基于改进忆阻器的高通滤波器设计与仿真

针对传统的忆阻器模型存在不能很好地与HP实验室提出的忆阻器物理模型中忆阻器的阻值变化特点相符的问题,提出了一种改进的带有阈值电压的忆阻器模型,该模型能很好地模拟忆阻器的"激活"现象,其特性与HP实验室的忆阻器物理模型相符;基于该改进模型设计了一种高通滤波器电路,该电路通过改变忆阻器阻值控制电路的输出信号来改变忆阻器的阻值,从而实现了滤波器截止频率的调节。SPICE仿真结果验证了设计的正确性。     

Multiply accumulate operations in memristor crossbar arrays for analog computing

Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann inmemory computing architectures.By mapping analog numerical matrices into memristor crossbar arrays,efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law.In this brief review,we present the recent progress in two niche applications:neural network accelerators and numerical computing units,mainly focusing on the advances in hardware demonstrations.The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections.The acceleration of multiple layer perceptrons,convolutional neural networks,generative adversarial networks,and long short-term memory neural networks are described.The latter one is hard computing because the solving of numerical problems requires high-precision devices.Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted.Besides,other nonvolatile devices with the capability of analog computing are also briefly introduced.Finally,we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.     

忆阻-CMOS混合模逆电路设计

模逆运算是加密算法中最复杂的运算,更是最关键的模块之一。忆阻器是替代现有的晶体管从而延续摩尔定律的有力竞争者。文中结合信息安全和忆阻器两个领域的研究现状,将忆阻蕴含机制应用于模逆电路设计,研究忆阻器应用于大规模数字电路中的可行性和适应性。首先,基于FPGA平台提出忆阻蕴含逻辑电路模型,进而实现了基础逻辑门和加法器等功能模块;再调用功能模块,成功设计出了基于二进制扩展的Euclidean算法的忆阻-CMOS混合模逆电路。经仿真与验证,模逆模块在200MHz的时钟下能正确地执行设计功能。     

气体放电灯的电路特性

工作状态下的气体放电灯,通常认为具有电阻电感特性(R-L),但是随着电路理论的发展以及气体放电灯的广泛应用,根据它的工作原理及电路特性,我们认为用现有的R、L、C以及它们的组合来表示它的电路特性是不合适的。本文从电路理论以及大量的实验结果说明了这个问题,并认为气体放电灯的电路特性应该用“流控忆阻器”来表征。正确认识气体放电灯的电路特性,对制灯工业及镇流器的设计、生产和发展都有指导意义,对气体放电灯也将出现一系列新的更合理的计算方法。     

The Future of Memristors: Materials Engineering and Neural Networks

From Deep Blue to AlphaGo, artificial intelligence and machine learning are booming, and neural networks have become the hot research direction. However, due to the size limit of complementary metal–oxide–semiconductor (CMOS) transistors, von Neumann-based computing systems are facing multiple challenges (such as memory walls). As the number of transistors required by the neural network increases, the development of neural networks based on the von Neumann computer is limited by volume and energy consumption. As the fourth basic circuit element, memristor shines in the field of neuromorphic computing. The new computer architecture based on memristor is widely considered as a substitute for the von Neumann architecture and has great potential to deal with the neural network and big data era challenge. This article reviews existing materials and structures of memristors, neurophysiological simulations based on memristors, and applications of memristor-based neural networks. The feasibility and advancement of implementing neural networks using memristors are discussed, the difficulties that need to be overcome at this stage are put forward, and their development prospects and challenges faced are also discussed.     

基于忆阻器的矩形波信号发生器

利用忆阻器独特的电路学性质,设计了一个基于忆阻器的新型矩形波信号发生器。电路中不含有分立的电容元件,输出波形频率和幅值精确可调。用PSPICE进行仿真分析,仿真结果验证了该方案的有效性。     

基于忆阻器的SRAM存储单元设计

为了突破冯?诺依曼架构瓶颈,实现存算一体的存储功能,利用D锁存器设计了一种忆阻器存储单元.该忆阻器存储单元由忆阻器基本逻辑与门、或门和MeMOS电路组成.PSpice仿真显示,该忆阻器存储单元不仅可以实现非易失性存储功能,而且具有体积小、功耗低、结构简单等优点,可为实现非易失性存储单元提供良好参考.     

Modeling the memristor with piecewise linear function

Memristor is introduced as the fourth basic circuit element. In this paper, we show that piecewise linear function can be used to model memristor. Piecewise linear function is used as a window function to model the memristor device. The piecewise linear window function is linear at each subregion of the domain. Because of this good character, explicit relation between the thickness of the doped region and the charge can be delivered, together with the explicit memristance expression. Models using a general nonlinear window function may not have these explicit results. The piecewise linear window function is also more flexible than the existing window functions. Piecewise linear function can also be used to model the more general memristive systems. Copyright © 2014 John Wiley & Sons, Ltd.     

新型二阶磁控忆阻器简化模型的设计与验证

忆阻理论的提出极大地推进了混沌系统的发展,丰富了混沌电路的动力学行为。运算放大器因其强大的信号处理能力,成为忆阻器电路模型的重要组成部分。本文基于低功耗差分对构建了一种极简化的运算放大器,该运算放大器将所需晶体管数目减少至2个;以此运算放大器为基础,设计了新型二阶磁控忆阻器的模拟等效电路模型和硬件实验电路。结果表明：激励信号频率增加,斜“8”字形紧磁滞回线的旁瓣面积减小;激励信号幅度增加,斜“8”字形紧磁滞回线的旁瓣面积增加。电路仿真结果与硬件电路实验结果验证了新型磁控忆阻器模型的有效性与设计方法的正确性。     

新型忆阻光伏电池的建模与分析

光伏电池在缓解能源危机、改善生态环境中发挥了重要作用。文章针对光伏电池工作环境的易变性和模型参数的不确定性,提出了2类新型忆阻光伏电池模型,这两种模型能够对光伏电池输出特性进行准确、自适应描述;然后,通过严谨的数学推导以及数值仿真分别讨论了内、外部参数对新型忆阻光伏电池输出特性的影响,这样有利于高效选取忆阻器模型参数;最后,通过一系列数值仿真,验证了文章方案的有效性,为后续相关的应用研究提供依据。