Memristors

ROLE OF MEMRISTORS IN ADVANCED ELECTRONIC   CIRCUIT THEORY

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. Its innovators say “Memristor are so significant that it would be mandatory to re-write the existing electronics engineering textbooks. “In this paper the introduction of Memristor and its role in advanced electronic circuits required to involved in processors, memory units and so on is presented

1. Definition of a Memristor

It is a two terminal passive element flux produced by it is proportional to charge flowing in it. The symbol of Memristor is given below.

So Memristor is a passive element which shoes fundamental relation between charge and flux

2. Flux –Charge Curve of a Memristor

Its φ-q curve is monotonically increasing. The slope of the φ–q curve is called Memristance M (q). Memristor is passive if and only if Memristance is non-negative. (M (q) ≥ 0).

Memristance is a transfer property of Memristor so it is simply impedance offered by Memristor. The current is defined as the time derivative of the charge and the voltage is defined as the time derivative of the flux.

Memristance is a property of the Memristor. When charge flows in a direction through a circuit, the resistance of the Memristor increases. When it flows in the opposite direction, the resistance of the Memristor decreases. If the applied voltage is turned off, thus stopping the flow of charge, the Memristor remembers the last resistance that it had. When the flow of charge is started again, the resistance of the circuit will be what it was when it was last active. So the Memristor is essentially a two-terminal variable resistor.

3. MODEL OF THE MEMRISTOR FROM HP LABS

In 2008, thirty-seven years after Chua proposed the Memristor, Stanley Williams and his group at HP Labs realized the Memristor in device form. To realize a Memristor, they used a very thin film of titanium dioxide (TiO2). The thin film is sandwiched between two platinum (Pt) contacts and one side of TiO2 is doped with oxygen vacancies. The oxygen vacancies are positively charged ions. Thus, there is a TiO2 junction where one side is doped and the other side is undoped. The device established by HP is shown in Fig.

D is the device length and w is the length of the doped region. Pure TiO2 is a semiconductor and has high resistivity. The doped oxygen vacancies make the TiO2-x material conductive. When a positive voltage is applied, the positively charged oxygen vacancies in the TiO2-x layer are repelled,

Moving them towards the undoped TiO2 layer. As a result, the boundary between the two materials moves, causing an increase in the percentage of the conducting TiO2-x layer. This increases the conductivity of the whole device. When a negative voltage is applied, the positively charged oxygen vacancies are attracted, pulling them out of TiO2 layer. This increases the amount of insulating TiO2, thus increasing the resistivity of the whole device. When the voltage is turned off, the oxygen vacancies do not move. The boundary between the two titanium dioxide layers is frozen. This is how the Memristor remembers the voltage last applied.

The simple mathematical model of the HP Memristor is given by

R_off    = High resistance state

R_on     = Low resistance state

W      =  width of doped region

D       = Thickness of semiconductor film sandwiched between two metal contacts.

4. Current–Voltage Curve of a Memristor

Memristor has the pinched hysteresis loop current voltage characteristic.

Another signature of the Memristor is that the pinched hysteresis loop shrinks with the increase in the excitation frequency. Figure shows the pinched hysteresis loop‖ and an example of the loop shrinking with the increase in frequency. In fact, when the excitation frequency increases towards infinity, the Memristor behaves as a normal resistor.

5.DC and AC responses
DC response:

This example shows a Memristor, a recently discovered device. It acts as a resistor, but the resistance varies depending on the current over time. In this example, use the slider at right to select the input voltage. The Memristor has a high resistance at first, but current flow causes the resistance to decrease over time until it hits a minimum value. If you set the input voltage to a negative value, then the resistance will gradually increase until it hits a maximum value. A graph of the memristors voltage, current, and resistance is shown below the circuit.

Sine response

The graphs below the circuit show the memristors voltage (in green), current (in yellow), and resistance (in white). A graph of voltage versus current is also shown. Note that the voltage has a nonlinear relationship to current.

Square  response

The graphs below the circuit show the memristors voltage (in green), current (in yellow), and resistance (in white). A graph of voltage versus current is also shown.

Triangular response

 The graphs below the circuit show the memristors voltage (in green), current (in yellow), and resistance (in white). A graph of voltage versus current is also shown.

6. APPLICATIONS OF MEMERISTOR

Some of applications of Memristor are given below

  A.arithmethic operations

  B.logic operations

  C.memory unit

Basic arithmetic operations

For performing any arithmetic operation such as addition, subtraction, multiplication or division, at first, two operands should be represented by some ways. In almost all of currently working circuits, signal values are represented by voltage or current. However, as explained in previous section, analog values can be represented by the Memristance of the Memristor. When 2 Memristor connected in series there corresponding Memristor are added. By this concept addition operation is done

Any subtraction, such as M1−M2 , can be written as M1+ (− M2). This means that for doing subtraction, Memristor should be connected in series with another Memristor which its Memristance is –M2 .

A simple opamp-based inverting amplifier which intrinsically is a Memristance divider. The output voltage of this circuit is –M2/M1

In bellow circuit output is (M1+M2)(M1||M2) =M1.M2 so it performs multiplication operation

Logic operations

Consider the set of memristors as shown in Fig.24 shows nand gate. The Memristor Mem1 of inverting configuration is replaced by set of memristors Mem1-Mem3, which are connected in parallel. The control terminals of Mem1-Mem3 are connected. The Memristor Mem4 is unconditionally open by applying a high negative voltage – at the control terminal. Then a voltage is applied at the common control terminal and is applied to the control of Mem4. In the scenario where memristors Mem1-Mem3 are open, the voltage at the terminal X is close to 0. The voltage drop across Mem4 is , which is enough to close Mem4. In the scenario where one Memristor Mem1 is closed and Mem2 and Mem3 are open, the intermediate node settles close to and the voltage across Mem4 is not enough to close the Memristor. Similar results occur when Mem2 or Mem3 are open. Hence, the logical computation can be treated as Mem4 = (Mem1.Mem2.Mem3)` which is NAND operation. This configuration is referred to as ‗wired-AND‘ as various inputs are wired together to produce result.

Memristor Memory

Next, the sense amplifier stage as shown in Fig. 6 fully

converts the sensed memristor state to a full-swing digital output. The voltage Vx will be compared with the referencevoltage Vref  which is half of Vin. If the memristor stores logic zero, Vx is less than Vref and output Vo is VL. If memristor stores logic one, Vx is greater than Vref and output Vo would be VH.

Fig.  illustrates a memristor-based memory array with peripheral circuits. Just like a typical memory array such as that of DRAM, it still has row decoder, sense amplifier and column selector/decoder. In addition, there is a pulse generator unit and a selector unit. Pulse generator generates read/writes pattern signals shown in Fig. 4(b) and Fig. 5(c). In Fig. 7, when Pselect signal is high, NMOSs are short and

PMOSs are open, signal directly goes through. If Pselect signal is low, NMOSs are open and PMOSs are short so signal gets negative in sign. Furthermore, the purpose of the selector unit is to switch the memristor to ground for a write operation and Rx for a read operation. Read Enable (RE) signal controls the MUX to switch properly depending on whether it is a read or a write operation.

7. BENEFITS OF USING MEMRISTORS

The advantages of using Memristor are as given below:

Ø  It provides greater resiliency and reliability when power is interrupted in data centers.

Ø  Memory devices built using Memristor have greater data density.

Ø   Faster and less expensive than present day devices

Ø  Uses less energy and produces less heat.

Ø  Would allow for a quicker boot up since information is not lost when the device is turned off.

Ø  The information is not lost when the device is turned off.

Ø  A very important advantage of Memristor is that when used in a device, it can hold any value between 0 and 1. However present day digital devices can hold only 1 or 0. This makes devices implemented using Memristor capable of handling more data.

8. Future Research

Recently, researchers have defined two new memdevices- memcapacitor and meminductor, thus generalizing the concept of memory devices to capacitors and inductors. These devices also show ―pinched‖ hysteresis loops in two constitutive variables— charge—voltage for the memcapacitor and current—flux for meminductor. Figure 13 shows the symbols for the memcapacitor and the meminductor.

Conclusion:-

 In a system contains mainly ALU and memory units. But they size can be reduced when they fabricate with memristors .so it leads a revolution in electronics

REFERENCE

www.memristor.org

en.wikipedia.org/wiki/Memristor

www.hpl.hp.com/news/2011/

http://spectrum.ieee.org/semiconductors/

http://highscalability.com/blog/2010

 http://www.wired.com

http://arxiv.org

Brain Control Motor

 Brain Control Motor

The brain is "hardwired" with connections, which are made by billions of neurons that make electricity whenever they are stimulated. The electrical patterns are called brain waves. Neurons act like the wires and gates in a computer, gathering and transmitting electrochemical signals over distances as far as several feet. The brain encodes information not by relying on single neurons, but by spreading it across large populations of neurons, and by rapidly adapting to new circumstances.

Motor neurons carry signals from the central nervous system to the muscles, skin and glands of the body, while sensory neurons carry signals from those outer parts of the body to the central nervous system. Receptors sense things like chemicals, light, and sound and encode this information into electrochemical signals transmitted by the sensory neurons. And interneurons tie everything together by connecting the various neurons within the brain and spinal cord. The part of the brain that controls motor skills is located at the ear of the frontal lobe.

How does this communication happen? Muscles in the body's limbs contain embedded sensors called muscle spindles that measure the length and speed of the muscles as they stretch and contract as you move. Other sensors in the skin respond to stretching and pressure. Even if paralysis or disease damages the part of the brain that processes movement, the brain still makes neural signals. They're just not being sent to the arms, hands and legs.

à A technique called neurofeedback uses connecting sensors on the scalp to translate brain waves into information a person can learn from. The sensors register different frequencies of the signals produced in the brain. These changes in brain wave patterns indicate whether someone is concentrating or suppressing his impulses, or whether he is relaxed or tense.

NEUROPROSTHETIC DEVICE:

A neuroprosthetic device known as Braingate converts brain activity into computer commands. A sensor is implanted on the brain, and electrodes are hooked up to wires that travel to a pedestal on the scalp. From there, a fiber optic cable carries the brain activity data to a nearby computer.

PRINCIPLE:

"The principle of operation of the BrainGate Neural Interface System is that with intact brain function, neural signals are generated even though they are not sent to the arms, hands and legs. These signals are interpreted by the System and a cursor is shown to the user on a computer screen that provides an alternate "BrainGate pathway". The user can use that cursor to control the computer, just as a mouse is used."

 BrainGate is a brain implant system developed by the bio-tech company Cyberkinetics in 2003 in conjunction with the Department of Neuroscience at Brown University. The device was designed to help those who have lost control of their limbs, or other bodily functions, such as patients with amyotrophic lateral sclerosis (ALS) or spinal cord injury. The computer chip, which is implanted into the brain, monitors brain activity in the patient and converts the intention of the user into computer commands

.

NUERO CHIP:

Currently the chip uses 100 hair-thin electrodes that 'hear' neurons firing in specific areas of the brain, for example, the area that controls arm movement. The activity is translated into electrically charged signals and are then sent and decoded using a program, which can move either a robotic arm or a computer cursor. According to the Cyberkinetics' website, three patients have been implanted with the BrainGate system. The company has confirmed that one patient (Matt Nagle) has a spinal cord injury, whilst another has advanced ALS.

In addition to real-time analysis of neuron patterns to relay movement, the Braingate array is also capable of recording electrical data for later analysis. A potential use of this feature would be for a neurologist to study seizure patterns in a patient with epilepsy.

Braingate is currently recruiting patients with a range of neuromuscular and neurodegenerative conditions for pilot clinical trials in the United States.

WORKING:

                      Operation of the BCI system is not simply listening the EEG of user in a way that let’s tap this EEG in and listen what happens. The user usually generates some sort of mental activity pattern that is later detected and classified.                   

PREPROCESSING:

  The raw EEG signal requires some preprocessing before the feature extraction. This preprocessing includes removing unnecessary frequency bands, averaging the current brain activity level, transforming the measured scalp potentials to cortex potentials and denoising

DETECTION:

The detection of the input from the user and them translating it into an action could be considered as key part of any BCI system. This detection means to try to find out these mental tasks from the EEG signal. It can be done in time-domain, e.g. by.                                 comparing amplitudes of the EEG and in frequency-domain. This involves usually digital signal processing for sampling and band pass filtering the signal, then calculating these time -or frequency domain features and then classifying them. These classification algorithms include simple comparison of amplitudes linear and non-linear equations and artificial neural networks. By constant feedback from user to the system and vice versa, both partners gradually learn more from each other and improve the overall performance.

CONTROL:        

The final part consists of applying the will of the user to the used application. The user chooses an action by controlling his brain activity, which is then detected and classified to corresponding action. Feedback is provided to user by audio-visual means e.g. when typing with virtual keyboard, letter appears to the message box etc.

TRAINING: 

  The training is the part where the user adapts to the BCI system. This training begins with very simple exercises where the user is familiarized with mental activity which is used to relay the information to the computer. Motivation, frustration, fatigue, etc. apply also here and their effect should be taken into consideration when planning the training procedures                                                                                  

BIO FEEDBACK: The definition of the biofeedback is biological information which is returned to the source that created it, so that source can understand it and have control over it. This biofeedback in BCI systems is usually provided by visually, e.g. the user sees cursor moving up or down or letter being selected from the alphabet.

                               boon to the paralyzed -Brain Gate Neural Interface System

The first patient, Matthew Nagle, a 25-year-old Massachusetts man with a severe spinal cord injury, has been paralyzed from the neck down since 2001. Nagle is unable to move his arms and legs after he was stabbed in the neck. During 57 sessions, at New England Sinai Hospital and Rehabilitation Center, Nagle learned to open simulated e-mail, draw circular shapes using a paint program on the computer and play a simple videogame, "neural Pong," using only his thoughts. He could change the channel and adjust the volume on a television, even while conversing. He was ultimately able to open and close the fingers of a prosthetic hand and use a robotic limb to grasp and move objects. Despite a decline in neural signals after few months, Nagle remained an active participant in the trial and continued to aid the clinical team in producing valuable feedback concerning the BrainGate technology.

NAGLE’S STATEMENT:

“I can't put it into words. It's just—I use my brain. I just thought it. I said, "Cursor go up to the top right." And it did, and now I can control it all over the screen. It will give me a sense of independence.”

                                                                       

OTHER APPLICATIONS:

Rats implanted with BCIs in Theodore Berger's experiments.Several laboratories have managed to record signals from monkey and rat cerebral cortexes in order to operate BCIs to carry out movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and without any motor output. Other research on cats has decoded visual signals.

Garrett Stanley's recordings of cat vision using a BCI implanted in the lateral geniculate nucleus (top row: original image; bottom row: recording)

In 1999, researchers led by Garrett Stanley at Harvard University decoded neuronal firings to reproduce images seen by cats. The team used an array of electrodes embedded in the thalamus (which integrates all of the brain’s sensory input) of sharp-eyed cats. Researchers targeted 177 brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. The cats were shown eight short movies, and their neuron firings were recorded. Using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognisable scenes and moving objects.

In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor-cortex neurons in rhesus macaque monkeys and the direction that monkeys moved their arms (based on a cosine function). He also found that dispersed groups of neurons in different areas of the brain collectively controlled motor commands but was only able to record the firings of neurons in one area at a time because of technical limitations imposed by his equipment.^[4]

There has been rapid development in BCIs since the mid-1990s.^[5] Several groups have been able to capture complex brain motor centre signals using recordings from neural ensembles (groups of neurons) and use these to control external devices, including research groups led by Richard Andersen, John Donoghue, Phillip Kennedy, Miguel Nicolelis, and Andrew Schwartz.

Diagram of the BCI developed by Miguel Nicolelis and collegues for use on Rhesus onkeys

Later experiments by Nicolelis using rhesus monkeys, succeeded in closing the feedback loop and reproduced monkey reaching and grasping movements in a robot arm. With their deeply cleft and furrowed brains, rhesus monkeys are considered to be better models for human neurophysiology than owl monkeys. The monkeys were trained to reach and grasp objects on a computer screen by manipulating a joystick while corresponding movements by a robot arm were hidden.^[8][9] The monkeys were later shown the robot directly and learned to control it by viewing its movements. The BCI used velocity predictions to control reaching movements and simultaneously predicted hand gripping force.

Other labs that develop BCIs and algorithms that decode neuron signals include John Donoghue from Brown University, Andrew Schwartz from the University of Pittsburgh and Richard Andersen from Caltech. These researchers were able to produce working BCIs even though they recorded signals from far fewer neurons than Nicolelis (15–30 neurons versus 50–200 neurons).

Donoghue's group reported training rhesus monkeys to use a BCI to track visual targets on a computer screen with or without assistance of a joystick (closed-loop BCI).^[10] Schwartz's group created a BCI for three-dimensional tracking in virtual reality and also reproduced BCI control in a robotic arm.          

CONCLUSION: The idea of moving robots or prosthetic devices not by manual control, but by mere “thinking” (i.e., the brain activity of human subjects) has been a fascinated approach. Medical cures are unavailable for many forms of neural and muscular paralysis. The enormity of the deficits caused by paralysis is a strong motivation to pursue BMI solutions. So this idea helps many patients to control the prosthetic devices of their own by simply thinking about the task.

                               This technology is well supported by the latest fields of Biomedical Instrumentation, Microelectronics, signal processing, Artificial Neural Networks and Robotics which has overwhelming developments. Hope these systems will be effectively implemented for many Biomedical applications.

      The