Accelerated Ionic Motion in Amorphous Memristor Oxides for Nonvolatile Memories and Neuromorphic Computing

Memristive devices based on mixed ionic–electronic resistive switches have an enormous potential to replace today's transistor‐based memories and Von Neumann computing architectures thanks to their ability for nonvolatile information storage and neuromorphic computing. It still remains unclear however how ionic carriers are propagated in amorphous oxide films at high local electric fields. By using memristive model devices based on LaFeO₃ with either amorphous or epitaxial nanostructures, we engineer the structural local bonding units and increase the oxygen‐ionic diffusion coefficient by one order of magnitude for the amorphous oxide, affecting the resistive switching operation. We show that only devices based on amorphous LaFeO₃ films reveal memristive behavior due to their increased oxygen vacancy concentration. We achieved stable resistive switching with switching times down to microseconds and confirm that it is predominantly the oxygen‐ionic diffusion character and not electronic defect state changes that modulate the resistive switching device response. Ultimately, these results show that the local arrangement of structural bonding units in amorphous perovskite films at room temperature can be used to largely tune the oxygen vacancy (defect) kinetics for resistive switches (memristors) that are both theoretically challenging to predict and promising for future memory and neuromorphic computing applications.     

Associative Memory for Image Recovery with a High‐Performance Memristor Array

Associative memory is one of the significant characteristics of the biological brain. However, it has yet to be realized in a large memristor array due to the high requirements on the memristor device. In this work, the multilevel memristor cell is optimized by employing an electro‐thermal modulation layer. Memristor devices show both high resistance, cell‐to‐cell uniformity, and multilevel resistive switching behaviors with good reliability. A Hopfield neural network is experimentally demonstrated on a 1k memristor array that is capable of realizing the associative memory function for emotion image recovery. By using both asynchronous and synchronous refresh schemes, complete emotion images can be recalled from partial information.     

时滞诱发的忆阻型Hopfield神经网络的复杂动力学

本文研究含时滞的忆阻型环状Hopfield神经网络的稳定性、Hopf分岔以及复杂振荡模式.根据特征方程根分布情况,获得了系统全时滞稳定条件和与时滞相关的稳定条件.通过数值计算揭示了丰富的动力学现象,如多种周期运动和混沌吸引子等,并给出了Poincaré截面上的分岔图.设计了电路实验平台,取得了与理论分析和数值计算高度吻合的实验结果.     

Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small‐scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High‐precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single‐layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible.     

基于忆阻器的计算存储融合体系结构研究进展

Memristor is an enabling device with non volatile resistance, low power consumption, high durability, ease of integration, and CMOS compatibility. The stateful logic of memristors can rea lize the true fusion of computing and storage, and is complete in logic, which is expected to break the limitation of Von Neumann architecture and effectively alleviate the memory wall bottleneck. These excellent properties gain memristors great interest from academia and industry. In light of this, this paper summarizes the research progress of application oriented computing storage fusion architecture based on stateful logic. Firstly, the implementation principle and improvement method of state logic are analyzed in detail. Secondly, the state logic design based on the memristor crossbar is reviewed, including the parallel implementation of the basic logics, copy operation and comparison operation, and then the design principle and implementation structure of the data storage structure based on the memristors are summarized. The paper then revisits an application oriented computing storage fusion architecture in detail, and finally summarizes the problems in the research of this direction, and looks forward to the future direction.     

A Large Dynamic Range Floating Memristor Emulator With Equal Port Current Restriction

In this paper, a large dynamic range floating memristor emulator(LDRFME) with equal port current restriction is proposed to be achieved by a large dynamic range floating voltage-controlled linear resistor(VCLR). Since real memristors have not been largely commercialized until now, the application of a LDRFME to memristive systems is reasonable. Motivated by this need, this paper proposes an achievement of a LDRFME based on a feasible transistor model. A first circuit extends the voltage range of the triode region of an ordinary junction field effect transistor(JFET). The idea is to use this JFET transistor as a tunable linear resistor. A second memristive non-linear circuit is used to drive the resistance of the first JFET transistor. Then those two circuits are connected together and, under certain conditions, the obtained "resistor" presents a hysteretic behavior,which is considered as a memristive effect. The electrical characteristics of a LDRFME are validated by software simulation and real measurement, respectively.     

Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution-Processed 2D Semiconductors

Memristive systems present a low-power alternative to silicon-based electronics for neuromorphic and in-memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large-area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a two-terminal vertical geometry. Here, the authors report thermally activated memristors fabricated from percolating networks of diverse solution-processed 2D semiconductors including MoS₂, ReS₂, WS₂, and InSe. The mechanisms underlying threshold switching and negative differential resistance are elucidated by designing large-area lateral memristors that allow the direct observation of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 10³) that are achieved at low fields (≈4 kV cm⁻¹) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solution-processed 2D semiconductors as a platform for neuromorphic architectures.     

On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

Abstract

Recently, the SWEET sensing setup has been proposed as a way of exploiting reservoir computing for sensing. The setup features three components: an input signal (the drive), the environment and a reservoir, where the reservoir and the environment are treated as one dynamical system, a super-reservoir. Due to the reservoir-environment interaction, the information about the environment is encoded in the state of the reservoir. This information can be inferred (decoded) by analysing the reservoir state. The decoding is done by using an external drive signal. This signal is optimised to achieve a separation in the space of the reservoir states: Under different environmental conditions, the reservoir should visit distinct regions of the configuration space. We examined this approach theoretically by using an environment-sensitive memristor as a reservoir, where the memristance is the state variable. The goal has been to identify a suitable drive that can achieve the phase space separation, which was formulated as an optimization problem, and solved by a genetic optimization algorithm developed in this study. For simplicity reasons, only two environmental conditions were considered (describing a static and a varying environment). A suitable drive signal has been identified based on intuitive analysis of the memristor dynamics, and by solving the optimization problem. Under both drives the memristance is driven to two different regions of the one-dimensional state space under the influence of the two environmental conditions, which can be used to infer about the environment. The separation occurs if there is a synchronisation between the drive and the environmental signals. To quantify the magnitude of the separation, we introduced a quality of sensing index: The ability to sense depends critically on the synchronisation between the drive and environmental conditions. If this synchronisation is not maintained the quality of sensing deteriorates.     

Initial Phase Properties and Anti-interference Capability of Memristor Models

忆阻器模型的初相特征与抗干扰性能

张驰,罗志彬,金湘亮

（湖南师范大学 物理与电子科学学院,长沙 410081）

摘要：

忆阻器是用来描述电流与磁通量之间关系的一种电子器件。现已有三种较经典的忆阻器理想模型以及相关的一些分析。但是对每一种模型的抗干扰性能与每种模型的优缺点仍缺少相应的对比分析,故有必要去仿真并对比各种模型。本文在matlab仿真软件中向各忆阻器模型加入噪声信号,以得到相应的特性曲线,从而分析各模型的抗干扰性能。仿真结果表明,HP模型展现了最佳的抗干扰性能。此外,当改变输入信号初相时,只有HP模型可以保持其忆阻特征;当改变输入信号数量时,只有HP模型可以保持其原有的精确度。

关键词：忆阻器;输入信号;噪声分析;精确度

     

一种改进的忆阻器交叉阵列

本文对忆阻器的惠普模型与差分型忆阻器存储单元进行了分析与研究.利用惠普模型构成了经典的忆阻器交叉阵列,并对其存储性能进行了分析与仿真,提出了一种改进的基于差分型忆阻器单元的忆阻器交叉阵列.理论分析与仿真表明,改进的阵列在存储灰度图像时具有更好的效果,其峰值信噪比提高了约30％.