MEMRISTORS

BACHELOR OF TECHNOLOGY

IN

ELECTRONICS AND COMMUNICATION ENGINEERING

Submitted

By

S.Y.Viswanath 08881A0460

Department of Electronics and Communication Engineering

VARDHAMAN COLLEGE OF ENGINEERING

(Approved by AICTE, Affiliated to JNTUH & Accredited by NBA)

1

2011 - 12

MEMRISTORS

A

Project Report

Submitted in the Partial Fulfilment of the

Requirements

for the Award of the Degree of

BACHELOR OF TECHNOLOGY

IN

ELECTRONICS AND COMMUNICATION ENGINEERING

Submitted

By

S.Y.Viswanath 08881A0460

Under the Guidance of

G.Kalyanchakravarthy

Assistant Professor

Department of ECE

Department of Electronics and Communication Engineering

VARDHAMAN COLLEGE OF ENGINEERING

(Approved by AICTE, Affiliated to JNTUH & Accredited by NBA)

2

2010 - 11

TABLE OF CONTENTS

ABSTRACT 4

CHAPTER 1 : INTRODUCTION 5

SECTION 1.1 : MEMRISTOR 5SECTION 1.2 : THEORY 6

CHAPTER 2 : DETAILED DESCRIPTION 8

SECTION 2.1 : STRUCTURE OF TITANIUM DIODE MEMRISTOR 8SECTION 2.2 : WORKING 9SECTION 2.3 : IMPLEMENTATION OF OTHER TYPES OF MEMRISTORS 12SECTION 2.4 : ADVANTAGES 14SECTION 2.5 : PROBLEMS 15SECTION 2.6 : ARRAY BASED MULTILEVEL MEMORY OF MEMRISTOR 20SECTION 2.7 : MEMRISTOR WRITE-IN CIRCUIT 21SECTION 2.8 : MEMRISTOR READ-OUT/RESTORATION CIRCUIT 23SECTION 2.9: SIMULATONS 24

CHAPTER 3 : FUTURE SCOPE 28

CHAPTER 4 : CONCLUSION 31

3

CHAPTER 5 : BIBILOGRAPHY 32

ABSTRACT

Typically electronics has been defined in terms of three fundamental

elements such as resistors, capacitors and inductors. These three elements are used to define the

four fundamental circuit variables which are electric current, voltage, charge and magnetic flux.

Resistors are used to relate current to voltage, capacitors to relate voltage to charge, and

inductors to relate current to magnetic flux, but there was no element which could relate charge

to magnetic flux.

To overcome this missing link, scientists came up with a new element called

Memristor. These Memristor has the properties of both a memory element and a resistor (hence

wisely named as Memristor). Memristor is being called as the fourth fundamental component,

hence increasing the importance of its innovation

4

1. INTRODUCTION

1.1 Memristor

For nearly 150 years, the known fundamental passive circuit elements were limited to the

capacitor (discovered in 1745), the resistor (1827), and the inductor (1831). Then, in a brilliant

but underappreciated 1971 paper, Leon Chua, a professor of electrical engineering at the

University of California, Berkeley, predicted the existence of a fourth fundamental device, which

he called a memristor. He proved that memristor behaviour could not be duplicated by any

circuit built using only the other three elements, which is why the memristor is truly

fundamental.

Memristor is a contraction of “memory resistor,” because that is exactly its function:

to remember its history. A memristor is a two-terminal device whose resistance depends on the

magnitude and polarity of the voltage applied to it and the length of time that voltage has been

applied. When you turn off the voltage, the memristor remembers it’s most recent resistance until

the next time you turn it on, whether that happens a day later or a year later

Chua discovered a missing link in the pair wise mathematical equations that relate the four

circuit quantities—charge, current, voltage, and magnetic flux—to one another. These can be

related in six ways. Two are connected through the basic physical laws of electricity and

magnetism, and three are related by the known circuit elements: resistors connect voltage and

current, inductors connect flux and current, and capacitors connect voltage and charge. But one

equation is missing from this group: the relationship between charge moving through a circuit

and the magnetic flux surrounded by that circuit

Chua demonstrated mathematically that his hypothetical device would provide a

relationship between flux and charge similar to what a nonlinear resistor provides between

voltage and cur- rent. In practice, that would mean the device’s resistance would vary according

to the amount of charge that passed through it. And it would remember that resistance value even

5

after the current was turned off.

6

1.2 THEORY

Memristor symbol.

The memristor is formally defined as a two-terminal element in which the magnetic flux Φm

between the terminals is a function of the amount of electric charge q that has passed through the

device. Each memristor is characterized by its memristance function describing the charge-

dependent rate of change of flux with charge.

Noting from Faraday's law of induction that magnetic flux is simply the time integral of voltage,

and charge is the time integral of current, we may write the more convenient form

It can be inferred from this that memristance is simply charge-dependent resistance. If M (q (t))

is a constant, then we obtain Ohm's Law R (t) = V (t)/ I (t). If M (q (t)) is nontrivial, however, the

equation is not equivalent because q (t) and M (q (t)) will vary with time. Solving for voltage as a

function of time we obtain

This equation reveals memristance defines a linear relationship between current and voltage, as

long as M does not vary with charge. Of course, nonzero current implies time varying charge.

7

Alternating current, however, may reveal the linear dependence in circuit operation by inducing

a measurable voltage without net charge movement as long as the maximum change in q does

not cause much change in M.

Furthermore, the memristor is static if no current is applied. If I (t) = 0, we find V (t) = 0 and

M (t) is constant. This is the essence of the memory effect.

The power consumption characteristic recalls that of a resistor, I2R.

As long as M (q (t)) varies little, such as under alternating current, the memristor will appear as a

resistor. If M (q (t)) increases rapidly, however, current and power consumption will quickly

stop.

8

2. DETAILED DISCRIPTION

2.1 Structure of Titanium Diode Memristor

IO

Fig 2.1.1

9

The HP device is composed of a thin (50 nm) titanium dioxide film between two 5

nm thick electrodes, two platinum wires

Initially, there are two layers to the titanium dioxide film, TiO2 and TiO2-x. The upper

layer has a slight depletion of oxygen atoms. The oxygen vacancies are donors of electrons

which makes the vacancies themselves positively charged. Stoichiometric TiO2 act as an

insulator

(It is a semiconductor) but oxygen deficient TiO2-x is a conductor and have lower resistance

than the stoichiometric compound

2.2 Working

Fig 2.2.1

10

If a positive voltage is applied to the top electrode of the device, it will repel the (also positive)

oxygen vacancies in the TiO2-x layer down into the pure TiO2 layer. That turns the TiO2 layer

into TiO2-x and makes it conductive, thus turning the device on. A negative voltage has the

opposite effect: the vacancies are attracted upward and back out of the TiO2, and thus the thick-

ness of the TiO2 layer increases and the device turns off.

The oxygen deficiencies in the TiO2-x manifest as “bubbles” of oxygen vacancies

scattered throughout the upper layer. A positive voltage on the switch repels the (positive)

oxygen deficiencies in the metallic upper TiO2-x layer, sending them into the insulating TiO2

layer below. That causes the boundary between the two materials to move down, increasing the

percentage of conducting TiO2-x and thus the conductivity of the entire switch. The more

positive voltage is applied, the more conductive the cube becomes.

A negative voltage on the switch attracts the positively charged oxygen bubbles, pulling

them out of the TiO2. The amount of insulating, resistive TiO2 increases, thereby making the

switch as a whole resistive. The more negative voltage is applied, the less conductive the cube

becomes. What makes this switch special is that when the voltage is turned off, positive or

negative, the oxygen bubbles do not migrate. They stay where they are, which means that the

boundary between the two titanium dioxide layers is frozen. That is how the memristor

“remembers” how much voltage was last applied.

Resistance also depends on the length of time that voltage has been applied

A memristor’s structure, shown here in a scanning tunnelling microscope image, will enable

dense, stable computer memories.

Fig 2.2.2

Bow Ties

Leon Chua’s original graph of the hypothetical memristor’s behavior is shown at top right;

the graph of R. Stanley William’s experimental results are shown below. The loops map the

switching behavior of the device: it begins with a high resistance, and as the voltage increases,

the current slowly increases. As charge flows through the device, the resistance drops, and the

current increases more rapidly with increasing voltage until the maximum is reached. Then, as

the voltage decreases, the current decreases but more slowly, because charge is flowing through

the device and the resistance is still dropping. The result is an on-switching loop. When the

voltage turns negative, the resistance of the device increases, resulting in an off-switching loop

Fig 2.2.3

2.3 Implementation of Other Types of Memristors:

Spintronic Memristor

Concept of Spintronic memristor is given as, resistance is caused by the

spin of electrons in one section of the device pointing in a different direction than

those in another section, creating a "domain wall," a boundary between the two

states. Electrons flowing into the device have a certain spin, which alters the

magnetization state of the device. Changing the magnetization, in turn, moves the

domain wall and changes the device's resistance.

Spin Torque Magnetoresistance

Spin Torque Transfer MRAM is a well-known device that exhibits memristive behavior.

The resistance is dependent on the relative spin orientation between two sides of a magnetic

tunnel junction. This in turn can be controlled by the spin torque induced by the current flowing

through the junction. However, the length of time the current flows through the junction

determines the amount of current needed, i.e., the charge flowing through is the key variable.

Additionally, MgO based magnetic tunnel junctions show memristive behavior based on the drift

of oxygen vacancies within the insulating MgO layer (resistive switching). Therefore, the

combination of spin transfer torque and resistive switching leads naturally to a second-order

memristive system with w=(w1,w2) where w1 describes the magnetic state of the magnetic tunnel

junction and w2 denotes the resistive state of the MgO barrier. Note that in this case the change of

w1 is current-controlled (spin torque is due to a high current density) whereas the change of w2 is

voltage-controlled (the drift of oxygen vacancies is due to high electric fields).

Polymeric Memristor

Juri H. Krieger and Stuart M. Spitzer claim to have developed a polymeric memristor

before the titanium dioxide memristor more recently announced.

There work describes the process of dynamic doping of polymer and inorganic

dielectric-like materials in order to improve the switching characteristics and retention required

to create functioning nonvolatile memory cells. Described is the use of a special passive layer

between electrode and active thin films, which enhances the extraction of ions from the

electrode. It is possible to use fast ion conductor as this passive layer, which allows to

significantly decreasing the ionic extraction field

Resonant Tunnelling Diode Memristor

1994, F. A. Buot and A. K. Rajagopal demonstrated that a 'bow-tie' current-voltage (I-V)

characteristics occurs in AlAs/GaAs/AlAs quantum-well diodes containing special doping design

of the spacer layers in the source and drain regions, in agreement with the published

experimental results This 'bow-tie' current-voltage (I-V) characteristic is sine qua non of a

memristor although the term memristor is not explicitly mentioned in their papers. No magnetic

interaction is involved in the analysis of the 'bow-tie' I-V characteristics

3-Terminal Memristor

Although the memristor is defined in terms of a 2-terminal circuit element, there was an

implementation of a 3-terminal device called a memistor developed by Bernard Widrow in 1960.

Memistors formed basic components of a neural network architecture called ADALINE

developed by Widrow and Ted Hoff the memistor was described as follows:

Like the transistor, the memistor is a 3-terminal element. The conductance between two of

the terminals is controlled by the time integral of the current in the third, rather than its

instantaneous value as in the transistor. Reproducible elements have been made which are

continuously variable (thousands of possible analog storage levels), and which typically vary in

resistance from 100 ohms to 1 ohm, and cover this range in about 10 seconds with several mille

amperes of plating current. Adaptation is accomplished by direct current while sensing the

neuron logical structure is accomplished nondestructively by passing alternating currents through

the arrays of memistor cells.

Since the conductance was described as being controlled by the time integral of current

as in Chua's theory of the memristor, the memistor of Widrow may be considered as a form of

memristor having three instead of two terminals. However, one of the main limitations of

Widrow's memistor was that they were made from an electroplating cell rather than as a solid-

state circuit element. Solid-state circuit elements were required to achieve the scalability of the

integrated circuit which was gaining popularity around the same time as the invention of

Widrow's memistor.

2.4 Advantages

When you turn off the voltage, the memristor remembers its most recent resistance until

the next time you turn it on, whether that happens a day later or a year later

This freezing property suits memristors brilliantly for computer memory. The ability to

indefinitely store resistance values means that a memristor can be used as a nonvolatile memory.

That might not sound like very much, but go ahead and pop the battery out of your laptop, right

now—no saving, no quitting, nothing. You’d lose your work, of course. But if your laptop were

built using a memory based on memristors, when you popped the battery back in, your screen

would return to life with everything exactly as you left it: no lengthy reboot, no half-dozen auto-

recovered files.

There are several advantages of the memristor memory over conventional transistor-

based memories. One is its strikingly small size. Though memristor is still at its early

development stage, its size is at most one tenths of its RAM counterparts. If the fabrication

technology for memristor is improved, the size and advantage could be even more significant.

Another feature of the memristor is its incomparable potential to store analog information which

enables the memristor to keep multiple bits of information in a memory cell. Besides these

features, the memristor is also an ideal device for implementing synaptic weights in artificial

neural networks

Williams' solid-state memristors can be combined into devices called crossbar

latches, which could replace transistors in future computers, taking up a much smaller area.

They can also be fashioned into non-volatile solid-state memory, which would allow

greater data density than hard drives with access times potentially similar to DRAM, replacing

both components HP prototyped a crossbar latch memory using the devices that can fit 100

gigabits in a square centimeter, and has designed a highly scalable 3D design (consisting of up to

1000 layers or 1 petabit in a cubic CM) has reported that its version of the memristor is

currently about one-tenth the speed of DRAM . The devices' resistance would be read with

alternating current so that they do not affect the stored value.

2.5 Problems

Despite many favorable features, memristors have several weaknesses in practice. One

weakness comes from the nonlinearity in the Ø vs. q curve which makes it difficult to determine

the proper pulse width for achieving a desired resistance value. If the nonlinearity is spatially

variant in the die of a chip which is common in the fabrication process, the difficulty could be

very serious. Another difficulty comes from the property of the memristor which integrates any

kind of signals including noise that appeared at the memristor and results in the memristors being

perturbed from its original pre-set values.

The principle of the memristor is based on the nonlinear property of basic circuit elements.

In the relationships defining basic circuit elements, charge is defined as the time integral of

current, namely,

Thus, the resistance can be interpreted as the slope at an operating point on the Ø- q curve. If theØ- q curve is nonlinear, the resistance will vary with the operating point. For instance, if the

Ø - q curve is the nonlinear function

Shown in Fig. 2.4.1, its small-signal resistance can be obtained by re-plotting it as a function of

Øq in the R vs .Ø plane as in Fig. 2.4.2.

Since the flux Ø is obtained by integrating the voltage, the resistance of the memristor can be

Controlled by applying a voltage signal across the memristor, where

Fig 2.4.1

Fig 2.4.2

Fig 2.4.3

The above memristance tuning method assumes an ideal operating condition. In

practice, there are some problems that must be overcome. The first problem is caused by the

nonlinearity between the applied voltage and the corresponding resistance. Suppose the

resistance characteristics of the memristors is different from each other as shown in Fig. 2.4.3,

where the resistance R d is obtained at different values of Ø such as Ø1

Ø2 and Ø3. If the same magnitude of voltage pulses is chosen, then the durations of the pulse

widths for obtaining the same resistance will be different depending on the characteristics of the

memristors.

Another problem comes from the fact that the operating point and its associated

memristance would be changed whenever some voltage is applied across the memristor. The

voltage applied for read-out or even noise voltages would be integrated which causes the flux

Ø to be altered. Again, this causes the programmed resistance to be varied. Chua had suggested

applying a voltage doublet with equal positive and negative read-out pulses to resolve such

problem. However, the problem remains if the positive and the negative pulses are not

perfectly identical due to the non-ideal pulse-generation circuits.

2.6 Array Based Multilevel Memory of Memristor

The proposed method has the operating point of the memristor be maintained its desired

location (or resistance value) utilizing a set of pre-determined multiple resistance levels. Fig.

2.5.1 shows the basic idea of the proposed method, where the resistance array to be referenced

and the memristor to be programmed (tuned) are shown. The goal is to have the memristor keep

any of the resistance level selected from the resistance array. If a predetermined magnitude of

the current pulse Is (t) is applied to the resistance array, different levels of voltages V k will

appear at each node of the resistance array. The same current pulse Is (t ) is also applied to the

memristor.

Fig 2.5.1

The programming (tuning) of the memristor is performed by applying additional current

pulses to the memristor with the appropriate directions until the voltage of the memristor equals

to that of the selected node voltage in the resistance array. If the voltage of the memristor reaches

that of the selected node, the resistance value of the memristor becomes the same as the partial

sum of the resistance from the ground to the selected node of the resistance array.

This idea is employed in both the “write-in” and the “read-out/restoration” circuits.

Detailed description of these circuits will be presented in the following sections.

2.7 Memristor Write-In Circuit

Fig 2.6.1

The memristor write-in circuit is used to bias the memristor at a desired resistance level.

The critical write-in circuit is shown in Fig 2.6.1. The first step is to choose the write-in

memristor and the resistance value to be memorized by turning on one of the switches in switch

array S1 of Fig2.6.1. and the corresponding switch pair in switch array S4 respectively. Then,

an initial current pulse I s(t) is applied at the drain of the transistor Q1 so that its mirrored

current pulses appear at transistors Q2 and Q3. With this current pulse, negative voltages appear

at both the selected reference nodes and at the output terminal V out of the memristors.

Suppose the selected memristance M j is less than the referenced sum of the resistances

Rk sum in Fig2.6.1. In this particular case, Diffk+ is smaller than Diffk- sinceVout (Tp) is less

negative than that of Vk (Tp). These Diffk outputs caused the comparator C1 to generate a

positive pulse. Note that the negative and the positive output terminals of Diffk are linked to the

positive and the negative input terminals of C1 respectively. As a consequence, switch S3 is

turned on. Ø such increased flux Ø, the increment of the memristance can be obtained with a

monotonically increasing function via the R vs. Ø graph in Fig 2.6.1. As a consequence, the

memristor voltage decreases toward the selected reference level.

The processing from the above voltage difference computation repeats until the

difference between Vk (Tp ) and Vout (Tp) becomes zero, thereby completing the “write-in”

processing of the reference resistance Rk sum.

On the other hand, when the selected memristance M j is larger than the referenced

sum Rk sum of the resistances, the memristance of the selected memristor is decreased

and the memristor voltage is increased toward the selected reference level through the

opposite procedure mentioned above.

The above comparison between the voltages and the adjustment of the memristance are

repeated until the memristor voltage is equal to its selected reference voltage level.

2.8 Memristor Read-Out/Restoration Circuit

Fig 2.7.1

The memristor read-out/ restoration circuit is used to read the content of the memristor by applying

an appropriate integrating current or voltage. The critical function of this circuit is to guarantee the

memristor will stay at a set of fixed values without being perturbed when a read- out voltage or a noise

voltage is applied across the memristor. To achieve this goal, a single compensating pulse is applied to

have the memristance changed toward the closest reference resistance after the initial read-out pulse is

applied. The read-out circuit is the same as the write- in circuit except the negative signal excluding circuit

(N_Excld), MIN A and MIN B circuits as shown in Fig.2.7.1 . The N_Excld is the circuit to choose only

the positive signals from Diffk+ or Diffk- using the negative signal excluding circuit N_Excld by

comparing between the DC voltage and the output of the Diff circuit. The circuits MIN A and MIN B

together with the comparator C1 are used to choose the smallest absolute value among all

Diff k+ and Diffk- signals.

If the output of MIN A is smaller than that of MIN B, the memristor voltage is higher than that of its

closest reference voltage (with M j< Rk sum)

In this case, the memristance M j should be increased. On the other hand, if the output of the MIN A is

larger than that of MIN B, then the memristor voltage is smaller than that of its closest reference voltage

(with M j>Rk sum). In this case, M j should be decreased.

The above adjustment of the memristor is executed only once during each read-out processing.

2.9 Simulations

The write-in circuit and the read-out/restoration circuit of the proposed method have been

simulated extensively. All circuit components are assumed to be ideal. The simulations aim to check if

the memristors are written accurately with the prescribed resistance levels and if the memristor contents

are adjusted properly when they are altered by noise or read-out voltages. Also, it focuses on whether

the proposed circuits are working well when memristors with slightly different characteristics are used

in practice. All memristors used in this simulation are mathematical models because physical memristor

devices with prescribed .Ø vs. Q

Characteristics are not commercially available at the moment.

The first simulation is designed to test the write-in operation of three memristors with slightly

different characteristics. To have this simulation be as close to real experiments as possible, scientists

chose the characteristic curve of the HP memristor and two contrived variations. This simulation consists

of writing a fixed reference resistance of 18 kΩ on the three memristors which have different Ø-q

characteristics. The initial values of the memristors are randomly selected. Fig. 2.8.1shows the changes in

the R- Ø

Values while repeated writing pulses are applied.

The relatively larger movements of the lower points of each characteristic curve are due to the big

difference between the reference resistance and the initial memristance. Note that the relatively larger

movement of the lower points of each compensation pulse width generated by the pulse width

modulator (PWM) is proportional to the difference between the reference resistance and that of the

memristor. Also observe that, depending on its characteristics, different amounts ΔØ of the flux Ø are

required to write and maintain the same resistance level on each memristor. Despite significant

differences in the 3 memristor characteristics, the proposed method is able to write exactly the same

resistance level in all 3 memristors

Fig 2.8.1

Extensive simulations for testing the write-in function for multiple levels have also been

made. The number of levels have chosen to write-in the memristors is 8 and the model of the

memristor used in this simulations is chosen from the HP publication whose resistance ranges

from about 8K Ohm to 25.5 K Ohm. The memristors are allowed to have 8 equally spaced

resistance levels of {8.0, 10.5, 13, 15.5, 18, 20.5, 23, 25.5} K Ohm as in Fig. 2.8.2. Big red dots

are the desired writing levels and the initial resistance values are selected randomly. As shown in

the fig. 2.8.2, all memristor converge successfully to their desired values during repeated

applications of the write-in pulses to 20 memristor models.

Fig 2.8.2

Similar simulations have been made for the read-out/restoration circuit. The goal of this

circuit is to have the memristors to stay at fixed values without being perturbed when a read-out

voltage or any noise voltage is applied across the memristor by applying a single compensating

pulse after the initial read-out pulse. Extensive simulations on 8 memristors with 8 slightly

different characteristics have been made. The memristors are perturbed initially by a maximum

of 10% from their reference resistances. Fig. 2.8.3 shows traces of the resistance on the R-Ø

curve of a typical memristor. The big red dots are the desired levels and the small cross symbols

are the traces of the resistance changes while the read-out/restoration operation is performed.

Note that a single compensation pulse is generated during each read-out processing. Observe that

the resistance values in Fig. 2.8.3 converge to their closest levels with the read-out pulses.

Fig 2.8.3

3. FUTURE SCOPE

Future Scope:

Combined with transistors in a hybrid chip, memristors could radically improve the

performance of digital circuits without shrinking transistors. Using transistors more efficiently

could in turn give us another decade, at least, of Moore’s Law performance improvement,

without requiring the costly and increasingly difficult doublings of transistor density on chips. In

the end, memristors might even become the cornerstone of new analog circuits that compute

using an architecture much like that of the brain. Memristor’s potential goes far beyond instant-

on computers to embrace one of the grandest technology challenges: mimicking the functions of

a brain. Within a decade, memristors could let us emulate, instead of merely simulate, networks

of neurons and synapses. Many research groups have been working toward a brain in silico:

IBM’s Blue Brain project, Howard Hughes Medical Institute’s Janelia Farm, and Harvard’s

Center for Brain Science are just three. However, even a mouse brain simulation in real time

involves solving an astronomical number of coupled partial differential equations. A digital com-

puter capable of coping with this staggering workload would need to be the size of a small city,

and powering it would require several dedicated nuclear power plants.

Memristors can be made extremely small, and they function like synapses. Using them,

we will be able to build analog electronic circuits that could fit in a shoebox and function

according to the same physical principles as a brain. Memristors can potentially learn like

synapses and be used to build human brain-like computers

Two CMOS circuits connected by a memristor is analogous to two neurons in the brain

connected by a synapse. It is thought that synaptic connections strengthen as the neurons either

side fire and so brain 'circuits' are established which constitutes the basis of human learning.

Wei Lu, a University of Michigan scientist connected two CMOS circuits by a silver and silicon Memristor and powered the two CMOS circuits on and off with varying time gaps between them.

The memristor alters its state differently depending on the timing of the powering of the CMOS circuits.

This is said to be the same behaviour as that shown by synapses, called "spike timing

plastic dependency", which is thought to be the possible basis for memory and learning in

human and other mammalian brains.

The synaptic connection between neurons becomes stronger or weaker, as the time gap

between when they are stimulated becomes shorter or longer. In the same way, the shorter the

time interval the lower the resistance of the memristor to electricity flowing across it between

the two CMOS circuits.

A 20 millisecond time interval between the two CMOS circuits caused a resistance level

roughly half that of a 40 millisecond gap. Lu said: "Cells that fire together wire together... The

memristor mimics synaptic action.

"We show that we can use voltage timing to gradually increase or decrease the

electrical conductance in this memristor-based system. In our brains, similar changes in

synapse conductance essentially give rise to long term memory.

A hybrid circuit—containing many connected memristors and transistors—could help us

research actual brain function and disorders. Such a circuit might even lead to machines that can

recognize patterns the way humans can, in those critical ways computers can’t—for example,

picking a particular face out of a crowd even if it has changed significantly since our

last memory of it.

There are several advantages of the memristor memory over conventional transistor-

based memories. One is its strikingly small size. Though memristor is still at its early

development stage, its size is at most one tenths of its RAM counterparts. If the fabrication

technology for memristor is improved, the size and advantage could be even more significant.

Another feature of the memristor is its incomparable potential to store analog information

which enables the memristor to keep multiple bits of information in a memory cell.

Besides these features, the memristor is also an ideal device for implementing synaptic

weights in artificial neural networks.

HP already has plans to implement memristor in a new type of non-volatile memory

which could eventually replace flash and other memory systems.

Recently, a simple electronic circuit consisting of an LC network and a memristor was

used to model experiments on adaptive behaviour of unicellular organisms. It was shown that the

electronic circuit subjected to a train of periodic pulses learns and anticipates the next pulse to

come, similarly to the behaviour of slime moulds Physarumpolycephalum subjected to periodic

changes of environment. Such a learning circuit may find applications, e.g., in pattern

recognition

4. Conclusion

The reference resistance array-based multilevel memristor memory is proposed in this

paper. The idea has been implemented with two circuits namely the write-in and the read-out

circuits. Simulation of the write-in circuit shows that the memristors memorize the desired

discrete resistance levels regardless of their characteristic differences. In read-out simulation,

contents of the memristors move toward their original values from the deviated ones whenever

the read-out processing is performed.

The proposed multilevel idea of the memristor together with its intrinsic feature of small

size should make the memristor to be a powerful memory device. Also, if the number of

multilevel of memory is increased, the memristor could be an ideal element for synaptic weight

implementation since the synaptic multiplication can be performed simply by Ohm’s law V=IR

in the memristor.

Memristor is the fourth fundamental component. Thus the discovery of a brand new fundamental

circuit element is something not to be taken lightly and has the potential to open the door to a

brand new type of electronics. HP already has plans to implement memristors in a new type of

non-volatile memory which could eventually replace flash and other memory systems

5. BIBILIOGRAPHY

Hyongsuk Kim Sah, M.P. Changju Yang Chua, L.O.”Memristor based multilevel memory” Cellular Nanoscale Networks and Their Applications (CNNA), 2010 12th International Workshop, 3-5 Feb. 2010, pp1-6.

Memrisotr from Wikipedia, en.wikipedia.org/wiki/Memristor

http://www.memristor.org/reference/research/13/what-are-memristors

www.hpl.hp.com/news/2010/apr-jun/memristor.html