High-resolution Image Sensors Research Articles

Study of Vertical Phototransistors Based on Integration of Inorganic Transistors and Organic Photodiodes.

We investigate the inorganic/organic hybrid vertical phototransistor (VPT) by integrating an atomic layer deposition-processed ZnO (ALD-ZnO) transistor with a prototype poly(3-hexylthiophene):[6,6]-phenyl-C61-butyric acid methyl ester (P3HT:PC61BM) blend organic photodiode (OPD) based on an encapsulated source electrode geometry, and discuss the device mechanism. Our preliminary studies on reference P3HT:PC61BM OPDs show non-ohmic electron injection between the ALD-ZnO and P3HT:PC61BM layers. However, the ALD-ZnO layer enables the accumulation of photogenerated holes under negative bias, which facilitates electron injection upon illumination and thereby enhances the external quantum efficiency (EQE). This mechanism underpins the photoresponse in the VPT. Furthermore, we demonstrate that the gate field in the VPT effectively modulates electron injection from the ALD-ZnO layer to the top OPD, resulting in the VPT operating as a non-ohmic OPD in the OFF state and as an ohmic OPD in the ON state. Benefiting from the unique transistor geometry and gate modulation capability, this hybrid VPT can achieve an EQE of 45,917%, a responsivity of 197 A/W, and a specific detectivity of 3.4 × 1012 Jones under 532 nm illumination and low drain-source voltage (Vds = 3 V) conditions. This transistor geometry also facilitates integration with various OPDs and the miniaturization of the ZnO channel area, offering an ideal basis for the development of highly efficient VPTs and high-resolution image sensors.

Highly-efficient (>70%) and Wide-spectral (400–1700 nm) sub-micron-thick InGaAs photodiodes for future high-resolution image sensors

This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors (PDs) designed for high-resolution imaging from the visible to short-wavelength infrared (SWIR) spectrum. Conventional approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer (AL). Therefore, we propose a guided-mode resonance (GMR) structure to enhance the quantum efficiency (QE) of the InGaAs PDs in the SWIR region with only sub-micron-thick AL. The TiOx/Au-based GMR structure compensates for the reduced AL thickness, achieving a remarkably high QE (>70%) from 400 to 1700 nm with only a 0.98 μm AL InGaAs PD (defined as 1 μm AL PD). This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high QE. Furthermore, the rapid transit time is highly expected to result in decreased electrical crosstalk. The effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness, simultaneously enhancing the transit time. This breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.

Fuchs’ Endothelial Corneal Dystrophy evaluation using a high-resolution wavefront sensor

This study aims to evaluate the applicability of the high-resolution WaveFront Phase Imaging Sensor (WFPI) in eyes with Fuchs’ Endothelial Corneal Dystrophy (FECD) through qualitative and quantitative analysis using a custom-designed Automatic Guttae Detection Method (AGDM). The ocular phase was measured using the t·\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\cdot$$\\end{document}eyede aberrometer and then was processed to obtain its High-Pass Filter Map (HPFM). The subjects were pathological and healthy patients from the Fundación Jiménez-Díaz Hospital (Madrid, Spain). The AGDM was developed and applied in pupils with 3 and 5 mm of diameter. A set of metrics were extracted and evaluated like the Root-Mean-Square error (RMS), Number of guttae, Guttae Area, and Area of Delaunay Triangulation (DT). Finally, a Support Vector Machine (SVM) model was trained to classify between pathological and healthy eyes. Quantitatively, the HPFM reveals a dark spots pattern according to the ophthalmologist’s description of the slit-lamp examination of guttae distribution. There were significant statistical differences in all the metrics when FECD and Healthy groups were compared using the same pupil size; but comparing both pupil sizes for the same group there were significant differences in most of the variables. This sensor is a value tool to objectively diagnose and monitor this pathology through wavefront phase changes.

Dual Chalcogen-Bonding Interaction for High-Performance Filterless Narrowband Organic Photodetectors.

A novel green-absorbing organic molecule featuring dual intramolecular chalcogen bonds is synthesized and characterized. This molecule incorporates two such bonds: one between a tellurium atom and the oxygen atom of a carbonyl moiety, and the other between the tellurium atom and the adjacent nitrogen atom within a pyridine moiety. The molecule, featuring dual intramolecular chalcogen bonds exhibits a narrow absorption spectrum and elevated absorption coefficients, closely aligned with a resonance parameter of approximately 0.5. This behavior is due to its cyanine-like characteristics and favorable electrical properties, which are a direct result of its rigid, planar molecular structure. Therefore, this organic molecule forming dual intramolecular chalcogen bonds achieves superior optoelectronic performance in green-selective photodetectors, boasting an external quantum efficiency of over 65% and a full-width at half maximum of less than 95nm while maintaining the performance after 1000h of heating aging at 85 °C. Such organic photodetectors are poised to enhance stacked organic photodetector-on-silicon hybrid image sensors, paving the way for the next-generation of high-resolution and high-sensitivity image sensors.

Meta-hybrid IR cut filter for image sensor

We proposed a meta-hybrid infrared (IR) cut filter consisting of metal/dielectric/metal nanodisks covered by a pigment layer. Compared to a conventional pigment-based IR cut filter, the meta-hybrid IR cut filter dramatically reduces the thickness requirement from 1000 to 425 nm. In our design, the simulated blocking factor of the meta-hybrid IR cut filter achieved 28.1. The measured blocking factor is 13.4, which is due to the oxidation of metal in nanodisks. Nevertheless, the measured blocking factor is still three times higher than a pigment layer under the same thickness. We believe the proposed meta-hybrid IR cut filter can be applied to high-resolution imaging sensors.

Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC’s permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.

Freeform metasurface color router for deep submicron pixel image sensors.

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 μm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

High-Density Polyethylene Custom Focusing Lenses for High-Resolution Transient Terahertz Biomedical Imaging Sensors.

Transient terahertz time-domain spectroscopy (THz-TDS) imaging has emerged as a novel non-ionizing and noninvasive biomedical imaging modality, designed for the detection and characterization of a variety of tissue malignancies due to their high signal-to-noise ratio and submillimeter resolution. We report our design of a pair of aspheric focusing lenses using a commercially available lens-design software that resulted in about 200 × 200-μm2 focal spot size corresponding to the 1-THz frequency. The lenses are made of high-density polyethylene (HDPE) obtained using a lathe fabrication and are integrated into a THz-TDS system that includes low-temperature GaAs photoconductive antennae as both a THz emitter and detector. The system is used to generate high-resolution, two-dimensional (2D) images of formalin-fixed, paraffin-embedded murine pancreas tissue blocks. The performance of these focusing lenses is compared to the older system based on a pair of short-focal-length, hemispherical polytetrafluoroethylene (TeflonTM) lenses and is characterized using THz-domain measurements, resulting in 2D maps of the tissue refractive index and absorption coefficient as imaging markers. For a quantitative evaluation of the lens effect on the image resolution, we formulated a lateral resolution parameter, R2080, defined as the distance required for a 20-80% transition of the imaging marker from the bare paraffin region to the tissue region in the same image frame. The R2080 parameter clearly demonstrates the advantage of the HDPE lenses over TeflonTM lenses. The lens-design approach presented here can be successfully implemented in other THz-TDS setups with known THz emitter and detector specifications.

Synthetic Aperture Radar Radio Frequency Interference Suppression Method Based on Fusing Segmentation and Inpainting Networks

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

Enhancing phenotyping efficiency in faba bean breeding: integrating UAV imaging and machine learning

Unmanned aerial vehicles (UAVs) equipped with high-resolution imaging sensors have shown great potential for plant phenotyping in agricultural research. This study aimed to explore the potential of UAV-derived red–green–blue (RGB) and multispectral imaging data for estimating classical phenotyping measures such as plant height and predicting yield and chlorophyll content (indicated by SPAD values) in a field trial of 38 faba bean (Vicia faba L.) cultivars grown at four replicates in south-eastern Norway. To predict yield and SPAD values, Support Vector Regression (SVR) and Random Forest (RF) models were utilized. Two feature selection methods, namely the Pearson correlation coefficient (PCC) and sequential forward feature selection (SFS), were applied to identify the most relevant features for prediction. The models incorporated various combinations of multispectral bands, indices, and UAV-based plant height values at four different faba bean development stages. The correlation between manual and UAV-based plant height measurements revealed a strong agreement with a correlation coefficient (R2) of 0.97. The best prediction of SPAD value was achieved at BBCH 50 (flower bud present) with an R2 of 0.38 and RMSE of 1.14. For yield prediction, BBCH 60 (first flower open) was identified as the optimal stage, using spectral indices yielding an R2 of 0.83 and RMSE of 0.53 tons/ha. This development stage presents an opportunity to implement targeted management practices to enhance yield. The integration of UAVs equipped with RGB and multispectral cameras, along with machine learning algorithms, proved to be an accurate approach for estimating agronomically important traits in faba bean. This methodology offers a practical solution for rapid and efficient high-throughput phenotyping in faba bean breeding programs.

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks.

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks.

The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of layers, and environmental conditions such as humidity and temperature. This study introduces an innovative application of convolutional neural networks (CNNs) for analyzing and classifying images of corrugated boards, particularly those with deformations. For this purpose, a special device with advanced imaging capabilities, including a high-resolution camera and image sensor, was developed and used to acquire detailed cross-section images of the corrugated boards. The samples of seven types of corrugated board were studied. The proposed approach involves optimizing CNNs to enhance their classification performance. Despite challenges posed by deformed samples, the methodology demonstrates high accuracy in most cases, though a few samples posed recognition difficulties. The findings of this research are significant for the packaging industry, offering a sophisticated method for quality control and defect detection in corrugated board production. The best classification accuracy obtained achieved more than 99%. This could lead to improved product quality and reduced waste. Additionally, this study paves the way for future research on applying machine learning for material quality assessment, which could have broader implications beyond the packaging sector.

Design and Implementation of a Hybrid Optical Camera Communication System for Indoor Applications.

Optical wireless communication is a promising emerging technology that addresses the limitations of radio-frequency-based wireless technologies. This study presents a new hybrid modulation method for optical camera communication (OCC), which integrates two waveforms transmitted from a single transmitter light-emitting diode (LED) and receives data through two rolling shutter camera devices on the receiver side. Then, a smart camera with a high-resolution image sensor captures the high-frequency signal, and a low-resolution image sensor from a smartphone camera captures the low-frequency signal. Based on this hybrid scheme, two data streams are transmitted from a single LED, which reduces the cost of the indoor OCC device compared with transmitting two signals from two different LEDs. In the proposed scheme, rolling-shutter orthogonal frequency-division multiplexing is used for the high-frequency signals, and M-ary frequency-shift keying is used for the low-frequency signals in the time domain. This proposed scheme is compatible with smartphone and USB cameras. By controlling the OCC parameters, the hybrid scheme can be implemented with high performance for a communication distance of 10 m.

Energy-Band Engineering By Remote Doping of Self-Assembled Monolayers Leads to High-Performance Igzo/P-Si Heterostructure Photodetectors

Advancements in emerging technologies, such as autonomous driving, artificial intelligence, medical imaging, and augmented reality, have increased the demand for high-resolution image sensors to transmit further accurate color information. To meet the performance requirements of these applications, several research have been accelerating towards the utilization of emerging low-dimensional semiconductor materials, such as metal oxides, transition metal dichalcogenides, perovskites, and other organic materials.Among them, metal oxide semiconductors (MOSs) are potential candidates for developing high-performance photodetectors. It can be synthesized over large areas using a simple/low-temperature process and are compatible with conventional fabrication equipment. However, MOS-based photodetectors still have several limitations, 1) the presence of charge traps and operational instability on the oxide surface, which degrade photodetection performance, and 2) the absence of an appropriate doping technique for MOS has blocked to improve photodetection performance via materials characteristic modification method.In this study, we present a strategy to overcome these limitations and significantly enhance the performance of IGZO, which is representative of oxide semiconductor thin film, based photodetector with high operation stability and sustainability under ambient atmospheres. We utilized energy-band engineering through an octadecylphosphonic acid (ODPA) self-assembled-monolayer (SAM)-based doping treatment to enhance the photoelectric characteristics of IGZO/p-Si hetero-interfaced devices (ISH). The ODPA consists of a head group of the hydroxyl group, which forms the self-assembled covalent bond formation of R–P–O–substrate. Combining the ODPA SAM with the oxide surface of IGZO provided carrier concentration control based on their a dipole effect, and remain the performance and functionality through the surface encapsulation. As a result, under a light-emitting diode (LED) wavelength with an intensity of 3.9 mW, the proposed device exhibited remarkable photo-switching current ratio of 4.33 × 103 A/A and negative differential resistance (NDR) phenomenon. Espacially, the dark current of the ODPA treated ISH is decreased about 10 times lower than as-fabricated ISH, which shows the possibility of signal noise minimization. These performance improvement is expected to be derived from band properties modulation through IGZO layer annealing and ODPA doping. To clarify the doping effect of each treatment, we characterized the ODPA self-assembled monolayer using X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and Kelvin probe force microscopy (KPFM) to provide comprehensive analysis of the heterointerface effects. We also characterized the electrical properties of IGZO thin-film transistors (TFTs) based on variations of the annealing temperature to clarify the ODPA doping effect.We evaluated 156 ODPA-treated IGZO/p-Si heterostructure photodetectors, which were scaled for fabrication on a 4 inch wafer. The devices exhibited a 100% yield, and uniform current behavior in the dark state (0.20 ± 0.06 nA) and light state (0.87 ± 0.15 µA), owing to an evenly deposited IGZO layer and a simple-to-treat ODPA doping scheme. Furthermore, consecutive sweeps of 20,000 cycles verified excellent cycle-to-cycle endurance, resulting in highly robust photoelectric behaviors without operational failure. Additionally, the performance of the ODPA-treated, IGZO/p-Si heterostructure photodetector remained unchanged for 237 days without degradation under atmospheric exposure (relative humidity (RH) = 37–53 % and 20.6–23.1 °C). Acknowledgements This research was supported by funding supports from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C1101647) and Korea government (MEST) (NRF-2017R1A2B3011222). Figure 1

STRATEGY ON HIGH-DEFINITION POINT CLOUD MAP CREATION FOR AUTONOMOUS DRIVING IN HIGHWAY ENVIRONMENTS

Abstract. In recent years, a lot of researchers have been trying the development of efficient ways to create HD maps with centimeter-level precision. Mobile mapping system (MMS) produce 3D HD point cloud map of the surrounding by integrating navigation (i.e., direct georeferencing or DG process) and high-resolution imaging sensor data. Unfortunately, in partially environments, the provided accuracy of the GNSS system degrades dramatically. In order to constraint the drift and correct the georeferenced point cloud map, ground control points (GCPs) are placed along the road. Moreover, there are approaches which use laser-based point cloud registration techniques to construct the point cloud map. However, all promising mapping techniques which currently use the laser as the core sensor for mapping the high-definition point cloud map may not be promised to construct the point cloud map in partially or unfriendly environments. As a literature review and result, a suitable approach for creating the promising point cloud map is to combine the INS/GNSS navigation solution, LiDAR matching techniques, and GCPs. Thus, this study introduces the HD point cloud map generation method that can potentially help researchers create personalized and globalized HD point cloud maps and develop new HD point cloud map generation methodologies.

Anti-Aliasing and Anti-Color-Artifact Demosaicing for High-Resolution CMOS Image Sensor

Anti-Aliasing and Anti-Color-Artifact Demosaicing for High-Resolution CMOS Image Sensor

A 3.06 μm Single-Photon Avalanche Diode Pixel with Embedded Metal Contact and Power Grid on Deep Trench Pixel Isolation for High-Resolution Photon Counting.

In this study, a 3.06 μm pitch single-photon avalanche diode (SPAD) pixel with an embedded metal contact and power grid on two-step deep trench isolation in the pixel is presented. The embedded metal contact can suppress edge breakdown and reduce the dark count rate to 15.8 cps with the optimized potential design. The embedded metal for the contact is also used as an optical shield and a low crosstalk probability of 0.4% is achieved, while the photon detection efficiency is as high as 57%. In addition, the integration of a power grid and the polysilicon resistor on SPAD pixels can help to reduce the voltage drop in anode power supply and reduce the power consumption with SPAD multiplication, respectively, in a large SPAD pixel array for a high-resolution photon-counting image sensor.

A Comprehensive Methodology for Optimizing Read-Out Timing and Reference DAC Offset in High Frame Rate Image Sensing Systems.

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor's operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e-rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e-·nJ. The sensor utilized a one-row read-out time of 6.9 µs, an amplifier bandwidth of 1.1 MHz, and a reference digital-to-analog converter (DAC) offset of 512 LSB. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.

A compact model for the well-depleted 1-T pixel sensor based on silicon-on-insulator technology

The silicon-on-I_nsulator (SOI) based one transistor (1-T) pixel sensor is a promising candidate for a high-resolution image sensor and in-sensor computing. In this work, a compact model for the photoresponse of a well-depleted SOI 1-T pixel sensor is proposed from the perspective of the physical process. The model explains the photoelectron accumulation, photoelectron redistribution, and modulation of the transistor according to the electron drift/diffusion mechanism, capacitor–voltage relationship, and current solution from BSIM-IMG. The model agrees well with the experimental results from the 1-T pixel sensor fabricated by the 22 nm fully depleted SOI technology, as well as the simulation results from technology computer-aided design. The extraction scheme of the model parameters is also provided to facilitate the use of the model.

High-performance photodetectors based on two-dimensional perovskite crystals with alternating interlayer cations

Organic-inorganic halide perovskite, as a low-cost, solution-processable material with remarkable optoelectronic properties, is ideal candidate to fabricate high-performance photodetectors and is expected to significantly reduce device costs. Compared to the common Dion-Jacobson and Ruddlesden-Popper two-dimensional (2D) layered hybrid perovskite compounds, the perovskites with alternating cations in the interlayer (ACI) phase show higher crystal symmetry and narrower optical bandgaps, which exhibit great potential for excellent photodetection performance. Herein, we report a high-performance photodetector based on the 2D bilayered hybrid lead halide perovskite single crystal with the ACI phase (GAMA2Pb2I7; GA = C(NH2)3 and MA = CH3NH3). The single-crystal photodetector exhibits high photoresponsivity of 1.56, 2.54, and 2.60 A/W for incident light wavelengths of 405, 532, and 635 nm under 9.82 nW, respectively, together with the correspondingly high detectivity values of 1.86 × 1012, 3.04 × 1012, and 3.11 × 1012 Jones under the same operating conditions. Meanwhile, a high-resolution imaging sensor is built based on the GAMA2Pb2I7 single-crystal photodetector, confirming the high stability and photosensitivity of the imaging system. These results show that the 2D hybrid lead halide perovskites with alternating interlayer cations are promising for high-performance visible light photodetectors and imaging systems.