ZnO全光控忆阻器及其类突触行为

本研究基于ZnO制备了一种全光控忆阻器,短波光照射可增大器件电导,长波光则可降低电导,并且电导态可以长时间保持.因此,通过改变施加光信号的波长,可实现忆阻器电导的可逆调控.基于以上特性,该器件可以模拟突触基本功能,包括长程增强与长程抑制、光功率密度依赖可塑性、频率依赖可塑性以及学习-遗忘-再学习的经验学习行为.与电相比...     

等离子体处理对突触晶体管长程塑性的影响

作为神经形态计算系统的基本组成单元,人工突触器件在高性能并行计算、人工智能和自适应学习方面具有巨大的应用潜力。其中,电解质栅突触晶体管(Electrolyte-gated synaptic transistors, EGSTs)以其沟道电导的可控性成为下一代神经形态器件被广泛研究的对象,并用来模拟神经突触功能。EGSTs因双电层的快速自放电效应,导致其存在长程塑性持续时间较短和沟道电导不易调控等问题。本研究采用水诱导的In₂O₃薄膜作为沟道材料,以壳聚糖作为栅电解质材料,制备了基于In₂O₃的EGSTs,并对器件沟道层进行了氧等离子体处理。研究发现,利用氧等离子体中的活性氧自由基在沟道层表面产生陷阱态,使更多氢离子在电解质/沟道界面处被俘获,器件性能表现为回滞窗口增大,对EGSTs器件的长程塑性实现调控。基于双电层的静电耦合效应和电化学掺杂效应,本研究利用EGSTs器件模拟了神经突触的兴奋性突触后电流(EPSC)、双脉冲易化(PPF)、短程塑性(STP)和长程塑性(LTP)等突触行为。同时,基于该器...     

用于人工突触的染料敏化钙钛矿/有机半导体三元晶体管（英文）

人工智能因其类脑工作模式和高效任务处理能力而受到广泛关注.光刺激突触器件在神经形态计算领域应用潜力巨大.本文报道了具有三元光敏材料体系的光刺激突触器件,该体系由有机金属卤化物钙钛矿CH₃NH₃PbBr₃、有机染料罗丹明B和有机半导体并五苯组成.我们发现,通过引入罗丹明B,复合器件的光吸收和光响应性能得到显著提升.通过调整输入信号,复合器件表现出了良好的仿真学习行为.并且,这些器件可对强度低至1.1μW cm^-2的光信号响应,并能在低电压下进行工作.器件在-50μV的源漏电压下实现了1.25 fJ的超低功耗,这一能量损耗与单次生物突触活动的能耗相当.此外,复合突触器件还实现了自我学习过程,表现出了良好的学习效率.同时,它们可识别不同摩斯电码之间的细微差别,未来可应用于加密信息解码领域.本文所提出的三元突触器件拥有优异的光性能,在开发人工智能方面具有广泛的应用前景.     

具有光电信号双输出的发光电化学人工突触（英文）

尽管近年来多种突触器件的研究已取得了显著进展,但寻找具有新功能的人工突触器件仍是构建人工神经网络的重要任务.片上光电互连技术的成熟使得人工神经网络中的权重更新能以光和电的形式进行,作为权重控制端的人工突触器件中光电信号的并行输出便成为一个有趣和值得拥有的功能.在大规模神经网络的设计中,光电信号双输出能够提供额外的输出自由度并降低电子引线密度.因此,本研究首次开发了基于聚[2-甲氧基-5-(2-乙基己氧基)-1,4-苯乙炔]/聚(环氧乙烷)/锂盐共混的具有光电信号双输出的发光电化学人工突触(LEEAS).LEEAS中的电化学氧化还原反应使该器件能够实现生物学中的突触可塑性,并模拟了记忆增强过程、高通滤波特性和经典的巴甫洛夫条件反射实验.此外,在连续相同的电脉冲刺激下, LEEAS的瞬态发光强度表现出类似突触可塑性的增强行为.由于结合了电致发光和突触记忆行为, LEEAS阵列展现了独特的图像显示和存储功能,可以记忆显示过的图像.本研究提出的LEEAS丰富了人工突触器件的种类,促进了下一代光电混合人工神经网络的多样化设计与发展.     

基于氧化物基电解质栅控晶体管突触的关联学习

电解质栅控晶体管(Electrolyte-gated transistors, EGTs)的沟道电导连续可调特性使其在构建神经形态计算系统中具有巨大应用潜力。本工作以非晶态Nb₂O₅作为沟道材料, Li_xSiO₂作为栅电解质材料,制备了一种具备低沟道电导(～120n S)的EGT器件。该器件利用Li⁺嵌入/脱出Nb₂O₅晶格导致的沟道电导连续可逆变化,模拟了神经突触的短程可塑性(Short-termplasticity,STP)、长程可塑性(Long-termplasticity,LTP)以及STP向LTP的转变等功能。基于这种EGT突触特性,本工作设计了关联学习电路,实现了突触权重的负反馈调节,并模拟了“巴普洛夫的狗”经典条件反射行为。这些结果展现出EGT作为神经突触器件的巨大潜力,为实现神经形态计算硬件提供了器件参考。     

一步掩膜法制备ZnO纳米线忆阻器

本文基于单根ZnO纳米线(NW),采用一步掩膜的方法制备了Au/ZnO NW/Au忆阻器。器件表现出无极性忆阻行为,开关比可达10~5以上。低阻态具有半导体导电特性,推测忆阻行为可能来源于ZnO NW表面氧空位形成的不连续导电丝的通断。一步掩膜法工艺简单,制备过程对器件污染少,因此是制备纳米线器件的有效方法。     

用于神经形态计算的具有可调功能的浮栅光敏突触晶体管

将记忆和处理功能整合为一个单元的突触器件在神经形态计算、软机器人和人机交互等方面具有广泛的应用潜力.然而,先前报道的大多数突触器件一旦制造出来就表现出固定的性能,这限制了它们在不同场景中的应用.在这里,我们报道了一种以钙钛矿量子点为电荷俘获层、以原子层沉积的Al₂O₃为隧穿层的浮栅光敏突触晶体管.在电或者光信号的刺激下,该器件都能展示出典型的突触行为,包括兴奋性突触后电流、双脉冲异化和动态滤波特性.进一步地,器件中高质量Al₂O₃隧穿层和高光敏的钙钛矿量子点电荷俘获层使得其突触可塑性可以在光和电信号的共同调制下实现大范围的调节.在电调制过程中施加光信号可以显著改善突触权重的变化和权值更新的非线性,而光调制下的记忆效应可以明显地受到栅极电压的调节.该器件的阵列进一步展示了对图案"0"和"1"的不同突触权重或记忆时间的学习和遗忘过程.综上,这项工作为构建复杂而稳固的人工神经网络提供了具有可调功能的突触器件.     

氧化铁忆阻器中缺陷态诱导的模拟型阻变及突触双脉冲易化特性

模拟型阻变突触特性能够为神经形态计算提供高的计算精度并避免计算过程中带来的电导卡滞、跃变以及失效等问题。模拟生物突触在刺激脉冲下的行为,能够更好地揭示电子器件的仿生特性机理并为高性能神经形态计算提供支撑。突触双脉冲易化是生物突触的重要特性,反映了在外界刺激作用下的易化和适应性过程,对揭示神经元的工作机制至关重要。为了构建突触双脉冲易化的模拟型忆阻器件,本研究通过器件的能带结构设计及氧空位缺陷态的调控,利用射频磁控溅射法制备了一种结构为Ag/FeOx/ITO的忆阻器。电学测试结果表明,该器件具有优异的渐进递增的非线性阻变特性,即模拟型阻变特性。在I-V循环扫描3000次范围内,这种器件均表现出模拟型阻变特性,可提供稳定的、可分离的16个电导状态,且在104 s内维持良好,说明这些电导状态是非易失性的,这主要归功于电子在氧空位缺陷态中的捕获与去捕获以及在势垒间隧穿行为。但是,在低电场强度情况下,捕获的热电子有可能会跃迁出浅陷阱能级,而呈现出易失性。根据这种器件的易失性和非易失性共存特性,通过调制电压脉冲宽度、幅度,器件能够表现出很好的突触双脉冲易化特性,显示出该类型器件在神经形态计算中的潜...     

p型ZnO掺杂及其发光器件研究进展与展望

ZnO是一种新型的Ⅱ-Ⅵ族宽带隙半导体,具有很多优异的的光电性能.但一般制备出的ZnO薄膜材料均是n型,很难实现p型的掺杂.ZnO的p型掺杂是实现其光电器件应用的关键技术,也是目前ZnO研究的关键课题.目前在p型ZnO的掺杂理论和实验方面都有很大的进展,对此进行了详细的分析与论述,并且展望了p型ZnO薄膜制备的前景.     

多阻态铁电HLO忆阻器及其在联想学习电路和人脸识别中的实现（英文）

随着摩尔定律接近物理极限,传统的冯诺依曼架构面临挑战.忆阻器在多层存储、神经形态系统和模拟电路中的应用具有克服冯诺依曼架构瓶颈的潜力.在这里,我们在硅衬底上生长了Pd/La:HfO₂(HLO)/La_2/3Sr_1/3MnO₃高性能忆阻器,其有利于与互补式氧化物半导体工艺兼容.该忆阻器器件表现出良好的循环稳定性和多级电阻状态存储能力以及器件的突触特性,如长时增强/抑制、短时记忆到长时记忆、尖峰时间依赖性可塑性和双脉冲促进.基于器件的类脑突触行为,在神经启发计算中识别人脸图像时,识别率高达91.11%.通过理论计算和硬件联想学习电路测试,基于铪基铁电忆阻器的生物联想学习行为得以实现.     

The Design of 3D‐Interface Architecture in an Ultralow‐Power,Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human–machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid‐ion gated organic field‐effect transistors (OFETs) with a 3D‐interface conducting channel for ultralow‐power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and high‐pass filtering, are successfully realized. Furthermore, the single‐fiber based artificial synapse can be operated by an ultralow presynaptic spike down to ?0.5 mV with an ultralow reading voltage at ?0.1 mV due to the large contact surface between the ionic electrolyte and fiber‐like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low‐power integrated synaptic circuit.     

Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification

For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy‐efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device‐to‐system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction.     

Mimicking Synaptic Plasticity and Neural Network Using Memtranstors

Artificial synaptic devices that mimic the functions of biological synapses have drawn enormous interest because of their potential in developing brain‐inspired computing. Current studies are focusing on memristive devices in which the change of the conductance state is used to emulate synaptic behaviors. Here, a new type of artificial synaptic devices based on the memtranstor is demonstrated, which is a fundamental circuit memelement in addition to the memristor, memcapacitor, and meminductor. The state of transtance (presented by the magnetoelectric voltage) in memtranstors acting as the synaptic weight can be tuned continuously with a large number of nonvolatile levels by engineering the applied voltage pulses. Synaptic behaviors including the long‐term potentiation, long‐term depression, and spiking‐time‐dependent plasticity are implemented in memtranstors made of Ni/0.7Pb(Mg_1/3Nb_2/3)O₃‐0.3PbTiO₃/Ni multiferroic heterostructures. Simulations reveal the capability of pattern learning in a memtranstor network. The work elucidates the promise of memtranstors as artificial synaptic devices with low energy consumption.     

A Synaptic Transistor based on Quasi‐2D Molybdenum Oxide

Biological synapses store and process information simultaneously by tuning the connection between two neighboring neurons. Such functionality inspires the task of hardware implementation of neuromorphic computing systems. Ionic/electronic hybrid three‐terminal memristive devices, in which the channel conductance can be modulated according to the history of applied voltage and current, provide a more promising way of emulating synapses by a substantial reduction in complexity and energy consumption. 2D van der Waals materials with single or few layers of crystal unit cells have been a widespread innovation in three‐terminal electronic devices. However, less attention has been paid to 2D transition‐metal oxides, which have good stability and technique compatibility. Here, nanoscale three‐terminal memristive transistors based on quasi‐2D α‐phase molybdenum oxide (α‐MoO₃) to emulate biological synapses are presented. The essential synaptic behaviors, such as excitatory postsynaptic current, depression and potentiation of synaptic weight, and paired‐pulse facilitation, as well as the transition of short‐term plasticity to long‐term potentiation, are demonstrated in the three‐terminal devices. These results provide an insight into the potential application of 2D transition‐metal oxides for synaptic devices with high scaling ability, low energy consumption, and high processing efficiency.     

Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Memristive devices, having a huge potential as artificial synapses for low‐power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability. Fundamentally, the poor repeatability of analog resistance states is closely associated with the random formation of conductive filaments, which consists of oxygen vacancies. In this work, graphene quantum dots (GQDs) are introduced into memristive devices. By virtue of the abundant oxygen anions released from GQDs, the GQDs can serve as nano oxygen‐reservoirs and enhance the localization of filament formation. As a result, analog resistance states with highly tight distribution are achieved with nearly 85% reduction in variations. In addition the insertion of GQDs can alter the energy band alignment and boost the tunneling current, which leads to significant reduction in both switching voltages and their distribution variations. This work may pave the way for achieving artificial neural networks with accurate and efficient learning capability.     

Capping CsPbBr<Subscript>3</Subscript> with ZnO to improve performance and stability of perovskite memristors

The rapid development of information technology has led to an urgent need for devices with fast information storage and processing,a high density,and low energy consumption.Memristors are considered to be next-generation memory devices with all of the aforementioned advantages.Recently,organometallic halide perovskites were reported to be promising active materials for memristors,although they have poor stability and mediocre performance.Herein,we report for the first time the fabrication of stable and high-performance memristors based on inorganic halide perovskite (CsPbBr3,CPB).The devices have electric field-induced bipolar resistive switching (ReS) and memory behaviors with a large on/off ratio (＞105),low working voltage (＜1 V) and energy consumption,long data retention (＞104 s),and high environmental stability,which are achieved via ZnO capping within the devices.Such a design can be adapted to various devices.Additionally,the heterojunction between the CPB and ZnO endows the devices with a light-induced ReS effect of more than 103 with a rapid response speed (＜1 ms),which enables us to tune the resistance state by changing the light and electric field simultaneously.Such multifunctional devices achieved by the combination of information storage and processing abilities have potential applications for future computing that transcends traditional architectures.     

Recent Progress in Three‐Terminal Artificial Synapses: From Device to System

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows changes in synaptic strength that make a brain capable of learning from experience. During development of neuromorphic electronics, great efforts have been made to design and fabricate electronic devices that emulate synapses. Three‐terminal artificial synapses have the merits of concurrently transmitting signals and learning. Inorganic and organic electronic synapses have mimicked plasticity and learning. Optoelectronic synapses and photonic synapses have the prospective benefits of low electrical energy loss, high bandwidth, and mechanical robustness. These artificial synapses provide new opportunities for the development of neuromorphic systems that can use parallel processing to manipulate datasets in real time. Synaptic devices have also been used to build artificial sensory systems. Here, recent progress in the development and application of three‐terminal artificial synapses and artificial sensory systems is reviewed.     

半导体ZnO单晶生长的技术进展

ZnO单晶是一种具有半导体、发光、压电、电光、闪烁等性能的多功能晶体材料。近年来,它在紫外光电器件和GaN衬底材料等方面的应用前景而使其成为新的研究热点。本文综述了ZnO单晶助熔剂法、水热法、气相法等生长技术的研究进展,结合ZnO单晶的化学结构,探讨了该晶体的结晶习性及生长技术发展方向。     

Stable aqueous dispersions of ZnO nanoparticles for ink-jet printed gas sensors

For the preparation of printed devices based on ZnO nanoparticles (ZnO NPs), stable colloidal dispersions of these materials are highly desirable. ZnO NPs have been synthesized by Chemical Vapor Synthesis. The particles have a spherical shape with a narrow size distribution. Stable aqueous dispersions of the ZnO NPs have been successfully prepared after the addition of a polymeric stabilizer. These stable dispersions have been used to print ZnO NP films on interdigital gold structures on silicon by ink-jet printing. The printing parameters have been optimized for forming layers with high quality. Close-packed ZnO NP thin films with a thickness between 100-250 nm have been prepared. Impedance spectroscopy has been used to study the gas sensing properties of the printed films at different temperatures in air and in hydrogen. The impedance spectra show the semi-circles typical for semiconducting materials. The conductance of the printed films has been measured at room temperature with high accuracy. In hydrogen gas, the conductance is larger as expected and this behavior is reversible.