一种基于忆阻器的新型曝光量传感器

为了增大曝光量传感器的测量范围,提出了一种基于忆阻器的新型曝光量传感器设计方案。该传感器主要由光敏电阻、限流电阻和忆阻器组成。光敏电阻的阻值随着光照度的变化发生改变,引起回路电流和电荷量发生变化。基于记忆效应,忆阻器利用其阻值来表征物体被辐照时光照度在一段时间内的积分值,从而实现了对曝光量的测量。为了验证所设计的基于忆阻器的曝光量传感器,进行了光照度恒定不变、逐渐增大和随机变化情况下的测试。实验结果表明,曝光量传感器实现了对曝光量的测量,具有比一般测量方法更大的测量范围,而且随着光照度的增大,传感器的测量范围随之增大。另外,随着限流电阻的增大,传感器的测量范围逐渐增大,但其灵敏度随之降低。     

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.     

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.     

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.     

LC振荡型忆阻混沌电路及时滞反馈控制

将磁控忆阻器耦合于LC振荡电路中,得到了一种新的忆阻混沌电路.随后通过理论上的动力学分析、数值仿真、电路实验等验证了该电路的混沌特性.为了实现电路的混沌控制,设计了一种新型模拟时滞控制器.利用该控制器将混沌电路状态变量加以延时并反馈至原电路中.数值仿真和电路实验结果均表明,所设计的时滞控制器可实现混沌电路的镇定控制.进一步研究时滞控制下电路的分岔行为,发现时滞控制下的电路又可通过倍周期分岔进入超混沌.     

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

A large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the most from nanodevices because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here, spike‐timing‐dependent plasticity (STDP), a basic and primordial learning function in the brain, is demonstrated with a new class of synapstor (synapse‐transistor), called nanoparticle organic memory field‐effect transistor (NOMFET). This learning function is obtained with a simple hybrid material made of the self‐assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, it is also demonstrated how the shape of the applied spikes can tailor the STDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre‐ and postsynaptic neurons, and a behavioral macromodel is developed on usual device simulator.     

有源忆阻器伏安关系与有源忆阻电路频率特性研究

忆阻器是具有记忆特性的非线性电阻器,是物理上新实现的第四种基本二端电路元件.以分段二次型非线性函数描述的有源磁控忆阻器为例,分析了有源忆阻器的伏安关系,研究了有源忆阻器与电容串联电路(简称有源WC电路)的频率响应特性,并与有源RC电路进行了比较分析.结果表明:有源忆阻器的伏安特性曲线为紧磁滞回线,且依赖于其内部初始状态;有源WC电路与有源RC电路均呈现高通特性,但前者为超前网络而后者为滞后网络.基于有源磁控忆阻器的等效电路进行了电路仿真,其结果很好地验证了理论分析结果.     

Synchronisation control for a class of complex-valued fractional-order memristor-based delayed neural networks

In this paper, the problem of drive-response synchronisation of complex-valued fractional-order memristor-based delayed neural networks is discussed via linear feedback control method. By separating complex-valued system into two equivalent real-valued systems, and using the comparison theorem, algebraic criteria are given to ascertain the synchronisation of the considered system with single delay. Meanwhile, for the case of model with multiple delays, the corresponding sufficient conditions are also presented. Because complex-valued system can reduce to real-valued ones when the imaginary part is ignored, the proposed results of this paper generalise existing works on relevant real-valued system. Finally, the effectiveness of the obtained theoretical results is testified by two numerical examples.     

一种高性能二阶荷控忆阻器电路设计与验证

当前忆阻器等效电路中的传统运算放大器存在功耗高、噪声大等问题。针对这些问题,基于简化差分对设计了两种极简化的运算放大器。通过电路仿真对两种简化运算放大器与传统运算放大器进行功耗与噪声仿真分析。结果表明,与三种传统运算放大器相比,该简化运算放大器的功耗最低,抗噪声性能最优。运算放大器的总功耗为15 mW,等效输出噪声电压为11.55 nV·Hz^-1/2,噪声系数为35.873 dB。基于两种简化运算放大器,设计了一种二阶荷控忆阻器等效电路。通过理论分析、电路仿真和硬件电路板基实验,对该等效电路的忆阻特性进行了分析与验证。     

基于TiO_2忆阻器的混沌振荡电路

忆阻器是一种具有记忆性的新型非线性电阻,是继电阻器、电容器和电感器之后的第4种基本电路元件。该文根据惠普实验室提出的荷控型TiO2忆阻器模型,构建了一个磁控TiO2忆阻器模型,采用该磁控忆阻器模型设计了一个新的混沌振荡电路,以期产生复杂的伪随机混沌信号。对该振荡电路的基本动力学特性进行了理论和仿真分析,并进行了DSP芯片实验验证。仿真结果、理论分析和实验观察具有一致性,表明该混沌系统具有与一般混沌系统不同的特性。