain and few components as well as easy control. The properties of the proposed converter, which consists of 2 modules with 2 multiplier stages, are clarified by theoretical analysis and circuit simulations. Moreover, we implement the prototype on a breadboard. In the evaluations, the proposed driver achieves higher efficiency and fewer components than the existing single inductor LED drivers.

High-performance large-area blade-coated perovskite solar cells with low ohmic loss for low lighting indoor applications

Emerging hybrid organic–inorganic perovskites with superior optoelectronic property demonstrate promising prospect for photovoltaic (PV) applications, in particular for low-lighting indoor applications e.g. within internet of things (IoT) networks or low-energy wireless communication devices. In order to prepare devices with high power output under low-illumination conditions, scalable fabrication techniques are preferred for large-area perovskite solar cells. In additions, one of the key parameters to achieve high-efficiency large-area perovskite solar cells is to minimize the ohmic loss to further boost the solar cell efficiency. Herein, a one-step blade-coating method assisted by hexafluorobenzene (HFB) was developed to deposit dense, large-area smooth and high-quality perovskite films with low ohmic loss. The as-fabricated devices demonstrated power conversion efficiency (PCE) of 20.7% (area of 0.2 cm2) and 16.5% (1 cm2), respectively, under standard (AM 1.5G) illumination conditions. Besides, the large-area (1 cm2) devices demonstrated a remarkable PCE of ∼ 33.8% and ∼ 30.0% under 1000 lx and 100 lx illumination provided by white light-emitting diode (LED) lamp, respectively. We exhibited a series-connected stack of large-area (totally active area ∼ 4 cm2) perovskite photovoltaic device powering up a LED under common indoor environment as an indoor self-power indicator lamp. The analysis using a single diode model suggests that the high performance of the large-area devices under low-lighting indoor conditions is highly associated with the largely reduced ohmic losses, which particularly indicate that the perovskite films by a facile and scalable blade-coating method. The presented scalable approach paves the way to designing high-performance perovskite solar cells for a variety of emerging indoor PV applications.

Atomic layer deposition of conformal anti-reflective coatings on complex 3D printed micro-optical systems

3D printing of micro-optics has recently become a very powerful fabrication method for sub-millimeter sized optics. Miniature optical systems and entire optical instruments such as endoscopes have become possible with this technique. 3D printed complex micro-optical systems are printed in one single process, rather than being assembled. This precludes anti-reflection coating of the individual lenses before assembly by conventional coating methods such as sputtering or directed plasma etching, as voids between the individual lenses cannot be reached by a directed coating beam. We solve this issue by conformal low-temperature thermal atomic layer deposition (ALD) which is compatible with the low glass transition temperature of the utilized 3D printed polymer materials. Utilizing 4-layer designs, we decrease the broadband reflectivity of coated flat substrates in the visible to below 1%. We characterize and investigate the properties of the coatings based on transmission measurements through coated and uncoated 3D printed test samples as well as through a double-lens imaging system. We find that the reflectivity is significantly reduced and conversely the transmission is enhanced, which is of particular interest for low-light applications. Furthermore, the physical durability and resistance against humidity uptake should also be improved.

Probabilistic Energy-to-Amplitude Mapping in a Tapered Superconducting Nanowire Single-Photon Detector.

A spectrum-resolved photon detector is crucial for cutting-edge quantum optics, astronomical observation, and spectroscopic sensing. However, such an ability is rarely obtained because a direct linear conversion from weak single-photon energy to a readable electrical signal above the noise level without causing an avalanche is challenging. Here, we overcame these difficulties by building a probabilistic energy-to-amplitude mapping in a tapered superconducting nanowire single-photon detector and combining a computational reconstruction to obtain equivalent spectral resolving capacity. Distinguished dependence of pulse amplitude distributions on varied input spectra has been observed experimentally. As the energy-to-amplitude mapping is probabilistic, statistical measurements are required. By collecting around a few hundred photons, we have demonstrated wavelength perception over a wide spectral range from 600 to 1700 nm with a resolution of 100 nm. These findings represent a new approach to designing spectrum-sensitive SNSPDs for low-light spectroscopic applications.

Lowlight object recognition by deep learning with passive three-dimensional integral imaging in visible and long wave infrared wavelengths.

Traditionally, long wave infrared imaging has been used in photon starved conditions for object detection and classification. We investigate passive three-dimensional (3D) integral imaging (InIm) in visible spectrum for object classification using deep neural networks in photon-starved conditions and under partial occlusion. We compare the proposed passive 3D InIm operating in the visible domain with that of the long wave infrared sensing in both 2D and 3D imaging cases for object classification in degraded conditions. This comparison is based on average precision, recall, and miss rates. Our experimental results demonstrate that cold and hot object classification using 3D InIm in the visible spectrum may outperform both 2D and 3D imaging implemented in long wave infrared spectrum for photon-starved and partially occluded scenes. While these experiments are not comprehensive, they demonstrate the potential of 3D InIm in the visible spectrum for low light applications. Imaging in the visible spectrum provides higher spatial resolution, more compact optics, and lower cost hardware compared with long wave infrared imaging. In addition, higher spatial resolution obtained in the visible spectrum can improve object classification accuracy. Our experimental results provide a proof of concept for implementing visible spectrum imaging in place of the traditional LWIR spectrum imaging for certain object recognition tasks.

Progressive Joint Low-Light Enhancement and Noise Removal for Raw Images.

Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in low image quality. Most of the previous works on low-light imaging focus either only on a single task such as illumination adjustment, color enhancement, or noise removal; or on a joint illumination adjustment and denoising task that heavily relies on short-long exposure image pairs from specific camera models. These approaches are less practical and generalizable in real-world settings where camera-specific joint enhancement and restoration is required. In this paper, we propose a low-light imaging framework that performs joint illumination adjustment, color enhancement, and denoising to tackle this problem. Considering the difficulty in model-specific data collection and the ultra-high definition of the captured images, we design two branches: a coefficient estimation branch and a joint operation branch. The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint operation branch works in a full-resolution space and progressively performs joint enhancement and denoising. In contrast to existing methods, our framework does not need to recollect massive data when adapted to another camera model, which significantly reduces the efforts required to fine-tune our approach for practical usage. Through extensive experiments, we demonstrate its great potential in real-world low-light imaging applications.

Photon Limited Non-Blind Deblurring Using Algorithm Unrolling

Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of the photon shot noise due to the low illumination and/or short exposure makes the deblurring task substantially more challenging than the conventional deblurring problems. In this paper, we present an algorithm unrolling approach for the photon-limited deblurring problem by unrolling a Plug-and-Play algorithm for a fixed number of iterations. By introducing a three-operator splitting formation of the Plug-and-Play framework, we obtain a series of differentiable steps which allows the fixed iteration unrolled network to be trained end-to-end. The proposed algorithm demonstrates significantly better image recovery compared to existing state-of-the-art deblurring approaches. We also present a new photon-limited deblurring dataset for evaluating the performance of algorithms.

How Thin Practical Silicon Heterojunction Solar Cells Could Be? Experimental Study under 1 Sun and under Indoor Illumination

The transition toward thinner microcrystalline silicon wafers for their potential performance gain has been of interest in recent years. Theoretical predictions have estimated a maximum efficiency for silicon wafers to be at about 100−110 μm thickness. The potential and losses in silicon heterojunction solar cells prepared on wafers with thickness in the range of 60−170 μm with focus on open‐circuit voltage (V OC) and fill factor (FF) are studied experimentally. The applicability of thinner wafers for low light and indoor applications using light emitting diode (LED) lighting is also studied. The implied V OC (iV OC) is observed to increase with a decrease in wafer thickness according to theoretical predictions with absolute values approaching the theoretical limit. Unlike the iV OC, the implied FF is observed to decrease with wafer thickness reduction opposite to the theoretical predictions which are related to the effect of surface recombination. A combination of gains and losses results in a broad range of high efficiency under 1 sun for wafer thicknesses ranging from 75 to 170 μm with maximum of 22.3% obtained at 75 μm. As for indoor performance, thinner wafers show slightly better efficiency at lower light intensity under sun and LED illumination, promising improved performance for even thinner devices.

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra.

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

40.1% Record Low-Light Solar-Cell Efficiency by Holistic Trap-Passivation using Micrometer-Thick Perovskite Film.

Perovskite solar cells exhibit not only high efficiency under full AM1.5 sunlight, but also have great potential for applications in low-light environments, such as indoors, cloudy conditions, early morning, late evening, etc. Unfortunately, their performance still suffers from severe trap-induced nonradiative recombination, particularly under low-light conditions. Here, a holistic passivation strategy is developed to reduce traps both on the surface and in the bulk of micrometer-thick perovskite film, leading to a record efficiency of 40.1% under 301.6 µW cm-2 warm light-emitting diode (LED) light for low-light solar-cell applications. The involvement of guanidinium into the perovskite bulk film and 2-(4-methoxyphenyl)ethylamine hydrobromide (CH3 O-PEABr) passivation on the perovskite surface synergistically suppresses the trap states. The charge carrier lifetimes of the perovskite film increase by tenfold and fivefold to 981ns and 8.02 µs at the crystal surface and in its bulk, respectively. The decreased nonradiative recombination loss translates to a high open-circuit voltage (Voc ) of 1.00V, a high short-circuit current (Jsc ) of 152.10 µA cm-2 , and a fill factor (FF) of 79.52%. Note that this performance also stands as the highest among all photovoltaics measured under indoor light illumination. This work of trap passivation for micrometer-thick perovskite film paves a way for high-performance, self-powered IoT devices.

Learning Optimal Wavefront Shaping for Multi-Channel Imaging.

Fast acquisition of depth information is crucial for accurate 3D tracking of moving objects. Snapshot depth sensing can be achieved by wavefront coding, in which the point-spread function (PSF) is engineered to vary distinctively with scene depth by altering the detection optics. In low-light applications, such as 3D localization microscopy, the prevailing approach is to condense signal photons into a single imaging channel with phase-only wavefront modulation to achieve a high pixel-wise signal to noise ratio. Here we show that this paradigm is generally suboptimal and can be significantly improved upon by employing multi-channel wavefront coding, even in low-light applications. We demonstrate our multi-channel optimization scheme on 3D localization microscopy in densely labelled live cells where detectability is limited by overlap of modulated PSFs. At extreme densities, we show that a split-signal system, with end-to-end learned phase masks, doubles the detection rate and reaches improved precision compared to the current state-of-the-art, single-channel design. We implement our method using a bifurcated optical system, experimentally validating our approach by snapshot volumetric imaging and 3D tracking of fluorescently labelled subcellular elements in dense environments.

Analytical Evaluation of Signal-to-Noise Ratios for Avalanche- and Single-Photon Avalanche Diodes

Performance of systems for optical detection depends on the choice of the right detector for the right application. Designers of optical systems for ranging applications can choose from a variety of highly sensitive photodetectors, of which the two most prominent ones are linear mode avalanche photodiodes (LM-APDs or APDs) and Geiger-mode APDs or single-photon avalanche diodes (SPADs). Both achieve high responsivity and fast optical response, while maintaining low noise characteristics, which is crucial in low-light applications such as fluorescence lifetime measurements or high intensity measurements, for example, Light Detection and Ranging (LiDAR), in outdoor scenarios. The signal-to-noise ratio (SNR) of detectors is used as an analytical, scenario-dependent tool to simplify detector choice for optical system designers depending on technologically achievable photodiode parameters. In this article, analytical methods are used to obtain a universal SNR comparison of APDs and SPADs for the first time. Different signal and ambient light power levels are evaluated. The low noise characteristic of a typical SPAD leads to high SNR in scenarios with overall low signal power, but high background illumination can saturate the detector. LM-APDs achieve higher SNR in systems with higher signal and noise power but compromise signals with low power because of the noise characteristic of the diode and its readout electronics. Besides pure differentiation of signal levels without time information, ranging performance in LiDAR with time-dependent signals is discussed for a reference distance of 100 m. This evaluation should support LiDAR system designers in choosing a matching photodiode and allows for further discussion regarding future technological development and multi pixel detector designs in a common framework.