Optical Imaging Sensors Research Articles

Optical readout Micromegas for monitoring medical pencil scanning proton beams

Micro-pattern gaseous detectors (MPGDs), when integrated with optical imaging sensors, have been proven to effectively and accurately capture radiation beam information. To address the challenges of monitoring the dose and profile of medical pencil proton beams (MPPB), which have a high density of greater than 109 Hz/cm2, an optical readout micro-mesh gaseous structure (ORM) was proposed. A Micromegas prototype was manufactured with a glass substrate coated with transparent indium tin oxide as the detector anode. Its effective area is 8 cm × 8 cm. The ORM was firstly characterized with an Iron-55 X-ray source (55Fe) and a silver target X-ray tube individually, good energy resolution of 14.5% (FWHM), high gain greater than 104, and spatial resolution of 400 μm (10% MTF) were achieved. The prototype was then tested with the MPPB. The evaluation revealed linear dose responses exceeding 99% (R-squared value) for both single-point and nine-point beam spots at various beam energies and doses. The size and center position deviation of the nine-point spot measurement were within 0.35 mm and 1 mm, respectively, indicating the good potential of this method for MPPB spot quality assurance. Additionally, the ORM is expected to be expanded to monitor other types of high-flux beams, such as medical neutron and gamma beam inspections, by adding suitable conversion layers.

Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor.

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.

Robust Light Detection from Ultraviolet to Near‐Infrared with ZnGa2O4/p‐Si Heterojunction Photodiode and Its Application for Optoelectronic Physically Unclonable Functions

AbstractThe Si‐based self‐powered broadband photodiode (SSBP) is prized for its ability to swiftly detect light across a wide spectrum without requiring an external voltage. However, boosting its efficiency remains challenging due to its high refractive index and limited UV light penetration. A combination of Si with ZnGa2O4, an ultra‐wide‐bandgap spinel material, can bring new opportunities to address these shortcomings of SSBP. In this study, a ZnGa2O4/p‐Si heterojunction photodiode is presented, which is capable of detecting UV to near‐infrared light autonomously. Operating without bias, this device exhibits excellent rectification and detects wavelengths from 265 to 1000 nm, achieving impressive performance metrics such as a photo‐to‐dark current ratio of 5.8 × 104, response speed of less than 3 ms, responsivity of 117 mA W−1, and specific detectivity of 5.5 × 1012 Jones while the photodiode demonstrates exceptional stability and durability under harsh conditions. The versatility of this device is demonstrated by applying it to the optical imaging sensors and physically unclonable security devices. This study provides new inspirations for the development of the energy‐efficient and emerging optical sensing technologies.

Wafer-Scale Replication of Plasmonic Nanostructures via Microbubbles for Nanophotonics.

Quasi-3D plasmonic nanostructures are in high demand for their ability to manipulate and enhance light-matter interactions at subwavelength scales, making them promising building blocks for diverse nanophotonic devices. Despite their potential, the integration of these nanostructures with optical sensors and imaging systems on a large scale poses challenges. Here, a robust technique for the rapid, scalable, and seamless replication of quasi-3D plasmonic nanostructures is presented straight from their production wafers using a microbubble process. This approach not only simplifies the integration of quasi-3D plasmonic nanostructures into a wide range of standard and custom optical imaging devices and sensors but also significantly enhances their imaging and sensing performance beyond the limits of conventional methods. This study encompasses experimental, computational, and theoretical investigations, and it fully elucidates the operational mechanism. Additionally, it explores a versatile set of options for outfitting nanophotonic devices with custom-designed plasmonic nanostructures, thereby fulfilling specific operational criteria.

Turbulent Image Restoration in Atmosphere with Cyclopean Processing via Binocular Fusion

Abstract The outstanding issue to overcoming atmospheric turbulence on distant imaging is a fundamental interest and technological challenge. We propose a novel scenario and technique to restore the optical image in turbulent environmental by referring to Cyclopean image with binocular vision. With human visual intelligence, image distortion resulting from the turbulence is shown to be substantially suppressed. Numerical simulation results taking into account of the atmospheric turbulence, optical image system, image sensors, display and binocular vision perception are presented to demonstrate the robustness of the image restoration, which is compared with a single channel planar optical imaging and sensing. Experiment involving binocular telescope, image recording and the stereo-image display is conducted and good agreement is obtained between the simulation with perceptive experience. A natural extension of the scenario is to enhance the capability of anti-vibration or anti-shaking for general optical imaging with Cyclopean image.

Stable, Scalable, and Free-Standing Perovskite Quantum Dots Composite Reinforced by Cellulose Fibers.

Perovskite quantum dots (PQDs) have attracted emerging attention as fluorescent and light-absorbing materials for next-generation optoelectronics due to their outstanding properties and cost-efficiency. However, PQD thin film suffers significant instability due to structure and material failures, which hinders their application in flexible and reliable PQD-based advanced wearable devices. Herein, we use commercial cellulose fiber-based filter paper as a substrate to synthesize PQDs in situ and fabricate PQD-paper free-standing flexible composite film. The abundant hydroxy capping ligands of cellulose fibers and the unique dense network structure of the filter paper can facilitate confined crystallization, forming strong interactions between the PQDs and substrate, the unpackaged PQD composite film showed extraordinary stability (>30 days) in the air with high humidity (90%). Meanwhile, the strong interaction between PQDs and paper enables an ultrasimple drop-cast synthesis process with excellent process tolerance, making it customizable and easy to scale up (10 cm in diameter). Due to the uniformly dispersed PQDs on cellulose fibers of the substrate, the composite demonstrates impressive photo-responsive properties. Photodetector (PD) arrays were designed on free-standing PQD paper and flexible graphitic electrodes, and circuits were fabricated by drawing. The PD arrays can work as optical and electrical dual-mode image sensors with incredible bending robustness, enduring up to 100,000 cycles at 180°.

Arylazopyrazole-modulated stable dual-mode phototransistors.

High-performance organic devices with dynamic and stable modulation are essential for building devices adaptable to the environment. However, the existing reported devices incorporating light-activated units exhibit either limited device stability or subpar optoelectronic properties. Here, we synthesize a new optically tunable polymer dielectric functionalized with photochromic arylazopyrazole units with a cis-isomer half-life of as long as 90 days. On this basis, stable dual-mode organic transistors that can be reversibly modulated are successfully fabricated. The trans-state devices exhibit high carrier mobility reaching 7.4 square centimeters per volt per second and excellent optical figures of merit, whereas the cis-state devices demonstrate stable but starkly different optoelectronic performance. Furthermore, optical image sensors are prepared with regulatable nonvolatile memories from 36 hours (cis state) to 108 hours (trans state). The achievement of dynamic light modulation shows remarkable prospects for the intelligent application of organic optoelectronic devices.

Color arrestor pixels for high-fidelity, high-sensitivity imaging sensors.

Silicon is the dominant material in complementary metal-oxide-semiconductor (CMOS) imaging devices because of its outstanding electrical and optical properties, well-established fabrication methods, and abundance in nature. However, with the ongoing trend toward electronic miniaturization, which demands smaller pixel sizes in CMOS image sensors, issues, such as crosstalk and reduced optical efficiency, have become critical. These problems stem from the intrinsic properties of Si, particularly its low absorption in the long wavelength range of the visible spectrum, which makes it difficult to devise effective solutions unless the material itself is changed. Recent advances in optical metasurfaces have offered new possibilities for solving these problems. In this study, we propose color arrestor pixels (CAPs) as a new class of color image sensors whose composite spectral responses directly mimic those of the human eye. The key idea is to employ linearly independent combinations of standardized color matching functions. These new basis functions allow our device to reproduce colors more accurately than the currently available image sensors with red-green-blue filters or other metasurface-based sensors, demonstrating an average CIEDE2000 color difference value of only 1.79 when evaluating 24 colors from the Gretag-Macbeth chart under standard illuminant D65. Owing to their high fidelity to the human eye response, CAPs consistently exhibit exceptional color reproduction accuracy under various spectral illumination compositions. With a small footprint of 860 nm height and 221 nm full-color pixel pitch, the CAPs demonstrated high absorption efficiencies of 79 %, 81 %, and 63 % at wavelengths of 452 nm, 544 nm, and 603 nm, respectively, and good angular tolerance. With such a high density of pixels efficiently capturing accurate colors, CAPs present a new direction for optical image sensor research and their applications.

An oil wear particle identification method based on Wasserstein generative adversarial network and improved CNN using a custom-built optical imaging sensor

The accurate classification of wear particles in lubricating oil holds significant importance for understanding the wear characteristics of lubricated components. However, there are prominent issues, such as blurred particle images, limited identification efficiency, and low generalization capabilities. To solve such issues, this research initially devises a novel online monitoring sensor to collect particle images based on the optical method. Subsequently, an improved Wasserstein Generative Adversarial Network (WGAN) is developed to ameliorate the categorization performance, which is hampered by data imbalance. Lastly, a MobileNetV2-SENet-based Convolutional Neural Network (MSCNN) is proposed to autonomously discover the associations between images and types of wear particles. Experimental results illustrate that our method achieves a recognition accuracy rate of 92.5%. The analysis outcomes between several comparative networks show that our approach can effectively improve recognition performance when limited samples are available. This work offers an effective solution for automatically identifying wear particles in oil and can be a powerful predictive maintenance tool for rotating mechanical equipment.

A Novel Hybrid Optical Imaging Sensor for Early Stage Short-Circuit Fault Diagnosis in Printed Circuit Boards

A Novel Hybrid Optical Imaging Sensor for Early Stage Short-Circuit Fault Diagnosis in Printed Circuit Boards

Rapid detection of West Nile and Dengue viruses from mosquito saliva by loop-mediated isothermal amplification and displaced probes.

Arthropod-borne viruses are major causes of human and animal disease, especially in endemic low- and middle-income countries. Mosquito-borne pathogen surveillance is essential for risk assessment and vector control responses. Sentinel chicken serosurveillance (antibody testing) and mosquito pool screening (by RT-qPCR or virus isolation) are currently used to monitor arbovirus transmission, however substantial time lags of seroconversion and/or laborious mosquito identification and RNA extraction steps sacrifice their early warning value. As a consequence, timely vector control responses are compromised. Here, we report on development of a rapid arbovirus detection system whereby adding sucrose to reagents of loop-mediated isothermal amplification with displaced probes (DP-LAMP) elicits infectious mosquitoes to feed directly upon the reagent mix and expectorate viruses into the reagents during feeding. We demonstrate that RNA from pathogenic arboviruses (West Nile and Dengue viruses) transmitted in the infectious mosquito saliva was detectable rapidly (within 45 minutes) without RNA extraction. Sucrose stabilized viral RNA at field temperatures for at least 48 hours, important for transition of this system to practical use. After thermal treatment, the DP-LAMP could be reliably visualized by a simple optical image sensor to distinguish between positive and negative samples based on fluorescence intensity. Field application of this technology could fundamentally change conventional arbovirus surveillance methods by eliminating laborious RNA extraction steps, permitting arbovirus monitoring from additional sites, and substantially reducing time needed to detect circulating pathogens.

Improving the Spatial Resolution of Small Satellites by Implementing a Super-Resolution Algorithm Based on the Optical Imaging Sensor’s Rotation Approach

Improving the Spatial Resolution of Small Satellites by Implementing a Super-Resolution Algorithm Based on the Optical Imaging Sensor’s Rotation Approach

Giant lateral photovoltaic effect in Ag/porous silicon/Si structure for high-performance near-infrared detection

The lateral photovoltaic effect demonstrates versatility across various applications, including photodetectors, imaging, spectroscopy, biosensing, and environmental monitoring. Challenges arise from the low energy of near-infrared photons, regarded as a bottleneck for high-sensitivity photodetector development in this spectrum, limiting applications like optical fiber communication and image sensors. However, this study achieved a remarkable 720.57 mV/mm sensitivity for the lateral photovoltaic effect in the near-infrared region with a 980 nm laser irradiation on an Ag/Porous Silicon/Si structure. This sensitivity surpasses previous observations several to hundreds of times and even outperforms many other structures in the short-wavelength spectrum, further emphasizing its significance in this field. The use of a unique fluoro-amino electrolysis technique ensures consistent porosity, mitigating the adverse effects of photoluminescence. Combined with surface Ag, it induces local surface plasmon resonance and efficient plasmon coupling, markedly increasing electron density. This highly sensitive structure propels advancements in near-infrared photodetectors and Raman signal enhancement, establishing a robust foundation for potential applications in highly sensitive photodetectors and biological sensors.

Ptychographic lens-less birefringence microscopy using a mask-modulated polarization image sensor

Birefringence, an inherent characteristic of optically anisotropic materials, is widely utilized in various imaging applications ranging from material characterizations to clinical diagnosis. Polarized light microscopy enables high-resolution, high-contrast imaging of optically anisotropic specimens, but it is associated with mechanical rotations of polarizer/analyzer and relatively complex optical designs. Here, we present a form of lens-less polarization-sensitive microscopy capable of complex and birefringence imaging of transparent objects without an optical lens and any moving parts. Our method exploits an optical mask-modulated polarization image sensor and single-input-state LED illumination design to obtain complex and birefringence images of the object via ptychographic phase retrieval. Using a camera with a pixel size of 3.45 μm, the method achieves birefringence imaging with a half-pitch resolution of 2.46 μm over a 59.74 mm2 field-of-view, which corresponds to a space-bandwidth product of 9.9 megapixels. We demonstrate the high-resolution, large-area, phase and birefringence imaging capability of our method by presenting the phase and birefringence images of various anisotropic objects, including a monosodium urate crystal, and excised mouse eye and heart tissues.

Microfluidics on lensless, semiconductor optical image sensors: challenges and opportunities for democratization of biosensing at the micro-and nano-scale.

Optical image sensors are 2D arrays of pixels that integrate semiconductor photodiodes and field effect transistors for efficient photon conversion and processing of generated electrons. With technological advancements and subsequent democratization of these sensors, opportunities for integration with microfluidics devices are currently explored. 2D pixel arrays of such optical image sensors can reach dimensions larger than one centimeter with a sub-micrometer pixel size, for high spatial resolution lensless imaging with large field of view, a feat that cannot be achieved with lens-based optical microscopy. Moreover, with advancements in fabrication processes, the field of microfluidics has evolved to develop microfluidic devices with an overall size below one centimeter and individual components of sub-micrometer size, such that they can now be implemented onto optical image sensors. The convergence of these fields is discussed in this article, where we review fundamental principles, opportunities, challenges, and outlook for integration, with focus on contact-mode imaging configuration. Most recent developments and applications of microfluidic lensless contact-based imaging to the field of biosensors, in particular those related to the potential for point of need applications, are also discussed.

Special Issue on Optical Camera Communications and Applications

Optical Camera Communication (OCC) is a groundbreaking technology that combines optical signals and image sensors for data transmission [...]

A Novel Electromagnetic Noise and Image Stripe Noise Suppression Method Between SMA-OIS Actuator and CMOS Image Sensor

In order to solve the electromagnetic compatibility (EMC) problem between shape memory alloy (SMA) optical image stabilization (OIS) actuator and CMOS image sensor (CIS), a novel electromagnetic noise and image stripe noise (ISN) suppression method is proposed in this paper. The electromagnetic interference mechanism of SMA-OIS actuator is analyzed by electromagnetic field simulation and magnetic dipole mathematical model. The CIS noise mechanism based on the readout circuit model and image signal processing (ISP) pipeline is analyzed and the ISN evaluation method of CIS is proposed. The drive frequency and ISN model is established by frequency sweep experiment through the joint experimental platform of SMA-OIS actuator and CIS. Furthermore, the random frequency modulation PWM method based on the spread spectrum principle is proposed to suppress the electromagnetic noise and ISN. The experimental results show that the state-of-the-art noise suppression method proposed in this paper can not only eliminate ISN, but also have good technical adaptability in practical applications.

Multiple side-coupled images recognition in plastic optical fibers based on deep learning

In recent years, the technology of multi-mode fiber (MMF) output speckle recognition has been widely used in the fields of optical imaging, endoscope devices, and fiber optic sensor. However, previous studies focused on the single-input transmission mode on a single fiber, which limited the application range of MMF. Meanwhile, the fiber optic sensors based on speckle recognition often lack multiplexing ability. In this paper, multiple lateral sections of plastic optical fiber (POF) are polished, and the images corresponding to 26 letters are coupled into POF form them. When images couple into POF from one lateral polishing section, by analyzing the output speckles based on deep learning technology, the accuracy of speckles recognition with convolution neural network (CNN) reaches over 95% and the structural similarity (SSIM) of reconstructed speckles with U-Net reaches up to 0.96. When two images couple into POF from different lateral polishing sections simultaneously, the output speckle can be reconstructed to the two images respectively, and the average SSIM value for them is 0.95. The experiment proves that the images coupled into fiber through the lateral polishing section of POF can be recognized and reconstructed well by output speckle analysis. This technology can promote the multiplexing ability of the speckles-recognition based fiber sensors or be applied to the field of fiber endoscopes.

High‐Resolution and Stable Ruddlesden–Popper Quasi‐2D Perovskite Flexible Photodetectors Arrays for Potential Applications as Optical Image Sensor

AbstractThe development of an efficient fabrication route to achieve high‐resolution perovskite pixel array is key for large‐scale flexible image sensor devices. Herein, a high‐resolution and stable 10 × 10 flexible PDs array based on formamidinium(FA+) and phenylmethylammonium (PMA+) quasi‐2D (PMA)2FAPb2I7 (n = 2) perovskite is demonstrated by developing SiO2‐assisted hydrophobic and hydrophilic treatment process on polyethylene terephthalate substrate. By introducing Au nanoparticles (Au NPs), the perovskite film quality is improved and grain boundaries are reduced. The mechanism by which Au NPs upgrade the photoelectric quality of perovskite is mainly revealed by glow discharge‐optical emission spectroscopy (GD‐OES) and grazing‐incidence wide‐angle X‐ray scattering (GIWAXS). To further improve the photoelectric performance of the devices, a post‐treatment strategy with formamidinium chloride (FACl) is used . The optimized flexible PDs arrays show excellent optoelectronic properties with a high responsivity of 4.7 A W−1, a detectivity of 6.3 × 1012 Jones, and a broad spectral sensitivity. The device also exhibits excellent electrical stability even under severe bending and excellent flexural strength, as well as excellent environmental stability. Finally, the integrated flexible PDs arrays are used as sensor pixels in an imaging system to obtain high‐resolution imaging patterns, demonstrating the imaging capability of the PDs arrays.

Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images

The deep-learning-based image super-resolution opens a new direction for the remote sensing field to reconstruct further information and details from captured images. However, most current SR works try to improve the performance by increasing the complexity of the model, which results in significant computational costs and memory consumption. In this paper, we propose a lightweight model named pixel-wise attention residual network for optical remote sensor images, which can effectively solve the super-resolution task of multi-satellite images. The proposed method consists of three modules: the feature extraction module, feature fusion module, and feature mapping module. First, the feature extraction module is responsible for extracting the deep features from the input spatial bands with different spatial resolutions. Second, the feature fusion module with the pixel-wise attention mechanism generates weight coefficients for each pixel on the feature map and fully fuses the deep feature information. Third, the feature mapping module is aimed to maintain the fidelity of the spectrum by adding the fused residual feature map directly to the up-sampled low-resolution images. Compared with existing deep-learning-based methods, the major advantage of our method is that for the first time, the pixel-wise attention mechanism is incorporated in the task of super-resolution fusion of remote sensing images, which effectively improved the performance of the fusion network. The accuracy assessment results show that our method achieved superior performance of the root mean square error, signal-to–reconstruction ratio error, universal image quality index, and peak signal noise ratio compared to competing approaches. The improvements in the signal-to-reconstruction ratio error and peak signal noise ratio are significant, with a respective increase of 0.15 and 0.629 dB for Sentinel-2 data, and 0.196 and 1 dB for Landsat data.