N1

NANO TECHNOLOGY

Abstract: Nanotechnology is the hybrid science combining engineering and chemistry that have applications in the real world. The area of nanotechnology lets one build elaborate structures ,atom by atom ,on a scale of 1 to 100 nanometers that can store information, switch electrical signals, convert sunlight to electricity. A nanometer is a billionth of a meter that is about 1/80,000 of the diameter of a human hair, or 10 times the diameter of a hydrogen atom. In this paper we had dealt with the main concepts involved in the field of nanotechnology which are as follows:  BioComputing  Molecular Computing  Quantum Computing  Optical Computing Atoms and molecules stick together because they have complementary shapes that lock together, or charges that attract. Just like magnets a positively charged atom will stick to a negatively charged atom. A specific product will take shape as millions of these atoms are pieced together by nanomachines. The goal of nanotechnology is to manipulate atoms individually and place them in a pattern to produce a desired structure of small size that spreads its wings in the modern trends. Reports indicate that Israeli scientists have built a DNA computer to tiny that a trillion of them could fit in a test tube and perform a billion operations per second with 99.8 per cent accuracy. Researchers also found that a self assembled molecule could

sustain a current of about 0.2 microamperes at five volts - which meant that the molecule could channel through itself roughly a million electron per second. Molecular devices can be used as memory elements that forms the basis for Nanotechnology.

Bio-Computing: Nanocomputers, though have several applications, the one that stirs the imagination is its identification of malfunctions in human beings by traveling inside the human body. The molecular machines inside the living cell already posses the repertoire of operations required to implement a universal computer.

A design for a biological

nanocomputer shows that a Turing machine can be realized by a basic cycle consisting of molecular recognition, two cleavages, two ligations and movement along a polymer, all controlled by allostreic conformational changes. Each of these operations is routinely performed by some molecular machine in the living cell, such as the ribosome, splicesome and the replisome. The computer’s input, and “software” are made up of DNA molecules.

For

“hardware” the computer uses two naturally occurring enzymes that manipulate DNA, Fokl, an enzyme that cuts DNA and Ligase and enzyme that seals two DNA molecules into one. When mixed together in solution, the software and hardware molecules operate in harmony on the input molecule to create the output molecule, forming a simple mathematical computing machine, known as a finite automaton. The automaton could be programmed to perform different tasks by selecting different subsets of the molecules. Both input and software molecules are designed to have one DNA strand longer than the other, resulting in a single strand overhand called a “sticky end. “Two molecules with complementary sticky ends can temporarily stick to each other (a process known as hybridization), allowing DNA Ligase to permanently seal them into one molecule. The sticky end of the input molecule encodes the current symbol and the current state of the computation, whereas the sticky end of each “software” molecule is designed to detect a particular state-symbol combination. A two-state, twosymbol automaton has four such combinations. For each combination, the nanocomputer

has two possible next moves, to remain in the same state or to change to the other state, allowing eight software molecules to cover all possibilities. In each processing step the input molecule hybridizes with a software molecule that has a complementary sticky end, allowing Ligase to seal them together using two ATP molecules as energy. Then comes Fok-I, detecting a special site in the software molecule known as the recognition site. It cleaves the input molecule in a location determined by the software molecule, thus exposing a sticky end that encodes the next input symbol and the next state of the computation. Once the last input symbol is processed, a sticky end encoding the final state of the computation is exposed and detected, again by hybridization and ligation, by one of two “output display” molecules. The resulting molecule, which reports the output of the computation, is made visible to the human eye in a process known as gel electrophoresis. The automation is so small that 1012 automata sharing the same software run independently and in parallel on inputs (which could in principle be distinct) in 120 µ 1 solution at room temperature. Their combined rate is 10 9 transitions per second, their transition fidelity is greater than 99.8% and together they consume less than 10 -10 Watt.

Using DNA for Basic Logical and Arithmetic Operations: After the potential power of DNA computing has been described by Adleman and Lipton, researchers have developed an interest in DNA computing for solving difficult computational problems. Guarnieri et al. and Vineet Gupta et.al have proposed DNA based methods to do arithmetic and logical operations. But in their methods, the strands representing result have to be polymerized for each and every instance of a specific operation, those strands are not reusable. The limitation can be overcome by using sticker-based method to perform arithmetic and logical operations. The advantage of the proposed method is that output values are computed and stored parallely. The strands which represent the output can be used repeatedly any number of times. The main idea of this method is grouping the strands according to the output value are stored. The result tubes are the

tubes, which contain the result strands after completion of the annealing process with stickers.

Biological operations and Notations: Some of the biological operations used in this paper and their notations are described below

Initializing Stickers corresponding to input blocks and memory strands are poured into a tube to represent all possible inputs. initialize → (No)

Extracting Particular Strands in a test tube are extracted based on whether the stickers stick with specific region of memory strands or not S (test tube label, Region, 1) S (No, Io, 1) - extracts the strands from No with which sticker in the Ioth region. S (No, In, 1) - extracts the strands from No with which sticker not stuck in the Inth region. (Note : S’ (No, Io, 1) = S (No, Io, 0))

Setting Multiple copies of a particular sticker are poured with memory strands to make a specific region double stranded.

Set (N1, Rn+1, 1) - add multiple copies of sticker

complementary to Rn+1th region in N1.

Merging The strands in two or more tubes are poured into a single tube. No = merge (N 1, N2) - pour the strands in N1 and N2 into N0.

The proposed method To perform arithmetic operations between two binary numbers of length k or logical operations between two statements with k variables, start with 22k identical ssDNA (single stranded DNA) memory strands each n(3k + 1) nucleotides long, where n represent the number of nucleotides in a block. Each strand containing 3k +1 district contiguous blocks I1, I2,.....Ik, O1, O2, ....Ik, R1, R2,....Rk+1. There are 3k+1 stickers S1, S2, .... S3k+1. Input - I1, I2, .... Ik, Operand = O1, O2, ..... Ok

Output - O1, O2, .... Ik, wad. Operand store - R1, R2, .......RK+1

Constructing the result tubes The result tubes are constructed in three steps: initialization, separation and output step.

Initialization step The memory strands and multiple copies of stickers S1.... S2k are poured together. The stickers randomly anneal with memory strands making use of Watson-Crick complementarily of DNA. If a sticker anneals with a particular region of the strand, it assigns value 1 to the particular region of the strand. Otherwise it assigns the value zero. Care to be taken to see that all possible combinations of annealing are obtained by this random annealing process.

Algorithm for logical operations NAND Operation: Consider N0 as an initial tube. It contains all 22k strands, which are randomly annealed with stickers S1.... S2k to represent all possible inputs. That is N0 is properly initialized. Now, the strands are separated into two tubes N1 and N’1. N1 contains the strands with which sticker Sk is annealed (i.e. Kth position is encoded as 0). The strands in tube N1 is separated into two tubes N2 and N2’. N2 contains strands with which sticker S2k is annealed (i.e. kth position is encoded as 0). The strands in tube N1 is empty. The strands in N1’ and N2 are poured into N1 together. The strands in N1 represents the NAND operation between the numbers 0 & a, 1 & 0 or 0 & 0. The result for all above operations should be one. To represent the result in strands, multiple copies of stickers S 3k+1 are poured into N1. They stick with all strands in N1 and set the value 1 for the region Rk+1. The tube contains the strands representing the value 1 for the last digit of output. The remaining strands in tube N2 represent NAND operation between the number 1 & 1. The result for this operation is 0. So the strands in tube N 2 are left without any modifications, because the region Rk+1 already has value zero by default. Then the strands in N1 and N2 are poured together into N0. Now the tube N0 contains the strands, which represent all

possible inputs and the last digit of outputs. To represent the next digit of output in the strands, the strands in N0 are separated into two groups based on whether the sticker S3k should or should not stick with strands. That is, the strands are separated into two groups to represent the value 0 or 1 for the next digit of output. Multiple copies of sticker S 3k is pured into corresponding tube to stick the sticker S3k with the respective strands. Again all strands are poured together into N0. This process is repeated until sticker S2k+2 sticks with respective strands. Now result tube N0 contains all 22k strands, which represent all possible input values with it’s corresponding output values. Tube N0 is ready as a working area, particularly for NAND operation. The algorithm describes the above process is as follows. initialize → N0 for n = k to 1 { input (N0) N1 = S (N0, In , 1) N’1 = S’(N0, In, 1) N2 = S (N1, On, 1) N’2 = S’(N1, On, 1) N1 = merge (N’1, N’2) N1 = set (N1, Rn+1, 1) N0 = merge(N1, N2) } result tube = N0 Similarly algorithms can be written to get the result tube corresponding to other logical operations AND, OR, NOT, XOR, NOR etc. Since NAND operation is functionally complete, separation and output step for NAND operation have been described in detail above.

Molecular Computing: Notre Dame researchers have been developing an alternative approach which is naturally suited to molecular devices, molecules do make excellent structured charge

containers.

In the quantum-dot cellular automata (QCA) paradigm information is

represented by the charge configuration of a molecule. A QCA molecule is designed so that its ground-state charge configuration is determined by the state of its neighboring molecules through the Coulomb interaction. Current does not move between molecular “cells.” Instead, information moves without current flow. This approach is capable of supporting general-purpose computing and offers the possibility of extremely low power dissipation. In the QCA paradigm, the field from the charge configuration of one devices alters the charge configuration of the next device.

QCA Cells An idealized QCA cell can be viewed as a set of four charge containers, or “dots” positioned at the corners of a square. The cell contains two extra mobile electrons which can quantum-mechanically tunnel between dots but, by design, cannot tunnel between cells. The barrier between dots should be high enough so that charge can move only by tunneling and is therefore localized in the dots and not in the connectors.

The

configuration of charge within the cell is quantified by the cell polarization, which can vary between P= -1, representing a binary “0”, and P= +1, representing a binary “1” the potential of the QCA concept extends beyond Boolean circuits.

QCA Circuits QCA Circuits can be created by putting QCA cells in proximity to each other. A QCA binary wire is formed simply by creating a linear array of cells. The Coulomb interaction makes nearby cells align in the same state. The corner interaction is anti-voting so it can be used to make an inverter. The natural logic gate is the three-input majority gate. A full adder has been stimulated using the full self-consistent Schrodinger equation, verifying that the adder works for all input possibilities. For complex circuits it is useful to be able to clock the cells. Clocking consists of controlling the activity of the cell by effectively raising and lowering the interdot barriers.

Quantum Computing AC electrokinetic techniques such as dielectrophoresis and electrorotation have been used for many years for the manipulation, separation and analysis of cellular-scale particles. The phenomenon occurs due to the interaction of induced dipoles with electric fields, and can be used to exhibit a variety of motions including attraction, repulsion and

rotation by changing the nature of the dynamic field. AC electrokinetics offers advantages over scanning-probe methods of nanoparticle manipulation in that the equipment used is simple, cheap and has no moving parts, relying entirely on the electrostatic interactions between the particle and dynamic electric field.

Dielectrophoresis Dielectrophoresis is the manipulation of polarisable particles in non-uniform electric fields. It has been demonstrated to be effective for the manipulation of nanometre - scale particles including polymer and metallic colloidal particles, DNA and other macromolecules, viruses and also potential nanocomponents including carbon nanotubes, semi conducting nanowires and carbon-60 molecules. Consider a dielectric particle suspended in a spatially non-uniform electric field. The applied field induces a dipole in the particle; the interaction of the induced charges either side of the body with the electric field generates forces in opposite directions. Due to the presence of a field gradient, these forces are not equal and there is a net movement. If the particle is more polarisable than the medium around it, the dipole aligns with the field and the force acts up the field gradient towards the region of highest electric field. If the particle is less polarisable than the medium, the dipole aligns against the field and the particle is repelled from regions of high electric field. The magnitude and direction of the force is dependent on the induced dipole and is unaffected by the direction of the electric field, responding only to the field gradient. Since the alignment of the field is irrelevant, this force can also be generated in AC fields which has the advantage of reducing any electrophoretic force (due to any net particle charge) to zero.

Optical Computing Optics, which is the science of light, is already used in computing, most often in the fibre-optic glass cables that currently transmit data down Internet lines much more quickly than traditional copper wires. In an optical computer, electrons are replaced by photons, the sub-atomic bits of electromagnetic radiation that make up light.

Advantages of Optical Computing:  Low-loss transmission

 Large bandwidth  Compact and light weight  Inexpensive

Current use of Optics for Computing A group at Brown University and the IBM Almaden Research Center (San Jose, CA) have used ultrafast laser pulses to build ultrafast data-storage devices and able to achieve ultrafast switching down to 100ps. NEC (Tokyo, Japan) has developed a method for interconnecting circuit boards optically using Vertical Cavity Surface Emitting Laser arrays (VCSEL). Optical data processing can be done much easier and less expensive in parallel than can be done in electronics using a simple optical design, an array of pixels can be transferred simultaneously in parallel from one point to another. Parallelism, therefore, when associated with fast switching speeds, would result in staggering computational speeds.  Since photons are uncharged and do not interact with one another as readily as electrons, light beams may pass through one another in full-duplex operation.  Signals in adjacent fibers or in optical integrated channels do not affect one another nor do they pick up noise due to loops. Finally, optical materials possess superior storage density and accessibility over magnetic materials.

Ultrafast Pulse Shaping and Tb/sec Data Speeds Generating ultra short laser pulses in the picosecond and femto second range by sending it through a modulator is known as ultrafast pulse shaping. If the optical pulse that we wish to shape has a temporal duration of fs or ps, then we will need a modulator that works on this time scale. The idea of shaping a pulse by sending it through a modulator, such as a Mach-Zehnder, is referred to as direct pulse shaping. Current modulators can operate at 60GHz, which is much slower than necessary to shape a femtosecond pulse. Therefore, the technique of indirect pulse shaping, which includes Liquid Crystal Modulators (LCM pulse shaping), Acousto-Optic Modulator (AOM pulse shaping) and time-stretched pulse shaping is used. The choice of which

pulse-shaping apparatus to use may depend on the particular application; each technique has different advantages to it.

A grating spreads the pulse, so that each different spectral component maps onto a different spatial position. The collimating lenses and grating pair are set up in a 4F configuration (F being the focal length of the collimating lenses), and in the center of the 4-F system, an element is placed that will modulate the spectrum. In case of the AOM as the encoding element, there is a huge difference between speed of sound and speed of light in AOM crystal. Since the ratio between two is about 1 to 1 million, we can use MHz electrical signal to achieve THz programmable modulation of an optical signal and still keep a reasonable update speed. In practice, high resolution spectral encoding is, by definition, a variation of the Dense Wavelength Division Multiplexing (DWDM) and can be used to significantly improve the bandwidth efficiency. The idea can be illustrated in the following way: If we start with a 100fs Full-Width at Half Maximum (FWHM) optical pulse and encode, for example, 16 amplitude on-off-keying return-to zero (RZ) format bits in its spectrum, which in the worst possible cases would broaden the pulse by a factor of 16-to about 1.6 ps FWHM. The encoded pulses can, therefore be well confined in a 4ps optical switching window, without much distortion to the encoded spectrum. By doing this, the Time Division Multiplexing (TDM) system can benefit from spectrum encoding by a factor of 16 and achievable Data Translation Rate (DTR) can be as high as 4 Tbps.

Need for Nanotechnology: Computer process combines several million transistors to form logic gates, adders, memory in a highly ordered and complex fashion.

Such devices are fabricated using planar technology whereby masks are used to define areas of  Electronic doping (the addition of electron donor or acceptor atoms),  Metallisation (the addition of conducting metal wires), or  Etching (the removal of unwanted material by chemical means). The mask process is only applicable for the definition of devices half the size of the wavelength of light used to expose the material through the mask. Since it is not possible to focus high-frequency (short wavelength) energy with sufficient precision, the limit of conventional fabrication (presently about 100nm) is fast approaching; Nanotechnology allows the placement of small structures such as nanowires or DNA molecules to be placed with precision, simplicity and low cost and which allows the process of fabrication to be either completely automated or at least semi-automated.

Application: Nano Scale Architectures and Quantum Computing Techniques for Image Feature Extraction Quantum Computation Quantum computation, unlike classical logical devices, which only exist, in two states (0/1), uses atoms that can have three states (0/1/01). Thus a superposition of the first two states exists in quantum computation. The use of quantum computation is very

much useful in investigating properties of complex systems since quantum registers allow all possible numbers to be stored in a given moment of time using quantum superposition. The property that atom can also be prepared in a coherent superposition of the two states is exploited in quantum computation, and the use of quantum registers increases the storage capacity exponentially.

Image Feature Extraction Using Quantum Registers In Image Feature Extraction, a large database is needed and processing using conventional logic takes large amount of time. The use of nano scale architectures employing quantum registers can drastically reduce the computation time without a complex algorithm. By using an L bit quantum register a total of 2 L numbers can be stored at once. Thus by using a quantum register of size 16 bits a complete image feature of size 256 x 256 can be stored at once.

Classical Image Feature Extraction Algorithm The classical image feature extraction algorithm uses nano scale architectures with quantum registers, etc., but the algorithm that they run will not involve quantum mechanics. One such algorithm is given in this paper, where quantum structures are used for data storage tasks but non-quantum mechanics algorithms are used.

Merging Existing Non-Quantum Computing Tools and Nano Scale Architectures Embedded systems designed for image feature extraction should reply back within a bounded time. The algorithms that govern the Feature extraction should  Minimize the time of processing,  Minimize the computations necessary to predict/extract a feature with high degree of accuracy i.e should be robust  Handle large volume of data. The first and third factor can be achieved easily by using nano scale architectures for data storage tasks. The second factor is achieved by using existing Computing tools such as

Genetic Algorithms or Neural Network which are implicitly robust and which can predict results with good accuracy even in ill-defined problems. The proper combination of existing computing structure for robustness and nano scale architectures for minimizing time will become inevitable in any embedded system developed for image processing applications such as feature extraction in future. Nano scale architectures are less robust due to the reason that quantum nano computers store data in the form of atomic quantum states or spin and instantaneous electron energy states are difficult to predict and even more difficult to control. A sophisticated and subtle programming of the nano machines is required and hence proper computing paradigms are needed in order to help us gain the benefits of nano scale structures. This can be done easily since both robust computing techniques and nano scale architectures are parallel tasks and one can assist the other.

Classical Quantum Structure Based Image Feature Extraction Algorithm Initialize: Store Total Features characterizing the image in a particular terrain in Quantum registers. File Declaration: For (Feature = 1; Feature < = M; Feature++) For (i = 1, i< = SAM ; i++) Read feature [i] from quantum registers Relational Matrix Declaration: Store relational matrix upto higher order in quantum registers

For (i =1; i<M;i++) For (j=1; j<M;j++) Read a[i] [i] from ith relation matrix table Call Decision Loop: { Use relation matrix for Image feature prediction and Robust computing subroutine } Robust computing Subroutine: Function Robust (k) Set Rob [input] = kth sample If (Rob[output] = 1) Set flag = 1 Else Flag = 0 Return flag End Function.

In optical fibre communication systems: Nanotechnology has played a vital role in dramatically advancing optical fibre communication systems using bulk gallium arsenide lasers and multimode fibre with transmission distances of a few kilometers and bit rates of a few megabits per second. Now the systems have single mode fibre with bit rates a thousand times greater and distance is no object. This is possible by two developments in nanotechnology  The development of the multiquantum well laser based on indium phosphide technology which operates in single longitudinal mode and has good thermal characteristics  The discovery of erbium doped fibre amplifier and the use of nanoscale fibre gratings to provide uniform amplification over a substantial fraction of the low loss fibre window

Competitive models in nanocomputing:

Unlike the hofield energy function approach that requires the researchers to define the constraints of the problem and then go through an imprecise and obscuring energy function to define the weights, these nano-networks have properties that can be directly defined and controlled.

Basic idea of the network: The network used here was a neuron-matrix type neural network. Here the neurons are arranged in the form of a matrix and each layer of the matrix were linked with switching mosfet which was switched by the status of the linking registers. The linking register was an array of flip-flops which can be selected and can be made set or reset. When it was set the switching mosfet connected to it will be ON and the node connected to it will be connected to the next node adjacent to it.

Neuron hardwired circuit:  It will select any of the weights through shift-register which run through the length and breadth of the neuron matrix. Now it will compare the existing output and ideal one which will also be given in the learning mode. If error occurs it generates a perturbation signal. This happens when the clock is high.



When the clock goes low, it will activate the compare and correction block of the learning circuit and it will compare the previous and current error and it will give a correction signal. This will activate the correction block in order to make the correction.

 Thus it will now be switched to the next weight by incrementing the shift register thus scanning the entire set of weight and correcting in the desired manner to obtain convergence.

Conclusion Nanotechnology, being an emerging field today, plays very vital role in each and every field.

This area includes amazing applications in

Medical, Bio-computing, Quantum Computing, Industrial and Consumer applications. This also shows its usefulness in Tissue Engineering and acts as a solution for medical problems. Nanotechnology has wide applications in the field of industries so that size of machines and efficiency can be greatly reduced. Thus through this nanotechnology in various fields we gain a lot and it is the friend of the whole world.

Reference: (i)

Nanotech (1998,acebooks) by Jack Dann&Gardner Dososis.

(ii)

Transition to Tomorrow: Society at the cusp of nanotehnology(1993)by Jamie Dinkelacker.

(iii)

Prospects in Nanotechnology: Toward Molecular Manufacturing(1995) by Markus Krummenacker and James Lewis.