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随着大数据和人工智能技术的飞速发展, 图像传感器向着多光学维度高质量成像和智能化信息处理方向发展. 传统的图像传感器架构由于感存算分离在处理指数级增长的视觉信息时面临存储墙和功耗墙瓶颈. 近年来, 基于二维材料的光电探测器在性能提升方面取得了显著的进展, 并与传感器内计算技术相结合, 为图像在传感器内智能处理开辟了新路径. 本文系统地综述了高性能二维材料光电探测器及图像智能处理技术的最新进展. 首先, 介绍了二维材料光电探测器的感知特性及其关键性能指标; 随后, 探讨了探测器内图像预处理方法; 接着, 总结了基于二维材料器件的传感器内计算技术及其在各类神经网络中的创新应用; 最后, 分析了利用二维材料开发新一代图像处理器件所面临的挑战与机遇.This paper provides a comprehensive review of recent advances in high-performance photodetectors based on two-dimensional materials and in-sensor computing for intelligent image processing, aiming to address the challenges of the “memory wall” and “power wall” caused by the separation of sensing, storage, and computing in traditional image sensors. Traditional image processing relies on the von Neumann architecture, where a large volume of raw data generated at the sensing end must be transmitted to independent computing units or cloud platforms for processing, leading to high energy consumption, significant latency, bandwidth burden, and security risks. Owing to their atomic thickness, high carrier mobility, weak short-channel effects, and tunable optoelectronic properties, two-dimensional (2D) materials provide an ideal physical foundation for achieving function integration of perception and computation. This paper discusses the topic from three perspectives: optical signal perception, image preprocessing, and advanced image processing. In terms of optical signal perception, 2D materials and their heterostructures exhibit ultrahigh responsivity, broadband operation, and fast response in light-intensity detection, enable miniaturized spectrometers through bandgap modulation and computational spectroscopy, and achieve compact, full-polarization analysis via twisted layers and metasurface structures. In image preprocessing, 2D material devices can perform convolution and feature extraction at the sensing end through linear photoresponse, suppress noise and extend dynamic range via superlinear and sublinear responses, and mimic biological visual adaptation in spectral and polarization domains to enhance image quality and robustness. In advanced image processing, the tunable photoresponse and memristive characteristics of 2D materials enable sensor-level integration of sensing, storage, and computation, This allows for the realization of matrix-vector multiplication and convolution operations within convolutional neural networks, significantly reducing power consumption and improving efficiency. Meanwhile, by implementing spike-rate and temporal encoding of optical signals in spiking neural networks, 2D material devices can achieve event-driven image recognition and classification under low-power and low-latency conditions. Furthermore, this paper highlights the challenges faced by 2D material image sensors, including scalable fabrication, heterogeneous integration with silicon technology, array- and circuit-level optimization, environmental stability and encapsulation, and system-level implementation, while envisioning their broad application prospects in intelligent imaging, wearable electronics, autonomous driving, and biomedical diagnostics. It is concluded that with the joint progress in materials science, device engineering, and artificial intelligence, 2D materials are expected to drive the development of next-generation low-power, high-performance, intelligent image processing platforms, and to become an essential foundation for future information perception and processing technologies.
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Keywords:
- two-dimensional materials /
- photodetectors /
- in-sensor computing /
- artificial neural networks
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图 3 (a) 学习过程示意图. BP的光响应可以通过两个平行金属板之间的电位移场(D)进行调节[52]; (b) BP光谱仪的响应矩阵RD,λ, 它依赖于外加电位移场(D)和入射光的波长(λ), 是通过对一组已知光谱(如图(a)中的红色、绿色和蓝色曲线在不同电场下)所产生的光响应进行“学习”而得出的[52]; (c) 采样过程示意图. 对于未知光谱的入射光, BP探测器在不同电位移场下测量光电流, 即进行采样; (d) 采样过程会生成一个光电流向量ID[52]; (e) 三种重建后的光谱示意图, 从上到下分别表示: 一个窄带发射光谱、一个宽带发射光谱, 以及一种气体分子的吸收特征光谱. 未知光谱的重建依赖于之前步骤中得到的响应矩阵RD,λ和电流向量ID(出自文献[52], 已获得授权); (f) 光谱仪示意图[53]; (g) MoS2/WSe2 在不同波长的光照射下的转移曲线, 固定功率为20 mW[53]; (h) 重建光谱和参考光谱之间的峰值信噪比与学习步骤的函数关系[53]; (i) 使用空间扫描方法的单结光谱仪配置光谱成像. 用彩色图像过滤的宽带光源入射到光谱仪进行光谱成像(出自文献[53], 已获得授权)
Fig. 3. (a) Schematic of the learning process. The photoresponse of BP can be tuned by the electric displacement (D) between two parallel metallic plates[52]; (b) the responsivity matrix RD,λ of the BP spectrometer, which depends on the biasing displacement field (D) and the incident light wavelength (λ), is ‘learned’ from the photoresponses to a group of known spectra (illustrated by the red, green and blue curves in a under different biasing displacement fields)[52]; (c) schematic of the sampling process. For the incident light with an unknown spectrum, the photocurrent in the BP detector is sampled as a function of the electric displacement D[52]; (d) the sampling process generates the photocurrent vector ID[52]; (e) schematics of three reconstructed spectra. From top to bottom, the spectra represent a narrowband emission, a broadband emission and an absorption feature of certain gas molecules. Reconstruction of the unknown spectra leverages the RD,λ and ID as discussed in panel (a)–(d). Reproduced with permission from Ref.[52]; (f) schematic diagram of the spectrometer[53]; (g) transfer curves of MoS2/WSe2 under different wavelengths of light illumination, with a fixed power of 20 mW[53]; (h) relationship between the peak signal-to-noise ratio of the reconstructed spectrum and the reference spectrum as a function of the learning step[53]; (i) spectral imaging with a single-junction spectrometer configuration using spatial scanning method. Broadband light filtered through a color image is incident on the spectrometer for spectral imaging. Reproduced with permission from Ref.[53].
图 4 (a) 双扭曲的黑砷磷(b-AsP)同质结证明可以同时检测完整的线性偏振态和强度信息(出自文献[60], 已获得授权); (b) 石墨烯上的双臂超表面结构可以将不同波长和旋性的光定位在双臂的两侧, 产生具有不同大小和方向的矢量光电流. CPL, LCP和RCP分别代表圆偏振光、左旋圆偏振光和右旋圆偏振光[62]; (c) 提取三波长圆偏振信号的示意图. 在混合入射光的情况下, 光电压信号编码的处理允许提取入射光的波长和圆偏振信息(出自文献[62], 已获得授权)
Fig. 4. (a) Twisted b-AsP homojunctions demonstrated simultaneous detection of full linear polarization states and intensity information. Reproduced with permission from Ref.[60]; (b) the dual-arm metasurface structure on graphene can localize light of different wavelengths and handedness on either side of the dual arms, generating vectorial photocurrents with varying magnitudes and directions. CPL, LCP, and RCP respectively represent circularly polarized light, left-handed circularly polarized light, and right-handed circularly polarized light[62]; (c) schematic representation of the extraction of three-wavelength circularly polarized signals. In the case of mixed light incidence, the processing of photovoltage signal encoding allows for the extraction of the wavelength and circular polarization information of the incident light. Reproduced with permission from Ref.[62].
图 5 (a) MoS2光电晶体管原理图. 插图: 单个MoS2的光学显微镜图像光电晶体管[66]; (b) 明、暗背景下8 pixel×8 pixel阵列的示意图以及“8”随时间变化过程(出自文献[66], 已获得授权); (c) 高噪声图像示例, 其中信号目标被噪声完全掩盖, SNR约为0.7; (d) 传统CCD相机+存储+软件对数变换处理路线与CIPS阵列直接输出的结果(出自文献[67], 已获得授权)
Fig. 5. (a) Schematic diagram of the MoS2 phototransistor. Inset: optical microscope image of a single MoS2 phototransistor; (b) schematic diagram of an 8×8 pixel array under bright and dark backgrounds, and the temporal variation process of "8". Reproduced with permission from Ref.[66]; (c) example of a highly noisy image in which the signal target is completely obscured by noise, with a SNR of approximately 0.7; (d) comparison between the traditional CCD camera+storage+software logarithmic transformation processing route and the direct output from the CIPS array. Reproduced with permission from Ref.[67].
图 6 (a) 器件结构的横截面示意图和扫描电子显微镜(scanningelectron microscope, SEM)图像. 比例尺, 200 nm[68]; (b) 光谱自适应视觉系统的操作方案. 图(i)和图(ii)显示了光谱自适应视觉传感器对典型I型和II型特征进行成像的示例, 图(iii)勾勒了双通道人工神经网络的架构(出自文献[68], 已获得授权); (c) 仿生双层PtSe2自发性色度适应的装置. O2-介导的双层PtSe2器件可以同时将传感、记忆和处理功能应用于彩色图像[69]; (d) 波长依赖性正负非易失性光电导示意图, 它模拟了彩色图像的拮抗感受野和突触细胞的记忆[69]; (e) 卷积神经网络(convolutional neural network, CNN)结构和不同的数据集. Origin和HSV(分别代表色相(hue)、饱和度(saturation)和明度(value), HSV)是多通道合成图像. 红色、蓝色和绿色图像是根据其伪彩色图像(原点)提取的三个通道. Hue是HSV图像的波长通道[69]; (f) 同一网络结构下四个数据集的分类结果. 波长相关结果显示平均准确率为93.55%. 伪彩色通道的性能为60.48%(蓝色), 62.10%(绿色)和73.39%(红色)(出自文献[69], 已获得授权)
Fig. 6. (a) Cross-sectional schematic and SEM image of the device structure. Scale bar, 200 nm[68]; (b) operational scheme of the spectra-adapted vision system. Panels (i) and (ii) show examples of the imaging of typical type-I and type-II features by the spectra-adapted vision sensor, and panel (iii) sketches the architecture of the two-channel artificial neural network. Reproduced with permission from Ref.[68]; (c) ioinspired bilayer PtSe2 device for spontaneous chromatic adaptation. The O2-mediated bilayer PtSe2 device can simultaneously apply sensing, memory, and processing functions to color images[69]; (d) schematic of wavelength-dependent positive and negative nonvolatile photoconductivity, which mimics the antagonistic receptive field of a colored image and the memory of synaptic cells[69]; (e) CNN structure and different datasets. Origin and HSV are multichannel composite images. The red, blue, and green images are the three channels extracted on the basis of their pseudocolor image (origin). Hue is the wavelength channel of the HSV image[69]; (f) classification results of four datasets under the same network structure. The wavelength-dependent results show an average accuracy of 93.55%. The performance of the pseudocolor channels is 60.48% (blue), 62.10% (green), 73.39% (red). Reproduced with permission from Ref.[69].
图 7 (a) PdSe2/WSe2异质结器件原理图[74]; (b) 图像边缘提取卷积过程, 在980 nm 偏振激光照射下进行(出自文献[74], 已获得授权); (c) PSOS器件的原理图和工作原理; (d) 分别在应用噪声之前和(e) 在应用50%盐胡椒噪声之前使用STP定义的边缘检测(垂直特征提取)识别MNIST数据集中手写数字“6”的图像(出自文献[75], 已获得授权)
Fig. 7. (a) Schematic diagram of the PdSe2/WSe2 heterojunction device[74]; (b) Convolution process for image edge extraction under 90 nm polarized laser irradiation. Reproduced with permission from Ref.[74]; (c) schematic diagram and working principle of the PSOS device[75]; (d) recognition images of handwritten digit “6” in the MNIST dataset using the STP-defined edge detection (vertical feature extraction) before applying noise and (e) after applying 50% salt pepper noise, respectively. Reproduced with permission from Ref.[75].
图 8 (a) PdSe2/MoTe2异质结构装置原理图. 插图显示了PdSe2/MoTe2异质结构的光学图像[90]. (b) 在波长为980 nm(由阴影区域表示)下不同光强度下Vg = –10 V和10 V的正负光响应[90]. (c) 宽带卷积计算机制的示意图, 该机制从宽带光学输入中得出加权电输出. 宽带核值可以用光响应度(Rj)表示, 可通过Vg进行调节. 像素值相当于光功率(Pj), 输出光电流可以用卷积运算(Pj, Vg). 插图显示了每个像素的门控可调权重的概念, 该权重得出加权的正和负光响应度(出自文献[90], 已获得授权). (d) 单个WSe2光电二极管的示意图. 该器件在短路条件下工作, 其光响应度通过向底部栅极电极施加一对电压VG/–VG来设置[80]. (e) 安装在芯片载体上的整体芯片的宏观图像. 第一层放大图: 包含3 pixel×3 pixel的光电二极管阵列的显微镜图像. 第二层放大图: 某个像素的扫描电子显微镜图像. 每个像素由三个WSe2光电二极管(子像素)组成, 其光响应度通过栅压设置[80]. 分类器(f)和自动编码器(g)的原理图(出自文献[80], 已获得授权)
Fig. 8. (a) Schematic of the PdSe2/MoTe2 heterostructure device. The inset shows an optical image of the PdSe2/MoTe2 heterostructure[90]. (b) Positive and negative photoresponses under different light intensities at a wavelength of 980 nm (indicated by shadow areas) at Vg = –10 V and 10 V, respectively[90]. (c) Schematic of the mechanism for the broadband convolution calculation that derives a weighted electrical output from broadband optical inputs. The broadband kernel values can be represented by photoresponsivity (Rj), which can be tunable by Vg. The input layer is represented by the incident light. The pixel values are equivalent to optical power (Pj), and the output photocurrent can be represented with convolution operation conv (Pj, Vg). The inset shows the concept of gate-tunable weights for each pixel that derives the weighted positive and negative photoresponsivity. Reproduced with permission from Ref.[90]. (d) Schematic of a single WSe2 photodiode. The device is operated under short-circuit conditions and the photoresponsivity is set by supplying a voltage pair VG/–VG to the bottom-gate electrodes[80]. (e) Macroscopic image of the bonded chip on the chip carrier. First magnification: microscope image of the photodiode array, which consists of 3 pixel×3 pixel. Second magnification: scanning electron microscopy image of one of the pixels. Each pixel consists of three WSe2 photodiodes/subpixels with responsivities set by the gate voltages. Schematics of the classifier (f) and the autoencoder (g). Reproduced with permission from Ref.[80].
图 9 (a) 2D b-AsP/MoTe2 vdWs异质结构模拟视网膜, 在随机NIR光终端的帮助下实现MIR对象的集成感知和编码功能. 编码的尖峰信号可以从源极-漏极电流(IDS)低于阈值电流(ITC)用于脉冲神经网络的输入以进行信息处理(出自文献[101], 已获得授权); (b) 器件结构示意图, 从下到上依次为p++-Si, SiO2, SiNx and MoS2; (c) 用于编码和训练的SNN基本结构; (d) 基于TTFS编码规则与速率编码规则, 对同一图像进行推理时, 识别准确率随训练轮次变化的比较, 以及总脉冲数的对比(出自文献[102], 已获得授权)
Fig. 9. (a) The 2D b-AsP/MoTe2 vdWs heterostructure simulates the retina, enabling integrated perception and encoding of MIR objects with the help of random NIR light terminals. The encoded spike signals can be used as inputs for the spiking neural network by passing through the source-drain current (IDS) below the threshold current (ITC) for information processing. Reproduced with permission from Ref.[101]; (b) the schematic diagram of the device structure, from bottom to top is: p++-Si, SiO2, SiNx and MoS2[101]; (c) basic structure of the SNN used for encoding and training[102]; (d) the comparison of recognition accuracy evolution with training epochs and total spike counts for the inference of the same image based on the TTFS coding rule and rate coding rule. Reproduced with permission from Ref.[102].
表 1 二维材料光谱仪和商用光谱仪对比
Table 1. Comparison between two-dimensional material spectrometers and commercial spectrometers.
下载: 导出CSV
表 2 二维材料阵列可扩展性与均匀性(其中σ/μ表示标准差和均差的比值, 用于比较响应度是否均匀)
Table 2. Scalability and uniformity of 2D material arrays (where σ/μ represents the ratio of the standard deviation to the mean, and is used to compare whether the responsivity is uniform).
下载: 导出CSV
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