Organic Photoelectric Synaptic Materials, Devices and Applications
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摘要: 随着大数据和物联网（IoTs）的迅猛发展，人工智能（AI）技术受到了广泛关注。可克服冯·诺依曼瓶颈和提高串行计算机性能的光电神经形态器件在半导体器件和集成电路领域逐渐发展迅猛。光信号具有低功耗、低串扰、高带宽和低计算要求等优点，可视为额外端口以丰富突触可塑性的调节自由度。光电器件的光电性能在很大程度上依赖于光电材料的设计制备。其中，有机材料具备分子多样性、成本低、易加工、机械柔韧性和与柔性基板兼容等优点，是构建高性能光电突触器件的重要材料载体。本文从有机材料出发，介绍了其在光电器件和视觉仿生领域应用的最新进展，并讨论了当前的应用挑战和未来发展趋势。Abstract: With the advent of big data and the Internet of Things (IoTs), artificial intelligence (AI) has received great attention from the global scientific and industrial communities. Photoelectric neuromorphic devices, which can overcome the Von Neumann bottleneck issue of the conventional computer systems, are developing repidly. The optical signal, which has the advantages of low power consumption, low crosstalk, high bandwidth and low computational requirements, can be regarded as an additional terminal to enrich the regulatory freedom of synaptic plasticity. The optoelectronic performance of optoelectronic devices largely depends on the design and preparation of optoelectronic materials. With the advantages of molecular diversity, low cost, easy processing, mechanical flexibility and compatibility with flexible substrates, organic materials are important materials platform for constructing high performance optoelectronic synaptic devices. In this review, the latest development of organic materials in optoelectronic devices and visual bionics is introduced, and the current application challenges and future prospects of organic materials are discussed.
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图 1 用于光电器件的代表性有机小分子的化学结构式
Figure 1. Chemical structures of small organic molecules used in optoelectronic devices
图 2 C8-BTBT光电二极管的示意图（a）[10]；黑暗和光照（9 μW/cm2）条件下光电二极管上测得的I-U曲线（b）[10]；紫外可见吸收光谱（c）[11]；C8-BTBT: PC61BM光电晶体管输出电流随栅压和光功率密度的变化图（d）[12]；不同光波长照射下，DPPT-TT/TFP: PS光电晶体管光响应度与栅压的关系图（e）[13]以及R和D*与光功率密度（Pw）的相关函数（f）[12]
Figure 2. Schematic diagram of a photodiode based on C8-BTBT （a）[10]；I-U curve measured on the photodiode under dark and light （9 μW/cm2） conditions （b）[10]; UV-visible absorption spectrum （c）[11]; Evalution of the photocurrent of the C8-BTBT: PC61BM based phototransistor vs. gate voltage and optical power density （d）[12]; The relationship between the photoresponsivity and gate voltage of the DPPT-TT/TFP: PS phototransistor under different wavelengths of the incident light （e）[13]and variation of the photoresponsivity and detectivity with the optical power intensity （f）[12]
图 4 Au/PMMA:Azo-Au NPs/ITO忆阻器件的结构示意图（a），典型I-U曲线（b）以及黑暗条件和UV照射30 min后30个器件的SET电压分布（c）[24]；ITO/ZnO/PDR1A/Al光电忆阻器的结构示意图（d），圆偏振光和线偏振光照射20 min前后的I-U曲线（e）以及高低阻态阻值在圆偏振和线偏振光照射下的可逆转变（f）[25]；Al/PVP-NCQD/ITO存储器件的结构示意图（g）以及不同UV光照时间下高低阻态阻值的演化（h）[26]
Figure 4. Gragh of the Au/PMMA: Azo-Au NPs/ITO memristor structure （a）, typical I-U curves （b） and SET voltage distribution tested under dark and UV irradiation conditions after 30 min （c）[24]; Schematic illustration of the ITO/ZnO/PDR1A/Al optical memristor device （d）, I-U curves tested before irradiation and after irradiation for 20 min with circularly polarized light and linearly polarized light （e） and reversible conversion of HRS and LRS under the repeated irradiation of circularly and linearly polarized light （f）[25]; Schematic diagram of the Al/PVP-NCQD/ITO memory device （g） and its HRS/LRS resistances evolution under different UV light time （h）[26]
图 5 有机-无机杂化钙钛矿材料CH3NH3PbI3的晶体结构（a）；Au/CH3NH3PbI3- xClx /FTO忆阻器的结构示意图（b）及其低压光辅助开关行为（c）[32]；G-PQD复合材料的上层结构示意图（d）、光电晶体管的光谱响应度（e）以及归一化光电流响应（f）[33]；Au/CH3NH3PbI3- xClx /FTO忆阻器的微观开关机制（g）[32]；光激发和光栅作用下G-PQD上部结构的能级图（h）[33]
Figure 5. Molecular formula of organic-inorganic hybrid perovskite CH3NH3PbI3 （a）; Schematic device structure of Au/CH3NH3PbI3- xClx /FTO memristor （b） and its photoassisted low-voltage switching behavior （c）[32]; Schematic illustration of the G-PQD superstructure （d）, its spectral responsivity of the phototransistor device （e）, normalized photocurrent response （f）[33]; microscopic switching mechanism of Au/CH3NH3PbI3- xClx /FTO memristor （g）[32]; Energy band diagram of the G-PQD superstructure under photoexcitation and photogating operations （h）[33]
图 6 基于Graphene-MOFs复合材料的光电探测器示意图（a）、与激发波长相关的光响应和吸收光谱（b）以及不同光强的325nm光照下器件的光电流和电压的关系曲线（c）[37]；Fe3（THT）2（NH4）3 二维MOF薄膜的单层示意图（d）、两端光电探测器装置示意图（e）以及785nm光照下器件的温度相关的响应曲线（f）[41]
Figure 6. Schematic diagram of the Graphene-MOF based photodetector （a）, excitation wavelength dependent photoresponsivity and absorption spectra （b） and photocurrent-voltage characteristics under 325 nm illumination with different optical power （c）[37]; Schematic illustration of a monolayer of Fe3（THT）2（NH4）3 2D MOF film （d）, its two-terminal photodetector （e） and temperature-dependent photoresponse as a function of time under pulsed illumination of 785 nm laser （f）[41]
图 7 人类视觉系统示意图（a）[44]；单个光脉冲触发的EPSC行为（b）、成对光脉冲触发的EPSC行为（c）、PPF指数随光脉冲间隔时间（Δt）的变化曲线（d）[11]；光子突触中光脉冲强度（e）、持续时间（f）和频率（g）相关的STP或LTP特性[47]；全光刺激的对称赫布STDP学习行为（h）[50]；光学可调的非对称赫布STDP学习行为（i）[25]；光电协同刺激的非对称赫布STDP学习行为（j）[51]
Figure 7. Schematic diagram of the human visual system （a）[44]; EPSC behavior triggered by a single light pulse （b）, EPSC behavior triggered by a pair of light pulses （c） and PPF index as the function of light pulse interval （ΔT） （d）[11]; Input light pulse intensity （e）, duration （f） and frequency （g） dependent formation of STP or LTP characteristics[47]; Symmetrical Hebbian STDP learning behavior stimulated by all-light pulses （h）[50]; Optically adjustable asymmetric Hebbian STDP learning rule （i）[25]; Asymmetric Hebbian STDP learning behavior stimulited by light and electric （j）[51]
图 8 视网膜多层结构的示意图和LOND的3D结构示意图（a）；不同光波长下人工视觉系统的颜色感知功能及其特征保留时间（b）[55]；柔性PI基板上光突触器件阵列的图像识别和记忆能力（c）[57]；有机光电突触（光电探测器和人工突触）和神经肌肉电子系统（人工突触，跨阻电路和人工肌肉致动器）（d）[42]
Figure 8. Schematic diagrams of a retina’s multilayer structure and the 3D structure of LOND （a）; The color-perception functions and characteristic retention times of the artificial-vision system under different light wavelengths （b）[55]; Image recognition and memory capacity of the photosynaptic device array on a flexible PI substrate （c）[57]; Configuration of organic optoelectronic synapse （photodetector and artificial synapse） and neuromuscular electronic system （artificial synapse, transimpedance circuit, and artificial muscle actuator） （d）[42]
图 9 系统级MNIST模式识别的仿真。SLP网络的原理图由785个输入神经元（即1个偏置神经元和28×28个神经元）和10个输出神经元（从0到9）组成，并通过785×10个突触权重进行全连接（a）；光/电脉冲序列下突触器件的200个权重状态的连续更新（b）；训练阶段中“A”的图像（c）和识别率（d）；训练阶段中“I”的图像（e）和识别率（f）[47]
Figure 9. System level simulation of MNIST pattern recognition. Schematics of the SLP network consisting of 785 input neurons （i.e., 1 bias neuron and 28 × 28 neurons） and 10 output neurons （from 0 to 9）, with full connection through 785 × 10 synaptic weights （a）; 200 weight states of synaptic device obtained under applied photonic/electric pulse （b）; Mapping images （c） and recognition rate （d） of “A” with the evolution of training phases; Mapping images （e） and recognition rate （f） of “I” with the evolution of training phases[47]
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