Self‐formed Schottky barrier induced selector‐less RRAM for cross‐point memory applications

We propose a selector‐less Pr_0.7Ca_0.3MnO₃ (PCMO) based resistive‐switching RAM (RRAM) for high‐density cross‐point memory array applications. First, we investigate the inhomogeneous barrier with an effective barrier height (Φ_eff), i.e., self‐formed Schottky barrier. In addition, a scalable 4F² selector‐less cross‐point 1 kb RRAM array has been successfully fabricated, demonstrating set, reset, and read operation for high cell efficiency and high‐density memory applications. (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Thermal effect on endurance performance of 3-dimensional RRAM crossbar array

Three-dimensional(3D) crossbar array architecture is one of the leading candidates for future ultra-high density nonvolatile memory applications. To realize the technological potential, understanding the reliability mechanisms of the3 D RRAM array has become a field of intense research. In this work, the endurance performance of the 3D 1D1 R crossbar array under the thermal effect is investigated in terms of numerical simulation. It is revealed that the endurance performance of the 3D 1D1 R array would be seriously deteriorated under thermal effects as the feature size scales down to a relatively small value. A possible method to alleviate the thermal effects is provided and verified by numerical simulation.     

Recent development of studies on the mechanism of resistive memories in several metal oxides

Resistive switching random access memories(RRAM)have been considered to be promising for future information technology with applications for non-volatile memory,logic circuits and neuromorphic computing.Key performances of those resistive devices are approaching the realistic levels for production.In this paper,we review the progress of valence change type memories,including relevant work reported by our group.Both electrode engineering and in-situ transmission electron microscopy(TEM)high-resolution observation have been implemented to reveal the influence of migration of oxygen anions/vacancies on the resistive switching effect.The understanding of resistive memory mechanism is significantly important for device applications.     

Electronic synapses based on ultrathin quasi-two-dimensional gallium oxide memristor

Synapse emulation is very important for realizing neuromorphic computing, which could overcome the energy and throughput limitations of today's computing architectures. Memristors have been extensively studied for using in nonvolatile memory storage and neuromorphic computing. In this paper, we report the fabrication of vertical sandwiched memristor device using ultrathin quasi-two-dimensional gallium oxide produced by squeegee method. The as-fabricated two-terminal memristor device exhibited the essential functions of biological synapses, such as depression and potentiation of synaptic weight, transition from short time memory to long time memory, spike-timing-dependent plasticity, and spike-rate-dependent plasticity. The synaptic weight of the memristor could be tuned by the applied voltage pulse, number,width, and frequency. We believe that the injection of the top Ag cations should play a significant role for the memristor phenomenon. The ultrathin of medium layer represents an advance to integration in vertical direction for future applications and our results provide an alternative way to fabricate synaptic devices.     

Impact of Ta/Ti electrodes on linearities of TaO_x-based resistive random-access memories for neuromorphic computing

In this work, by incorporating different electrodes(Ta/Ti) onto TaOxdielectric layer, we studied both the conductance reading and conductance updating(long term potentiation and depression) linearities in the two RRAM devices. Owing to the composition modulation(CM) mechanism, the Ta-electrode device shows better conductance reading and updating linearities. The RRAM device linearities directly influence the performance of the neural network when the devices are used as synapses. System evaluation of a two-layer neural network considering the conductance reading and updating linearity factors further confirm that both the training and inference accuracies of Ta electrode device are better than those of the Ti electrode one. We believe that this work could serve as a powerful reference for engineering synaptic devices with good linearity for neuromorphic computing applications.     

Synaptic plasticity and classical conditioning mimicked in single indium-tungsten-oxide based neuromorphic transistor

Emulation of synaptic function by ionic/electronic hybrid device is crucial for brain-like computing and neuromorphic systems. Electric-double-layer(EDL) transistors with proton conducting electrolytes as the gate dielectrics provide a prospective approach for such application. Here, artificial synapses based on indium-tungsten-oxide(IWO)-based EDL transistors are proposed, and some important synaptic functions(excitatory post-synaptic current, paired-pulse facilitation,filtering) are emulated. Two types of spike-timing-dependent plasticity(Hebbian STDP and anti-Hebbian STDP) learning rules and multistore memory(sensory memory, short-term memory, and long-term memory) are also mimicked. At last, classical conditioning is successfully demonstrated. Our results indicate that IWO-based neuromorphic transistors are interesting for neuromorphic applications.     

N型局部有源忆阻器的神经形态行为

局部有源忆阻器(locally-active memristor,LAM)凭借其高集成度、低功耗和局部有源特性等优点,在神经形态计算领域显示出巨大的潜力.本文提出了一种简单的N型LAM数学模型,通过揭示其非线性动力特性,设计了N型LAM神经元电路.采用Hopf分岔、数值分析等方法定量研究了该电路的动力学行为,成功模拟了多种神经形态行为,包括全或无行为、尖峰、簇发、周期振荡等.并利用该神经元电路结构模拟了生物触觉神经元的频率特性.仿真结果表明:当输入信号幅值低于阈值时,神经元电路输出信号的振荡频率与输入信号强度呈正相关(即兴奋状态),并在阈值处达到最大值.随后,继续增大激励强度,振荡频率则逐渐降低(即保护性抑制状态).最后,设计了N型LAM硬件仿真器,并完成了人工神经元电路的硬件实现,实验结果与仿真结果、理论分析相一致,验证了该N型LAM具备的神经形态行为.     

N型局部有源忆阻器的神经形态行为

局部有源忆阻器(locally-active memristor,LAM)凭借其高集成度、低功耗和局部有源特性等优点,在神经形态计算领域显示出巨大的潜力.本文提出了一种简单的N型LAM数学模型,通过揭示其非线性动力特性,设计了N型LAM神经元电路.采用Hopf分岔、数值分析等方法定量研究了该电路的动力学行为,成功模拟了多种神经形态行为,包括全或无行为、尖峰、簇发、周期振荡等.并利用该神经元电路结构模拟了生物触觉神经元的频率特性.仿真结果表明:当输入信号幅值低于阈值时,神经元电路输出信号的振荡频率与输入信号强度呈正相关(即兴奋状态),并在阈值处达到最大值.随后,继续增大激励强度,振荡频率则逐渐降低(即保护性抑制状态).最后,设计了N型LAM硬件仿真器,并完成了人工神经元电路的硬件实现,实验结果与仿真结果、理论分析相一致,验证了该N型LAM具备的神经形态行为.     

Recent progress on two-dimensional neuromorphic devices and artificial neural network

Mimicking biological synapses with microelectronic devices is widely considered as the first step in hardware building artificial neuromorphic networks, which is also the basis of brain-inspired neuromorphic computing. Numerous artificial neurons and synapses making up an artificial neuromorphic network have been gained wide attention due to their powerful and efficient data processing capabilities. Recently, artificial synapses, especially memristor-type and transistor-type synapses based on multifarious two-dimensional (2D) materials have been paid much attention. The unique properties of 2D materials make devices perform well in learning ability and power efficiency when mimicking synaptic behaviors, which highlights the feasibility of 2D neuromorphic devices in constructing artificial neuromorphic networks. Herein, the basic structures and principles of biological synapses are introduced, and the definitions of synaptic behaviors in synaptic electronic devices are discussed. Then, the progress of 2D memristor-type and transistor-type neuromorphic devices involving their device architecture, neuromorphic operational mechanism, and promising applications is reviewed. Finally, the future challenges of artificial synaptic devices based on 2D materials are discussed briefly.     

Optoelectronic memristor for neuromorphic computing

With the need of the internet of things,big data,and artificial intelligence,creating new computing architecture is greatly desired for handling data-intensive tasks.Human brain can simultaneously process and store information,which would reduce the power consumption while improve the efficiency of computing.Therefore,the development of brainlike intelligent device and the construction of brain-like computation are important breakthroughs in the field of artificial intelligence.Memristor,as the fourth fundamental circuit element,is an ideal synaptic simulator due to its integration of storage and processing characteristics,and very similar activities and the working mechanism to synapses among neurons which are the most numerous components of the brains.In particular,memristive synaptic devices with optoelectronic responding capability have the benefits of storing and processing transmitted optical signals with wide bandwidth,ultrafast data operation speed,low power consumption,and low cross-talk,which is important for building efficient brain-like computing networks.Herein,we review recent progresses in optoelectronic memristor for neuromorphic computing,including the optoelectronic memristive materials,working principles,applications,as well as the current challenges and the future development of the optoelectronic memristor.     

自旋霍尔纳米振荡器的非线性动力学及其应用

自旋霍尔纳米振荡器利用电流产生的自旋轨道力矩驱动磁性薄膜中磁矩进行高频进动,能在微纳尺度下实现全电学调控的相干自旋波和微波信号,是一类新型的纳米自旋电子学器件,在信息存储、处理和通信方面具有广泛的应用前景。基于强自旋轨道矩效应,人们近期在各类铁 磁/非磁重金属构成的双层薄膜结构中,已实现了多种不同自旋波模式的电学激发和调控,并对 其复杂的非线性动力学特性进行了深入的探究。基于这些前期的研究结果与最新的进展,我们在 本综述中对“对三角”结构的纳米间隙型、“蝴蝶结”型、纳米线型、垂直纳米点接触型以及阵 列等具有各类器件结构的自旋霍尔纳米振荡器所体现出来的丰富非线性动力学特性进行了详细讨 论与归纳,并对其在新型低能耗量子磁振子自旋器件和非冯诺依曼架构的自旋型人工神经网络计 算方面的潜在应用也进行了探讨。     

基于忆容器件的神经形态计算研究进展

人工智能的快速发展需要人工智能专用硬件的快速发展,受人脑存算一体、并行处理启发而构建的包含突触与神经元的神经形态计算架构,可以有效地降低人工智能中计算工作的能耗.记忆元件在神经形态计算的硬件实现中展现出巨大的应用价值;相比传统器件,用忆阻器构建突触、神经元能极大地降低计算能耗,然而在基于忆阻器构建的神经网络中,更新、读取等操作存在由忆阻电压电流造成的系统性能量损失.忆容器作为忆阻器衍生器件,被认为是实现低耗能神经网络的潜在器件,引起国内外研究者关注.本文综述了实物/仿真忆容器件及其在神经形态计算中的最新进展,主要包括目:前实物/仿真忆容器原理与特性,代表性的忆容突触、神经元及神经形态计算架构,并通过总结近年来忆容器研究所取得的成果,对当前该领域面临的挑战及未来忆容神经网络发展的重点进行总结与展望.     

Thermal stability and data retention of resistive random access memory with HfO_x/ZnO double layers

As an industry accepted storage scheme, hafnium oxide(HfO_x) based resistive random access memory(RRAM)should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current( 3 μA) with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures(up to 180℃), which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.     

Unconventional phase transition of phase-change-memory materials for optical data storage

Recent years, optically controlled phase-change memory draws intensive attention owing to some advanced applications including integrated all-optical nonvolatile memory, in-memory computing, and neuromorphic computing. The light-induced phase transition is the key for this technology. Traditional understanding on the role of light is the heating effect. Generally, the RESET operation of phase-change memory is believed to be a melt-quenching-amorphization process. However, some recent experimental and theoretical investigations have revealed that ultrafast laser can manipulate the structures of phase-change materials by non-thermal effects and induces unconventional phase transitions including solid-to-solid amorphization and order-to-order phase transitions. Compared with the conventional thermal amorphization,these transitions have potential superiors such as faster speed, better endurance, and low power consumption. This article summarizes some recent progress of experimental observations and theoretical analyses on these unconventional phase transitions. The discussions mainly focus on the physical mechanism at atomic scale to provide guidance to control the phase transitions for optical storage. Outlook on some possible applications of the non-thermal phase transition is also presented to develop new types of devices.     

Graphene resistive random memory — the promising memory device in next generation

Graphene-based resistive random access memory(GRRAM) has grasped researchers' attention due to its merits compared with ordinary RRAM. In this paper, we briefly review different types of GRRAMs. These GRRAMs can be divided into two categories: graphene RRAM and graphene oxide(GO)/reduced graphene oxide(r GO) RRAM. Using graphene as the electrode, GRRAM can own many good characteristics, such as low power consumption, higher density, transparency,SET voltage modulation, high uniformity, and so on. Graphene flakes sandwiched between two dielectric layers can lower the SET voltage and achieve multilevel switching. Moreover, the GRRAM with r GO and GO as the dielectric or electrode can be simply fabricated. Flexible and high performance RRAM and GO film can be modified by adding other materials layer or making a composite with polymer, nanoparticle, and 2D materials to further improve the performance. Above all,GRRAM shows huge potential to become the next generation memory.     

Low power resistive random access memory using interface-engineered dielectric stack of SiOx/a-Si/TiOy with 1D1R-like structure

In this study, we report a resistive random access memory (RRAM) using trilayer SiO_x/a-Si/TiO_y film structure. The low switching energy of <10 pJ, highly uniform current distribution (<13% variation), fast 50-ns speed and stable cycling endurance for 10⁶ cycles are simultaneously achieved in this RRAM device. Such good performance can be ascribed to the use of interface-engineered dielectric stack with 1D1R-like structure. The SiO_x tunnel barrier in contact with top Ni electrode to form diode-like rectifying element not only lowers self-compliance switching currents, but also improves cycling endurance, which is favorable for the application of high-density 3D memory.     

Fabrication and investigation of ferroelectric memristors with various synaptic plasticities

In the post-Moore era, neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks. Memristors have been proposed as a key part of neuromorphic computing architectures, and can be used to emulate the synaptic plasticities of the human brain. Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage, low write/read latency and tunable conductive states. However, among the reported ferroelectric memristors, the mechanisms of resistive switching are still under debate. In addition, there needs to be more research on emulation of the brain synapses using ferroelectric memristors. Herein, Cu/PbZr_0.52Ti_0.48O₃ (PZT)/Pt ferroelectric memristors have been fabricated. The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior. The synaptic plasticities, including excitatory post-synaptic current, paired-pulse facilitation, paired-pulse depression and spike time-dependent plasticity, have been mimicked by the PZT devices. Furthermore, the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models. This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.     

Nonvolatile memory with multilevel switching: a basic model

There is a current upsurge in research on nonvolatile two-terminal resistance random access memory (RRAM) for next generation electronic applications. The RRAM is composed of a simple sandwich of a semiconductor with two metal electrodes. We introduce here an initial model for RRAM with the assumption that the semiconducting part has a nonpercolating domain structure. We solve the model using numerical simulations and the basic carrier transfer mechanism is unveiled in detail. Our model captures three key features observed in experiments: multilevel switchability of the resistance, its memory retention, and hysteretic behavior in the current-voltage curve.     

Engineering oxide resistive switching materials for memristive device application

Based on a unified physical model and first-principle calculations, a material-oriented methodology has been proposed to control the bipolar switching behavior of an oxide-based resistive random access memory (RRAM) cell. According to the material-oriented methodology, the oxide-based RRAM cell can be designed by material engineering to achieve the required device performance. In this article, a Gd-doped HfO₂ RRAM cell with excellent bipolar switching characteristics is developed to meet the requirements of memristive device application. The typical memristive characteristics of the Gd-doped HfO₂ RRAM cell are presented, and the mechanism is discussed.     

Role of optical nonlinearity on transverse localization of light in a disordered one-dimensional optical waveguide lattice

We report a detailed numerical investigation on transverse localization of light in a 1D disordered lattice consisting of a large array of coupled waveguides in the presence of nonlinearity in the medium. Our study reveals that the presence of a focusing type of nonlinearity favors faster localization of light while a defocusing type of nonlinearity degrades the quality of localization. It is shown that presence of either of these could over-shadow localization of light unless the strength of disorder is sufficiently strong. Influence of the input beam width on propagation of light in such a disordered nonlinear medium has also been discussed. The present study should be useful in potential applications, in which one could exploit dominance of focusing nonlinearity on transverse localization of light in a disordered medium.