忆阻器

我们在分析了RTD的基本特性的基础上，利用RTD完美的负差分电阻特性和忆阻器的自动记忆功能，构建了忆阻器／RTD混合结构细胞神经网络。

The basic characteristics of resonant tunneling diode are analyzed and RTD cellular neural network based on memristor is built up by utilizing negative differential resistance property of RTD and auto-memory function .

证实忆阻器模型的可用性。

Confirm the availability of the memristor model .

论文第二章重点介绍了Pt/TiO2-x/TiO2/TiO2+x/Pt纳米结构双扩展忆阻器的基本工作原理、结构模型。

Secondly introduced the basic principle , structure model of the double expansion memristor .

本文着重研究忆阻器在混沌电路和逻辑门电路构造中的应用潜力。

This thesis focuses on the potential applications of memristor in chaotic circuit and logic gate structure .

根据忆阻器特点，提出面向数据库的应用。第二，建立了磁控忆阻器模型。

For memristor characteristics , database applications are proposed . Secondly , a flux-controlled memristor model is established .

忆阻器的体积小且具有天然的信息存储能力，它非常适合作为神经形态系统中的电子突触。

Memristor has small size and natural information storage capability , which is suitable to be the electronic synapse .

最后，本文在加法器的基础上，提出基于忆阻器的乘法器设计方法。

Finally , based on the adder , we present the design method for multiplier based on the memristor .

我们通过探究忆阻器与神经突触之间的相似性，获得了具有自主学习功能的智能化器件。

We study the analogy between the memristor and synapse , and achieved the synaptic device with inherent learning abilities .

他们可以从中观察到忆阻器运行时发生的化学和结构改变的详细信息。

That gave them a detailed insight into the chemistry and structure changes that happen when the device is operating .

威廉姆斯及其研究小组开创性的研究以纳电子学为基础，他们是证明忆阻器存在的第一人。

Building on their groundbreaking research in nanoelectronics , Williams and team are the first to prove the existence of the memristor .

但依据Ⅰ-Ⅴ回线形状的分类，人们所研究的大多属于第Ⅰ类忆阻器，Ⅰ-Ⅴ回线在原点发生交叉。

But according to shape of I-V loop , the researched mostly belongs to type I memristor which the I-V loop cross at origin .

忆阻器理论提出和物理实现为细胞神经网络的发展带来了新的曙光。

Fortunately , The theory discovering and physical implementation of memristor have brought a novel promising hope for the development of cellular neural networks .

由于低电压下的欧姆区间的存在，Ⅰ-Ⅴ回线在该区间重叠。因而从Ⅰ-Ⅴ回线的形状来看，属于第Ⅱ类忆阻器。

Because the Ohm region of the overlapping I-V loop exists at low voltage range , I-V loop belongs to type ⅱ memristor form shape .

通过忆阻器的电流可以改变它的电阻状态，但是在实际环境中观察随之而来的材料改变是一个挑战。

Electrical charge flowing through a memristor changes the resistance state of the device , but actually observing the corresponding material changes has been a challenge .

比如说，有一天，基于忆阻器的设备将在计算机电路中模仿人脑中的神经元，如同神经突起般运作。

Memristor-based devices could one day , for example , act like synapses inside computer circuits , mimicking the behavior of neurons in the human brain .

然后通过增加一定数量的忆阻器对该方法进行了优化，使赋值操作次数减少了O（n~2）。

Then , we optimize this method by adding a number of memristors , which makes the number of assignments reduced by O ( n ~ 2 ) .

由于忆阻器具有纳米尺寸和自动记忆的功能，目前被广泛应用到忆阻交叉阵列中，用来作为存储器。

Because memristor has the functions of nanoscale size and automatic memory , it has been widely applied to the cross array based on memristor as a memory unit .

忆阻器构成的存储器不仅具有优异的存储能力，还可以进行计算，可以实现计算和存储更好的融合，有可能突破计算与存储分离的冯·诺依曼体系结构。

The memory consisted by memristors not only has the excellent memory capability , but also has the computing capability , which can implement better combination of computing and memory .

忆阻器是被誉为继电阻、电容、电感之后的第四个基本电子元件，是具有记忆性能的电阻器，表现出能够根据流过它的电荷大小而改变电阻值的特性。

Memristor is known as the fourth basic electronic components following the resistors , capacitors , inductors . Memristor is a resistor with memory , which shows the characteristics that " remember " what charge it had when power runs through it .

基于提出的模型，本研究给出了过渡时间的显式定量表达，理论上分析了逻辑操作需要的电路结构与器件特性，以及控制电压的方式。第三，提出忆阻器存储运算结构设计。

Based on the proposed model , an explicit quantitative expression of switching time can be given . We also discuss the device characteristics adapt to the structure and logic operations , and the mode of control voltages . Thirdly , the structure for memristor memory and computing is designed .