Nitrogen-Doped 3D-Graphene Advances Near-Infrared Photodetector for Logic Circuits and Image Sensors Overcoming 2D Limitations.

The limitations of two-dimensional (2D) graphene in broadband photodetector are overcome by integrating nitrogen (N) doping into three-dimensional (3D) structures within silicon (Si) via plasma-assisted chemical vapor deposition (PACVD) technology. This contributes to the construction of vertical Schottky heterojunction broad-spectrum photodetectors and applications in logic devices and image sensors. The natural nanoscale resonant cavity structure of 3D-graphene enhances photon capture efficiency, thereby increasing photocarrier generation. N-doping can fine-tune the electronic structure, advancing the Schottky barrier height and reducing dark current. The as-fabricated photodetector exhibits exceptional self-driven photoresponse, especially at 1550 nm, with an excellent photoresponsivity (79.6 A/W), specific detectivity (1013 Jones), and rapid response of 130 µs. Moreover, it enables logic circuits, high-resolution pattern image recognition, and broadband spectra recording across the visible to near-infrared range (400-1550 nm). This research will provide new views and technical support for the development and widespread application of high-performance semiconductor-based graphene broadband detectors.

A Hybrid System for Defect Detection on Rail Lines through the Fusion of Object and Context Information.

Defect detection on rail lines is essential for ensuring safe and efficient transportation. Current image analysis methods with deep neural networks (DNNs) for defect detection often focus on the defects themselves while ignoring the related context. In this work, we propose a fusion model that combines both a targeted defect search and a context analysis, which is seen as a multimodal fusion task. Our model performs rule-based decision-level fusion, merging the confidence scores of multiple individual models to classify rail-line defects. We call the model "hybrid" in the sense that it is composed of supervised learning components and rule-based fusion. We first propose an improvement to existing vision-based defect detection methods by incorporating a convolutional block attention module (CBAM) in the you only look once (YOLO) versions 5 (YOLOv5) and 8 (YOLOv8) architectures for the detection of defects and contextual image elements. This attention module is applied at different detection scales. The domain-knowledge rules are applied to fuse the detection results. Our method demonstrates improvements over baseline models in vision-based defect detection. The model is open for the integration of modalities other than an image, e.g., sound and accelerometer data.

A Review of Image Sensors Used in Near-Infrared and Shortwave Infrared Fluorescence Imaging.

To translate near-infrared (NIR) and shortwave infrared (SWIR) fluorescence imaging into the clinic, the paired imaging device needs to detect trace doses of fluorescent imaging agents. Except for the filtration scheme and excitation light source, the image sensor used will finally determine the detection limitations of NIR and SWIR fluorescence imaging systems. In this review, we investigate the current state-of-the-art image sensors used in NIR and SWIR fluorescence imaging systems and discuss the advantages and limitations of their characteristics, such as readout architecture and noise factors. Finally, the imaging performance of these image sensors is evaluated and compared.

Investigation of the Bending Process and Theory in Free-Boundary Pneumatic Film-Forming for Curved Image Sensors.

To explore the bending process and theory of the free-boundary aerodynamic film forming method for curved detectors, this study integrates practical forming structures with theoretical analysis and establishes a simulation model to investigate stress, strain, and morphological changes during bending. The analysis indicates that the shift from "projection" to "wrapping" in forming theory is due to the release of boundary degrees of freedom. The forming process can be summarized as the mold's arc characteristics, originating from the chip's corners, gradually replacing the chip's rectangular characteristics along the diagonal, resulting in corresponding stress and strain changes. The "wrapping" bending theory of this method has significant advantages over traditional methods and represents a crucial direction for achieving higher curvature in the future. However, this study found that the use of film pressure can only inhibit out-of-plane deformation to a certain extent, and the buckling phenomenon will still occur when the thinner chip is bent. It prevents the use of thinner chips in the thinning-bending method, so avoiding out-of-plane deformation during the molding process is the direction that needs to be broken in the future.

Robust Pixel Design Methodologies for a Vertical Avalanche Photodiode (VAPD)-Based CMOS Image Sensor.

We present robust pixel design methodologies for a vertical avalanche photodiode-based CMOS image sensor, taking account of three critical practical factors: (i) "guard-ring-free" pixel isolation layout, (ii) device characteristics "insensitive" to applied voltage and temperature, and (iii) stable operation subject to intense light exposure. The "guard-ring-free" pixel design is established by resolving the tradeoff relationship between electric field concentration and pixel isolation. The effectiveness of the optimization strategy is validated both by simulation and experiment. To realize insensitivity to voltage and temperature variations, a global feedback resistor is shown to effectively suppress variations in device characteristics such as photon detection efficiency and dark count rate. An in-pixel overflow transistor is also introduced to enhance the resistance to strong illumination. The robustness of the fabricated VAPD-CIS is verified by characterization of 122 different chips and through a high-temperature and intense-light-illumination operation test with 5 chips, conducted at 125 °C for 1000 h subject to 940 nm light exposure equivalent to 10 kLux.

High-resolution X-Ray imaging of small animal samples based on Commercial-Off-The-Shelf CMOS image sensors.

 An automated system for acquiring microscopic-resolution radiographic images of biological samples was developed. Mass-produced, low-cost, and easily automated components were used, such as Commercial-Off-The-Self CMOS image sensors (CIS), stepper motors, and control boards based on Arduino and RaspberryPi. System configuration, imaging protocols, and Image processing (filtering and stitching) were defined to obtain high-resolution images and for successful computational image reconstruction. Radiographic images were obtained for animal samples including the widely used animal models zebrafish (Danio rerio) and the fruit-fly (Drosophila melanogaster), as well as other small animal samples. The use of phosphotungstic acid (PTA) as a contrast agent was also studied. Radiographic images with resolutions of up to (7±0.6)µm were obtained, making this system comparable to commercial ones. This work constitutes a starting point for the development of more complex systems such as X-ray attenuation micro-tomography systems based on low-cost off-the-shelf technology. It will also bring the possibility to expand the studies that can be carried out with small animal models at many institutions (mostly those working on tight budgets), particularly those on the effects of ionizing radiation and absorption of heavy metal contaminants in animal tissues.

CMOS Image Sensor for Broad Spectral Range with >90% Quantum Efficiency.

Even though the recent progress made in complementary metal-oxide-semiconductor (CMOS) image sensors (CIS) has enabled numerous applications affecting our daily lives, the technology still relies on conventional methods such as antireflective coatings and ion-implanted back-surface field to reduce optical and electrical losses resulting in limited device performance. In this work, these methods are replaced with nanostructured surfaces and atomic layer deposited surface passivation. The results show that such surface nanoengineering applied to a commercial backside illuminated CIS significantly extends its spectral range and enhances its photosensitivity as demonstrated by >90% quantum efficiency in the 300-700 nm wavelength range. The surface nanoengineering also reduces the dark current by a factor of three. While the photoresponse uniformity of the sensor is seen to be slightly better, possible scattering from the nanostructures can lead to increased optical crosstalk between the pixels. The results demonstrate the vast potential of surface nanoengineering in improving the performance of CIS for a wide range of applications.

Development of a Charge-Multiplication CMOS Image Sensor Based on Capacitive Trench for Low-Light-Level Imaging.

This paper presents an electron multiplication charge coupled device (EMCCD) based on capacitive deep trench isolation (CDTI) and developed using complementary metal oxide semiconductor (CMOS) technology. The CDTI transfer register offers a charge transfer inefficiency lower than 10-4 and a low dark current o 0.11nA/cm2 at room temperature. In this work, the timing diagram is adapted to use this CDTI transfer register in an electron multiplication mode. The results highlight some limitations of this device in such an EM configuration: for instance, an unexpected increase in the dark current is observed. A design modification is then proposed to overcome these limitations and rely on the addition of an electrode on the top of the register. Thus, this new device preserves the good transfer performance of the register while adding an electron multiplication function. Technology computer-aided design (TCAD) simulations in 2D and 3D are performed with this new design and reveal a very promising structure.

Silicon Nanowire Phototransistor Arrays for CMOS Image Sensor Applications.

This paper introduces a new design of silicon nanowire (Si NW) phototransistor (PT) arrays conceived explicitly for improved CMOS image sensor performance, and comprehensive numerical investigations clarify the characteristics of the proposed devices. Each unit within this array architecture features a top-layer vertical Si NW optimized for the maximal absorption of incoming light across the visible spectrum. This absorbed light generates carriers, efficiently injected into the emitter-base junction of an underlying npn bipolar junction transistor (BJT). This process induces proficient amplification of the output collector current. By meticulously adjusting the diameters of the NWs, the PTs are tailored to exhibit distinct absorption characteristics, thus delineating the visible spectrum's blue, green, and red regions. This specialization ensures enriched color fidelity, a sought-after trait in imaging devices. Notably, the synergetic combination of the Si NW and the BJT augments the electrical response under illumination, boasting a quantum efficiency exceeding 10. In addition, by refining parameters like the height of the NW and gradient doping depth, the proposed PTs deliver enhanced color purity and amplified output currents.

Evaluation of Microlenses, Color Filters, and Polarizing Filters in CIS for Space Applications.

For the last two decades, the CNES optoelectronics detection department and partners have evaluated space environment effects on a large panel of CMOS image sensors (CIS) from a wide range of commercial foundries and device providers. Many environmental tests have been realized in order to provide insights into detection chain degradation in modern CIS for space applications. CIS technology has drastically improved in the last decade, reaching very high performances in terms of quantum efficiency (QE) and spectral selectivity. These improvements are obtained thanks to the introduction of various components in the pixel optical stack, such as microlenses, color filters, and polarizing filters. However, since these parts have been developed only for commercial applications suitable for on-ground environment, it is crucial to evaluate if these technologies can handle space environments for future space imaging missions. There are few results on that robustness in the literature. The objective of this article is to give an overview of CNES and partner experiments from numerous works, showing that the performance gain from the optical stack is greater than the degradation induced by the space environment. Consequently, optical stacks can be used for space missions because they are not the main contributor to the degradation in the detection chain.

Effects of Hot Pixels on Pixel Performance on Backside Illuminated Complementary Metal Oxide Semiconductor (CMOS) Image Sensors.

Effects of hot pixels on pixel performance in light and dark environments have been investigated in pinned photodiode 0.18 µm backside illuminated CMOS image sensors irradiated by 10 MeV protons. After exposure to protons, hot pixels and normal pixels are selected from the whole pixel array, and their influences on key parameters are analyzed. Experimental results show that radiation-induced hot pixels have a significant impact on pixel performance in dark environments, such as dark signal nonuniformity, long integration time, and random telegraph signal. Hot pixels are caused by defects with complex structures, i.e., cluster defects. Furthermore, the dark current activation energy result confirms that the defects causing the hot pixels have defect energy levels close to mid-gap.

A Feasibility Study on Extension of Measurement Distance in Vision Sensor Using Super-Resolution for Dynamic Response Measurement.

The current civil infrastructure conditions can be assessed through the measurement of displacement using conventional contact-type sensors. To address the disadvantages of traditional sensors, vision-based sensor measurement systems have been derived in numerous studies and proven as an alternative to traditional sensors. Despite the benefits of the vision sensor, it is well known that the accuracy of the vision-based displacement measurement is largely dependent on the camera extrinsic or intrinsic parameters. In this study, the feasibility study of a deep learning-based single image super-resolution (SISR) technique in a vision-based sensor system is conducted to alleviate the low spatial resolution of image frames at long measurement distance ranges. Additionally, its robustness is evaluated using shaking table tests. As a result, it is confirmed that the SISR can reconstruct definite images of natural targets resulting in an extension of the measurement distance range. Additionally, it is determined that the SISR mitigates displacement measurement error in the vision sensor-based measurement system. Based on this fundamental study of SISR in the feature point-based measurement system, further analysis such as modal analysis, damage detection, and so forth should be continued in order to explore the functionality of SR images by applying low-resolution displacement measurement footage.

A 0.5 MP, 3D-Stacked, Voltage-Domain Global Shutter Image Sensor with NIR QE Enhancement, Event Detection Modes, and 90 dB Dynamic Range.

We introduce a compact voltage-domain global shutter CMOS image sensor for a wide range of applications including consumer, IoT, and industrial applications. With 0.5 MP and 2.79 µm pixels packed in a die size of only 2.3 mm × 2.8 mm, the sensor achieves more than 92% quantum efficiency (QE) in the visible wavelength and more than 36% at near-infrared (940 nm) all while drawing a mere 20 mW at a 10-bit, 30-frame-per-second operational mode. In this article, we focus on the architecture of the sensor and the design challenges encountered to fit all the necessary circuitry in such a limited footprint. Moreover, we detail the new solutions we have developed to meet the demanding specifications of low-power operation and high dynamic range (HDR).

Surface Passivation by Quantum Exclusion: On the Quantum Efficiency and Stability of Delta-Doped CCDs and CMOS Image Sensors in Space.

Radiation-induced damage and instabilities in back-illuminated silicon detectors have proved to be challenging in multiple NASA and commercial applications. In this paper, we develop a model of detector quantum efficiency (QE) as a function of Si-SiO2 interface and oxide trap densities to analyze the performance of silicon detectors and explore the requirements for stable, radiation-hardened surface passivation. By analyzing QE data acquired before, during, and after, exposure to damaging UV radiation, we explore the physical and chemical mechanisms underlying UV-induced surface damage, variable surface charge, QE, and stability in ion-implanted and delta-doped detectors. Delta-doped CCD and CMOS image sensors are shown to be uniquely hardened against surface damage caused by ionizing radiation, enabling the stability and photometric accuracy required by NASA for exoplanet science and time domain astronomy.

Constructing a High-Quality t-Se/Si Interface Via In Situ Light Annealing of a-Se for a Self-Powered Image Sensor.

The quality of the interface, e.g., the semiconductor-semiconductor or metal-semiconductor interface, is the main factor restricting the photodetection performance of a heterojunction. In this study, a high-quality Se/Si interface is constructed via in situ directional transformation of amorphous Se (a-Se) into crystalline Se (t-Se) on a Si substrate via light annealing. Benefitting from the high-quality interface and appropriate energy band between Si and Se, the t-Se/Si heterojunction exhibits an extremely high responsivity and detectivity of 583.33 mA W-1 and 8.52 × 1012 Jones at 760 nm, respectively. In addition, the device exhibits an ultrafast rise time of 183 µs and a decay time of 405 µs. Furthermore, an image sensor fabricated via local light annealing successfully recognizes patterns of "N," "P," and "U." This study provides valuable guidance for the construction of high-quality interfaces and the design of self-powered image sensors.

High-throughput field crop phenotyping: current status and challenges.

In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities.

Our aim is to promote the widespread use of electronic insect traps that report captured pests to a human-controlled agency. This work reports on edge-computing as applied to camera-based insect traps. We present a low-cost device with high power autonomy and an adequate picture quality that reports an internal image of the trap to a server and counts the insects it contains based on quantized and embedded deep-learning models. The paper compares different aspects of performance of three different edge devices, namely ESP32, Raspberry Pi Model 4 (RPi), and Google Coral, running a deep learning framework (TensorFlow Lite). All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption. Our findings suggest that ESP32 appears to be the best choice in the context of this application according to our policy for low-cost devices.

A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification.

A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.

Arrayed MoS2 -In0.53 Ga0.47 As van der Waals Heterostructure for High-Speed and Broadband Detection from Visible to Shortwave-Infrared Light.

A high-speed and broadband 5 × 5 photodetector array based on MoS2 /In0.53 Ga0.47 As heterojunction is successfully demonstrated to take full advantage of the type-II band-aligned multilayer MoS2 /In0.53 Ga0.47 As. The fabricated devices exhibit good uniformity in the Raman spectrum and clear rectifying characteristics. The fabricated MoS2 /In0.53 Ga0.47 As photodetectors show good optical performances at a broad wavelength range showing high responsivities corresponding to the detectivity of ≈1010 Jones at -3 V for the incident broadband light from 400 to 1550 nm. A very fast photoresponse is also obtained with a small rise/fall time in the order of microseconds both for visible (638 nm) and shortwave infrared (1310 nm). Finally, the image scanning properties of MoS2 /In0.53 Ga0.47 As devices are demonstrated for visible and infrared light, indicating that the suggested device is one of the promising options for future broadband imager, which can be integrated on the focal plane arrays (FPAs).

Recent Advances in Perovskite Photodetectors for Image Sensing.

In recent years, metal halide perovskites have been widely investigated to fabricate photodetectors for image sensing due to the excellent photoelectric performance, tunable bandgap, and low-cost solution preparation process. In this review, a comprehensive overview of the recent advances in perovskite photodetectors for image sensing is provided. First, the key performance parameters and the basic device types of photodetectors are briefly introduced. Then, the recent developments of image sensors on the basis of different dimensional perovskite materials, including 0D, 1D, 2D, and 3D perovskite materials, are highlighted. Besides the device structures and photoelectric properties of perovskite image sensors, the preparation methods of perovskite photodetector arrays are also analyzed. Subsequently, the single-pixel imaging of perovskite photodetectors and the strategies to fabricate narrowband perovskite photodetectors for color discrimination are discussed. Finally, the potential challenges and possible solutions for the future development of perovskite image sensors are presented.