Image Sensor Array Research Articles

Cell classification with phase-imaging meta-sensors.

The development of photonic technologies for machine learning is a promising avenue toward reducing the computational cost of image classification tasks. Here we investigate a convolutional neural network (CNN) where the first layer is replaced by an image sensor array consisting of recently developed angle-sensitive metasurface photodetectors. This array can visualize transparent phase objects directly by recording multiple anisotropic edge-enhanced images, analogous to the feature maps computed by the first convolutional layer of a CNN. The resulting classification performance is evaluated for a realistic task (the identification of transparent cancer cells from seven different lines) through computational-imaging simulations based on the measured angular characteristics of prototype devices. Our results show that this hybrid optoelectronic network can provide accurate classification (>90%) similar to its fully digital baseline CNN but with an order-of-magnitude reduction in the number of calculations.

Exploring the Efficacy of Nonlinear Filters in CMOS for 2-D Signal Processing for Image Quality Enhancement.

This study rigorously investigates the effectiveness of nonlinear filters in CMOS for 2-D signal processing to enhance image quality. We comprehensively compare traditional linear filters' performance, which operate on the principle of linearity, with nonlinear filters, such as the median-median (Med-Med) approach, designed to handle nonlinear data. To ensure the validity of our findings, we use widely accepted metrics like normalized squared error (NSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) to quantify the differences. Our simulations and experiments, conducted under controlled conditions, demonstrate that nonlinear filters in CMOS outperform linear filters in removing impulse noise and enhancing images. We also address the challenges of implementing these algorithms at the hardware level, focusing on power consumption and chip area optimization. Additionally, we propose a new architecture for the Med-Med filter and validate its functionality through experiments using a 9-pixel image sensor array. Our findings highlight the potential of nonlinear filters in CMOS for real-time image quality enhancement and their applicability in various real-world imaging applications. This research contributes to visual technology by combining theoretical insights with practical implementations, paving the way for more efficient and adaptable imaging systems.

Angle-dependent quantum efficiency correction for improved signal accuracy in small-scale Scheimpflug lidar systems

Wide-incidence-angle detection in Scheimpflug lidar causes pixel-wise variations in signal intensity due to differences in quantum efficiency across the detector. This study proposes an angular response correction factor and a correction method to address the difference caused by a wide incidence angle. A Scheimpflug lidar system was developed with a 405 nm laser and an array image sensor; it achieved a detection angle of 11.4°. Experiments with uniform atmosphere and smoke aerosols demonstrated the effectiveness of the correction method. For uniform atmosphere, it ensured consistent signal intensity. In smoke aerosol detection, the Klett method revealed the correction factor’s role in counteracting small-scale variations in the extinction coefficient. The method improved the extinction coefficient accuracy within 4 m by 4%–75%.

Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing

AbstractIn order to realize the evaluation of salted duck eggs quality, this study constructed salts‐sensitive colorimetric sensor array (CSA) based on silver nanoprisms (AgNPRs) etching principle and utilized hyperspectral imaging (HSI) system to capture the reflectance spectral of CSA and section of salted duck eggs. To minimize the noise in spectral signals, three preprocessing methods including Savitzky–Golay Smoothing (SG), standard normal variate (SNV) transformation, and multiplicative scatter correction were used. The competitive adaptive reweighted sampling and uninformative variables elimination were used to select the optimal wavelengths, and then partial least squares regression was used to predict moisture, lipid, and salt content (SC). Results demonstrated coefficients of determination of 0.8200, 0.8969, 0.8709, and 0.9239 for protein moisture, yolk moisture, yolk lipid, and protein SC. Visualization distribution maps successfully display the distribution of moisture and lipid content during salting periods. The results indicated that the CSA based on AgNPRs etching principle can be an effective tool in quantitative measurement of salt and HSI combining CSA can be applied to evaluate the quality of salted duck eggs comprehensively. The study offers a rapid detection method for the production and processing of salted duck eggs in the industry.Practical applicationA novel salts‐sensitive CSA was constructed to evaluate quality of salted duck eggs based on AgNPRs etching. This study designed and developed a portable sample collection device using 3D printing technology to acquire the information of samples and CSA at the same time. This study combined with HSI system and colorimetric sensor technology to evaluate the quality of salted duck eggs during the salting process.

Active pixel image sensor array for dual vision using large‐area bilayer WS2

AbstractTransition metal dichalcogenides (TMDs) are a promising candidate for developing advanced sensors, particularly for day and night vision systems in vehicles, drones, and security surveillance. While traditional systems rely on separate sensors for different lighting conditions, TMDs can absorb light across a broad‐spectrum range. In this study, a dual vision active pixel image sensor array based on bilayer WS2 phototransistors was implemented. The bilayer WS2 film was synthesized using a combined process of radio‐frequency sputtering and chemical vapor deposition. The WS2‐based thin‐film transistors (TFTs) exhibit high average mobility, excellent Ion/Ioff, and uniform electrical properties. The optoelectronic properties of the TFTs array exhibited consistent behavior and can detect visible to near‐infrared light with the highest responsivity of 1821 A W−1 (at a wavelength of 405 nm) owing to the photogating effect. Finally, red, green, blue, and near‐infrared image sensing capabilities of active pixel image sensor array utilizing light stencil projection were demonstrated. The proposed image sensor array utilizing WS2 phototransistors has the potential to revolutionize the field of vision sensing, which could lead to a range of new opportunities in various applications, including night vision, pedestrian detection, various surveillance, and security systems.image

A high-performance broadband phototransistor array of a PdSe2/SOI Schottky junction.

There is great interest in the incorporation of novel two-dimensional materials into Si-based technologies to realize multifunctional optoelectronic devices via heterogeneous integration. Here, we demonstrate a gate-tunable, self-driven, high-performance broadband phototransistor array based on a PdSe2/Si Schottky junction, which is fabricated by pre-depositing a semi-metallic PdSe2 film on a SOI substrate. In addition, thanks to the zero bandgap of the PdSe2 material and the PdSe2/Si vertical heterostructure, the prepared phototransistor exhibits pronounced photovoltaic properties in a wide spectral range from ultraviolet to near-infrared. The responsivity, specific detectivity and response time of the device at the incident light wavelength of 808 nm are 1.15 A W-1, 9.39 × 1010 Jones, and 27.1/40.3 μs, respectively, which are better than those of previously reported PdSe2-based photodetectors. The photoelectric performance can be further improved by applying an appropriate gate voltage to the phototransistor and the responsivity of the device increases to 1.61 A W-1 at VG = 5 V. We demonstrate the excellent imaging capabilities of a 4 × 4 array image sensor using PdSe2/SOI phototransistors under 375 nm, 532 nm, and 808 nm laser sources.

Enhancing the performance of Self-Powered Deep-Ultraviolet photoelectrochemical photodetectors by constructing α-Ga2O3@a-Al2O3 Core-Shell nanorod arrays for Solar-Blind imaging

With the advancement of Ga2O3-based deep-ultraviolet (DUV) photodetectors (PDs), the integration of PDs into array image sensors has become a sought-after objective. However, while solar-blind imaging research primarily revolves around solid-state Ga2O3-based PDs, there remains a significant gap in the exploration of novel photoelectrochemical (PEC) PDs for solar-blind imaging applications. In this study, self-powered solar-blind PEC-PDs with enhanced performance are fabricated based on α-Ga2O3@a-Al2O3 core–shell nanorod arrays (NRAs). Under DUV irradiation and without external bias, the α-Ga2O3@a-Al2O3 devices demonstrate remarkable superiority over α-Ga2O3 devices. When the light intensity was at 0.50 mW cm−2, the photocurrent density notably rises from 4.60 to 11.24 μA cm−2, accompanied by a corresponding increase in responsivity from 9.60 to 22.70 mA W−1. The enhanced performance is attributed to the α-Ga2O3@a-Al2O3 heterostructure, which establishes a built-in electric field facilitating the separation and directed transport of photogenerated carriers at the interface. Moreover, for the first time, a 5 × 5 matrix of α-Ga2O3@a-Al2O3 core–shell NRAs-based PEC-type PDs is employed to demonstrate a proof-of-concept for self-powered solar-blind imaging, capable of effectively capturing the shapes of the letters “C” and “N”. This work underscores the immense potential of Ga2O3-based PEC-type PDs for future large-area solar-blind imaging applications.

Wafer-scale heterogeneous integration of self-powered lead-free metal halide UV photodetectors with ultrahigh stability and homogeneity

Large-scale growth and heterogeneous integration with existing semiconductors are the main obstacles to the application of metal halide perovskites in optoelectronics. Herein, a universal vacuum evaporation strategy is presented to prepare copper halide films with wafer-scale spatial homogeneity. Benefiting from the electric field manipulation method, the built-in electric fields are optimized and further boost the self-powered UV photodetecting performances of common wide-bandgap semiconductors by more than three orders of magnitude. Furthermore, with effective modulation of the interfacial charge dynamics, the as-fabricated GaN-substrate heterojunction photodetector demonstrates an ultrahigh on/off ratio exceeding 107, an impressive responsivity of up to 256 mA W–1, and a remarkable detectivity of 2.16 × 1013 Jones at 350 nm, 0 V bias. Additionally, the device exhibits an ultrafast response speed (tr/td = 716 ns/1.30 ms), an ultra-narrow photoresponse spectrum with an FWHM of 18 nm and outstanding continuous operational stability as well as long-term stability. Subsequently, a 372-pixel light-powered imaging sensor array with the coefficient of variation of photocurrents reducing to 5.20% is constructed, which demonstrates exceptional electrical homogeneity, operational reliability, and UV imaging capability. This strategy provides an efficient way for large-scale integration of metal halide perovskites with commercial semiconductors for miniature optoelectronic devices.

시각의 구조적, 기능적 특징을 모사한 생체모사 카메라

Conventional imaging and data processing devices may not be ideal for mobile machine vision applications, such as drones and robots, due to the bulky and heavy multi-lens optics used in conventional cameras. Additionally, physical isolation of camera and processors necessitates the capture, transfer, and processing of redundant data, resulting in large power consumption and data latency. Here, we review on bio-inspired cameras inspired by the structural and functional features of biological eyes. We first summarize recent strategies to fabricate curved image sensor (CurvIS) arrays mimicking the curved retina of biological eyes. These CurvIS arrays enable aberration-free imaging with a single lens optics, leading to a miniaturization of camera module. Other optical advantages, such as wide field-of-view and deep depth-of-field, could be also offered by the integrated camera module. Next, we discuss bio-inspired cameras capable of performing in-sensor processing as well as image acquisition. As notable examples, we introduce synaptic optoelectronic devices that can efficiently enhance image contrast and reduce the noise using photon-triggered synaptic plasticity.

Enhanced Performance of Ru-Based Infrared Imaging Sensor Array With Electrospun Thermal Isolation Structure

Predicting and optimizing thermal conductivity of materials used in infrared thermal imaging sensors is an effective method to improve thermal isolation performance, which can substantially reduce the thermal crosstalk among pixels. This paper reports an electrospun P(VDF-TrFE) nanofiber film used as adiabatic support structure in thermal sensor array, and investigates its effect on imaging performance of thermal sensor. Based on solid heat transfer simulation, the thermal conductivities of electrospun P(VDF-TrFE) films with different packing ratios were theoretically calculated, showing a significant decrease compared with the spin-coated counterpart. The temperature increment of thermal sensor using nanofiber film with 43.71% packing ratio is calculated as 1.68 times higher than that using spin-coated film. An average temperature rise of 1.73 times was experimentally obtained, and the thermal response time was also reduced to half, compared with the sensor using spin-coated film. The method of improving thermal isolation performance using electrospun fibrous film could be of great advantage to high sensitivity infrared imaging sensor.

Trajectory tracking method based on Bayesian classifier for pulse array image sensor

A pulse array image sensor (PAIS) is a bionic image sensor that converts light intensity into a pulse sequence to reduce the data volume. The traditional tracking method is not suitable for the sparse data form because of the absence of grayscale information. A trajectory tracking method based on a Bayesian classifier is proposed to maximize the property of the pulse data. First, candidate points with the smallest interval distances are selected in the area of interest. Then the total pulse numbers in a specified period at different positions are gathered to compose the positive and negative feature vectors, which are used to train a naive Bayesian classifier. The classifier can exactly obtain positions from the candidate points, and features in the new tracking positions train and update the classifier. The two-step filtering target point tracking algorithm using the interval distance and Bayesian classifier requires only the raw pulse data without processing, which can maximize the advantages of the special data format and improve computing efficiency. In this way, the position information can be directly obtained from pulse data, and the problem of wasting computing resources on reconstructing grayscale images and treating them in the traditional way is avoided. Experiments were performed on both real filmed data and the public event camera datasets. Our method can obtain trajectories with a high accuracy and long tracking time. The tracking errors are in single digits. In the comparison experiments, although our method has a smaller data volume than other models that obtain both frame and event data, the results still show that it has a comparable performance to the state-of-the-art methods.

Perovskite-based color camera inspired by human visual cells

There are two primary types of photoreceptor cells in the human eye: cone cells and rod cells that enable color vision and night vision, respectively. Herein, inspired by the function of human visual cells, we develop a high-resolution perovskite-based color camera using a set of narrowband red, green, blue, and broadband white perovskite photodetectors as imaging sensors. The narrowband red, green, and blue perovskite photodetectors with color perceptions mimic long-, medium-, and short-wavelength cones cells to achieve color imaging ability. Also, the broadband white perovskite photodetector with better detectivity mimics rod cells to improve weak-light imaging ability. Our perovskite-based camera, combined with predesigned pattern illumination and image reconstruction technology, is demonstrated with high-resolution color images (up to 256 × 256 pixels) in diffuse mode. This is far beyond previously reported advanced perovskite array image sensors that only work in monochrome transmission mode. This work shows a new approach to bio-inspired cameras and their great potential to strongly mimic the ability of the natural eye.

Fast quantum-enhanced imaging with visible-wavelength entangled photons.

Quantum resources can provide supersensitive performance in optical imaging. Detecting entangled photon pairs from spontaneous parametric down conversion (SPDC) with single-photon avalanche diode (SPAD) image sensor arrays (ISAs) enables practical wide-field quantum-enhanced imaging. However, matching the SPDC wavelength to the peak detection efficiency range of complementary metal-oxide-semiconductor (CMOS) compatible mass-producible SPAD-ISAs has remained technologically elusive, resulting in low imaging speeds to date. Here, we show that a recently developed visible-wavelength entangled photon source enables high-speed quantum imaging. By operating at high detection efficiency of a SPAD-ISA, we increase acquisition speed by more than an order of magnitude compared to previous similar quantum imaging demonstrations. Besides being fast, the quantum-enhanced phase imager operating at short wavelengths retrieves nanometer scale height differences, tested by imaging evaporated silica and protein microarray spots on glass samples, with sensitivity improved by a factor of 1.351 ± 0.004 over equivalent ideal classical imaging. This work represents an important stepping stone towards scalable real-world quantum imaging advantage, and may find use in biomedical and industrial applications as well as fundamental research.

High Performance Graphene-C60 -Bismuth Telluride-C60 -Graphene Nanometer Thin Film Phototransistor with Adjustable Positive and Negative Responses.

Graphene is a promising candidate for the next-generation infrared array image sensors at room temperature due to its high mobility, tunable energy band, wide band absorption, and compatibility with complementary metal oxide semiconductor process. However, it is difficult to simultaneously obtain ultrafast response time and ultrahigh responsivity, which limits the further improvement of graphene photoconductive devices. Here, a novel graphene/C60 /bismuth telluride/C60 /graphene vertical heterojunction phototransistor is proposed. The response spectral range covers 400-1800nm; the responsivity peak is 106 A W-1 ; and the peak detection rate and peak response speed reach 1014 Jones and 250 µs, respectively. In addition, the regulation of positive and negative photocurrents at a gate voltage is characterized and the ionization process in impurities of the designed phototransistor at a low temperature is analyzed. Tunable bidirectional response provides a new degree of freedom for phototransistors' signal resolution. The analysis of the dynamic change process of impurity energy level is conducted to improve the device's performance. From the perspective of manufacturing process, the ultrathin phototransistor (20-30nm) is compatible with functional metasurface to realize wavelength or polarization selection, making it possible to achieve large-scale production of integrated spectrometer or polarization imaging sensor by nanoimprinting process.

Stain-Detection and Anti-Stain Algorithms Based on Dual Detector Data Interchange in Image-Type Displacement Measurement Technology

Displacement measurement technology based on image recognition algorithms represents a novel high-performance technique. In such measurements, when there are stains contaminating the measuring light path, it causes recognition errors from the linear array image sensor on the grating, resulting in inaccurate measurement results. Therefore, this report proposes stain-detection and anti-stain algorithms based on image recognition. The principle of image-type displacement measurement technology is described, and the influence of stains on the displacement measurements is analyzed according to four criteria in order to judge whether there are stains present. Then, an anti-stain algorithm based on the data exchange of dual-image sensors is proposed. Finally, experimental verifications are carried out. The experimental results show that the proposed algorithm effectively identifies the errors in the displacement measurements, when the experimental device is contaminated, the developed algorithms can accurately self-correct for the stain’s interference. The results presented herein provide a theoretical basis for developing highly-reliable displacement measurement devices.

Zero-bias Bi-based perovskite image sensor arrays with direct laser-scribing process

We presented a 25 × 25 array imaging sensor based on Cs3Bi2Br9 photodetectors using a laser-scribing technique, which has the advantages of low cost, high accuracy, and being lithography-free.

High-Performance Photodetectors Based on Graphene/MoS₂ Heterojunction FETs

Being an excellent representative of atomically thin 2-D materials, graphene has attracted extensive research attention in the fields of electronic and optoelectronic devices. However, its low-light absorption coefficient, gapless nature, and short carrier lifetime hinder its development in photodetectors. Another atomically thin 2-D semiconducting material molybdenum disulfide (MoS2) has stronger light interaction. Here, we show a high-performance photodetector based on graphene/MoS2 heterojunction field effect transistors (FETs), where the electrodes are fabricated in the middle of two materials. Graphene functions as a conductive material that improves the carrier transmit speed of the device. Meanwhile, MoS2 mainly functions as a light-sensitive material that promotes the generation of photogenerated carriers. The combination of the two materials substantially improves the performance of the photodetector. The photoresponsivity of our device was demonstrated up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.5} \times {10} ^{{4}}$ </tex-math></inline-formula> A/W under a 470-nm light-emitting diode (LED) light illumination, which was much higher than that of a reported photodetector based on monolayer graphene or MoS2 (~mA/W). Moreover, the photoresponsivity of the device could be easily tuned by applying buried-gate, back-gate, and source–drain voltage, which has important implications for the application of 2-D material photodetectors in neural network image sensor arrays. Our study established a method for large-scale preparation of high-performance photodetector arrays based on 2-D materials heterojunctions.

Hetero‐Integrated InGaAs Photodiode and Oxide Memristor‐Based Artificial Optical Nerve for In‐Sensor NIR Image Processing

AbstractIn‐sensor computing is an emerging architectural paradigm that fuses data acquisition and processing within a sensory domain. The integration of multiple functions into a single domain reduces the system footprint while it minimizes the energy and time for data transfer between sensory and computing units. However, it is challenging for a simple and compact image sensor array to achieve both sensing and computing in each pixel. Here, this work demonstrates a focal plane array with a heterogeneously integrated one‐photodiode one‐resistor (1P‐1R)‐based artificial optical neuron that emulates the sensing, computing, and memorization of a biological retina system. This work employs an InGaAs photodiode featuring a high responsivity and a broad spectrum that covers near‐infrared (NIR) signals and employs an HfO2 memristor as the artificial synapse to achieve the computing/memorization in an analog domain. Using the fabricated focal plane array integrated with an artificial neural network, this work performs in‐sensor image identification of finger veins driven by NIR light illumination (≈84 % accuracy). The proposed in‐sensor image computing architecture that broadly covers the NIR spectrum offers widespread application of focal plane array for computer vision, neuromorphic computing, biomedical engineering, etc.

Large-Scale MoS2 Pixel Array for Imaging Sensor.

Two-dimensional molybdenum disulfide (MoS2) has been extensively investigated in the field of optoelectronic devices. However, most reported MoS2 phototransistors are fabricated using the mechanical exfoliation method to obtain micro-scale MoS2 flakes, which is laboratory- feasible but not practical for the future industrial fabrication of large-scale pixel arrays. Recently, wafer-scale MoS2 growth has been rapidly developed, but few results of uniform large-scale photoelectric devices were reported. Here, we designed a 12 × 12 pixels pixel array image sensor fabricated on a 2 cm × 2 cm monolayer MoS2 film grown by chemical vapor deposition (CVD). The photogating effect induced by the formation of trap states ensures a high photoresponsivity of 364 AW-1, which is considerably superior to traditional CMOS sensors (≈0.1 AW-1). Experimental results also show highly uniform photoelectric properties in this array. Finally, the concatenated image obtained by laser lighting stencil and photolithography mask demonstrates the promising potential of 2D MoS2 for future optoelectrical applications.

A Hemispherical Image Sensor Array Fabricated with Organic Photomemory Transistors.

Hemispherical image sensors simplify lens designs, reduce optical aberrations, and improve image resolution for compact wide-field-of-view cameras. To achieve hemispherical image sensors, organic materials are promising candidates due to the following advantages: tunability of optoelectronic/spectral response and low-temperature low-cost processes. Here, a photolithographic process is developed to prepare a hemispherical image sensor array using organic thin film photomemory transistors with a density of 308 pixels per square centimeter. This design includes only one photomemory transistor as a single active pixel, in contrast to the conventional pixel architecture, consisting of select/readout/reset transistors and a photodiode. The organic photomemory transistor, comprising light-sensitive organic semiconductor and charge-trapping dielectric, is able to achieve a linear photoresponse (light intensity range, from 1 to 50W m-2 ), along with a responsivity as high as 1.6 A W-1 (wavelength = 465nm) for a dark current of 0.24 A m-2 (drain voltage =-1.5V). These observed values represent the best responsivity for similar dark currents among all the reported hemispherical image sensor arrays to date. A transfer method wasfurther developed that does not damage organic materials for hemispherical organic photomemory transistor arrays. These developed techniques are scalable and are amenable for other high-resolution 3D organic semiconductor devices.