‘Intermittency, quasiperiodicity and chaos' in thieving of a ferroelectric transistor

ARTICLES PUBLISHED ONLINE: 17 NOVEMBER 2013 | DOI: 10.1038/NPHYS2796

Intermittency, quasiperiodicity and chaos in probe-induced ferroelectric domain switching A. V. Ievlev1,2 , S. Jesse2 , A. N. Morozovska3 , E. Strelcov2 , E. A. Eliseev4 , Y. V. Pershin5 , A. Kumar2,6 , V. Ya. Shur1 * and S. V. Kalinin2 * Memristive materials and devices, which enable information storage and processing on one and the same physical platform, offer an alternative to conventional von Neumann computation architectures. Their continuous spectra of states with intricate field-history dependence give rise to complex dynamics, the spatial aspect of which has not been studied in detail yet. Here, we demonstrate that ferroelectric domain switching induced by a scanning probe microscopy tip exhibits rich pattern dynamics, including intermittency, quasiperiodicity and chaos. These effects are due to the interplay between tip-induced polarization switching and screening charge dynamics, and can be mapped onto the logistic map. Our findings may have implications for ferroelectric storage, nanostructure fabrication and transistor-less logic.

C

omplex time dynamics including periodic, quasiperiodic and chaotic behaviours1,2 have long been recognized as a hallmark of strongly non-equilibrium systems ranging from convection and climate to reactions on catalytic surfaces3–5 . The chaotic time behaviour necessitates the existence of fractal strange attractors in the phase space of the system, establishing an intrinsic relationship between dynamics and spatial complexity. Significant efforts have been devoted towards spatial visualization of these behaviours, as well as the development of control algorithms through macroscopically measurable variables to evoke or suppress these behaviours6,7 .

At the same time, non-equilibrium systems often exhibit spatial complexity, including the formation of intricate periodic and fractal-like patterns in diffusion-limited aggregation, reaction and solidification fronts, percolation systems3,4 , and even one-dimensional (1D) droplet or bubble jets8 . Despite multiple examples of spatiotemporal complexity in physics, biology and chemistry, only limited information is available on controlling these behaviours spatially, precluding their practical use. Furthermore, these behaviours were assumed to be irrelevant to systems with binary physical states, such as computer memories and data storage. Here we demonstrate that polarization switching in ferroelectric materials under the action of a biased scanning probe microscopy (SPM) tip can give rise to complex spatiotemporal dynamics, including intermittency, quasiperiodicity and chaos.

This behaviour is ascribed to the balancing of polarization switching and screening charge-mediated depolarization fields, and opens exciting opportunities for the physical implementation of systems such as cellular automata.

As a model system, we have chosen the ferroelectric LiNbO3 surface, shown in Fig. 1. Application of a bias to the SPM tip in contact with the surface leads to local polarization reversal below the tip, thereby resulting in the formation of a switched domain. This switched domain can subsequently be detected in

the piezoresponse force microscopy mode, visualizing domains on the basis of the magnitude and direction of the piezoelectric response9,10 . Here, a set of bias pulses was applied, resulting in a clearly visible chain of domains of equal size. Note that domains are formed at each spatial point of bias application, and that the domain size is uniform, indicative of the high quality of the material. This behaviour was explored extensively in the context of ferroelectric data storage11,12 and fundamental studies of domain formation and stability13,14 . Generally, domain growth follows kinetic laws with domain size linearly proportional to the tip bias (above a certain threshold corresponding to domain nucleation) and approximately logarithmic with the pulse time11 , and is traditionally attributed to domain wall pinning in the disordered medium13 .

However, unusual effects are observed once the spacing between the points of bias application is varied, as illustrated in Fig. 2. On decreasing the separation between the bias application locations, the domain sizes become non-equal, often with pronounced transient behaviour between the first and subsequent domains in the chain.

For certain parameters, we observe intermittency, namely only every second domain is formed or domains of non-equal size with clear double periodicity are observed. On further decreasing the spacing, period tripling or the formation of non-periodic or longperiodic structures is observed. This behaviour is highly unusual for domain switching, and is clearly reminiscent of the period doubling and chaotic dynamics present in dynamic systems8 , where the number of locations at which bias is applied along the surface plays the role of discrete time and the domain radius is the system state.

We explore the variation of point spacing and tip bias (for a fixed bias pulse duration of 250 ms) as shown in Fig. 2a–i to construct the diagram shown in Fig. 2j. Application of biases below the nucleation threshold does not lead to domain formation (region I). Large biases result in continuous domain switching (domain size exceeds spacing between domains) and the formation of a ‘stripe’ domain (region II). Application of moderate biases for large separations

1 Ferroelectric

Laboratory, Institute of Natural Sciences, Ural Federal University, 620000 Ekaterinburg, Russia, 2 The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37922, USA, 3 Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauki, 03028 Kiev, Ukraine, 4 Institute for Problems of Materials Science, National Academy of Sciences of Ukraine, 3, Krjijanovskogo, 03142 Kiev, Ukraine, 5 Department of Physics and Astronomy, and the University of South Carolina Nanocenter, University of South Carolina, Columbia, South Carolina 29208, USA, 6 Centre for Nanostructured Media, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, UK. *e-mail: shur@labfer.ru; sergei2@ornl.gov NATURE PHYSICS | ADVANCE ONLINE PUBLICATION | www.nature.com/naturephysics

© 2013 Macmillan Publishers Limited. All rights reserved.

1

ARTICLES

NATURE PHYSICS DOI: 10.1038/NPHYS2796

a

b h=1

0.8

0.4

0.0

1

2 δ

3

U (V)

c

0 14

20

40

60

80

10

3

No switching

δ 6

0.4

0.0

1

2 δ

3

Uniform chain (δ = 2.0)

Period doubling ( = 1.35)

2 Chaos (δ = 1.2)

4 2 0 0

0.8

4≤

12

8

d

100

h = 0.3

1.2

Realizations of xn

Realizations of xn

1.2

1

2

Chaos

1

Merging

0

3

4

Chaos (δ = 1.1)

5

u

Figure 5 | Numerical solutions for various conditions. a,b, Bifurcation maps obtained by direct numerical solution of equation (3) for parameter h = 0.3 (a) and 1 (b), and bias u = 1.15. c, Phase diagram of switching behaviour as a function of dimensionless voltage u = (U/Ecr R0 ) and domain spacing δ = d/R0 calculated for h = 1. The colour bar indicates the number of bifurcation solutions. Note the qualitative similarity between the experimental diagram in Fig. 2j and the theoretical diagram (c). Also shown are axes obtained by rescaling to experimentally measurable parameters for R0 = 30 nm and Ecr R0 = 20 V. Using bulk materials parameters PS = 0.75 C m−2 , ε33 = 30 and ε11 = 84, the critical field for wall motion can be estimated as Ecr = 0.67 V nm−1 and the effective width of the water layer is H = 60 nm. d, Several realizations of domain sequences obtained by numerical solution of equation (3). The size of the red circles corresponds to the domain size. The parameters are chosen as shown in b. Note the agreement between calculated patterns and those observed experimentally in Fig. 2.

charge that suppresses nucleation in the vicinity of domains can be expected to be correct, the observation of chaotic behaviour requires the function xn+1 = q(xn ) to be non-monotonic; that is, interaction between domains at short ranges should be repulsive but at long ranges attractive. This behaviour is visible in Fig. 4d,e.

To explore this behaviour directly, Fig. 6 shows the experimental data of xn+1 = q(xn ). For large domains (or small separations) the pre-existent domains clearly suppress the formation of domains at n + 1 locations. For small rn (large separations or small domain sizes), there is a small upward trend, clearly visible on the regularized curve.

We further note that the consequence of an increasing value, dq/dxn > 0 for xn ≥ 0, would be that the presence of a domain facilitates nucleation at large separations. To establish this effect, we perform writing with high biases as illustrated in Fig. 6b,c.

Application of a bias in primary locations along the chain induces the formation of secondary domains where no bias is applied. This suggests that at large distances nucleation is indeed increased, in agreement with the upward trend of the curve in Fig. 6a for small xn .

To summarize, we report the surprising observations of intermittency, quasiperiodicity and chaotic dynamics during SPM tip-induced polarization switching on ferroelectric surfaces. These experiments unambiguously indicate that polarization switching 6

involves a coupling between switching and electrochemical processes, and the electrochemical step can be a rate-limiting element and also result in long-range interactions.

From the point of view of potential applications, the domain interaction effect demonstrated in this paper is very interesting in connection to the emergent computing paradigm known as memcomputing36 . Here, massively parallel dynamics of networks of memristors or memcapacitors37 could allow a significant improvement in information processing capabilities, provide a solution to the von Neumann bottleneck problem, and result in future brain-like computing architectures36 . Clearly, the main requirement for memcomputing—the ability to store and process information on the same physical platform—is met in our system.

The domain radius is a natural quantity to encode binary or analogue information depending on a specific task. In fact, the experimental results presented in Fig. 2 already demonstrate the possibility of binary logic operations. For this purpose, it is convenient to use the period doubling regime (Fig. 2f), when the domains of small (logic 0) and large (logic 1) radii alternate. In terms of logic operations, the domain alternation is the NOT gate. By using the interaction of three domains, the logic NOR (functionally complete) and AND gates can be realized (see Supplementary Information for more details). This shows that our system in principle possesses a universal computing ability.

NATURE PHYSICS | ADVANCE ONLINE PUBLICATION | www.nature.com/naturephysics © 2013 Macmillan Publishers Limited. All rights reserved.

International Scholarly Research Network ISRN Nanotechnology Volume 2012, Article ID 102783, 9 pages doi:10.5402/2012/102783

Review Article A CNTFET-Based Nanowired Induction Two-Way Transducers Rostyslav Sklyar Verchratskogo st. 15-1, Lviv 79010, Ukraine Correspondence should be addressed to Rostyslav Sklyar, sklyar@tsp.lviv.ua Received 15 December 2011; Accepted 28 February 2012 Academic Editors: C. A. Charitidis and J. Sha Copyright Š 2012 Rostyslav Sklyar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A complex of the induction magnetic field two-way nanotransducers of the different physical values for both the external and implantable interfaces in a wide range of arrays are summarized. Implementation of the nanowires allows reliable transducing of the biosignals’ partials and bringing of carbon nanotubes into circuits leading to examination of the superconducting transition.

Novel sensors are based on the induction magnetic field principle, which causes their interaction with an ambient EM field.

Mathematical description of both the signal and mediums defines space embracing of the relevant interfacing devices. As a result, a wide range of the nano-bio-transducers allow both delivering the variety of ionized biosignals and interface the bioEM signals with further stages of electronic systems. The space coverage and transducing values properties of the state-of-the-art magnetic interfaces are summarized, and directions for their future development are deduced.

1. Introduction: Biophysical Signals, Transducing, and Interface Applications A biosensor is a device that incorporates a biologically active layer as the recognition element and converts the physical parameters of the biological interaction into a measurable analytical signal [1]. Understanding of biosignals’ (BS) nature and properties of their mediums are a basis for effective design of magnetic interfaces (MIs). Rapid progress in the advancement of several key science areas including nanoscale interfaces has stimulated the development of electronic sensor technologies applicable to many diverse areas of human activity. For example, the conceptualization and production of electronic nose devices have resulted in the creation of a remarkable new sector of sensor technology resulting from the invention of numerous new types of olfactory-competent electronic sensors and sensor arrays [2].

The growing variety of biosensors can be grouped into two categories: implantable and external. In turn, the last one has two existing paradigms: wearable sensor and noncontact sensor. A wearable sensor had potential to be intrusive, and noncontact sensor methods may still be intrusiveness to a certain extent, while a noncontact sensor is limited in its capability of acquiring physiological signals [3]. Voltage potentials of the living organism and its organs are measured

by both implantable and external electric field probes of high sensitivity [4]. Information on organ activity is obtained by measuring biomagnetic signals. For such purposes a multichannel high-temperature superconducting quantum interference device (high Tc SQUID) system for magnetocardiography (MCG) and magnetoencephalography (MEG) of humans, with high magnetic field resolution, has been developed [5, 6].

The most current sensing devices give us the possibility to receive a full scale of both the internal and external control BS. The internal ones are picking up by polymeric microprobes, CMOS chips, and nanoneedles, while the external by electromyography and neuroprosthetic (electroencephalogram (EEG) and MEG) systems. Improving an informational capability of the interface is implemented by the application of the advanced superconducting transducer and electromagnetic (EM) transistor/memristor [7, 8]. These elements are arranged into the arrays of a different configurations and can cover the order of spaces from macro- to nanolevels.

There are a number of methods and devices for transducing different BS into recordable or measurable information.

The transfer of nerve impulses (NI) is the main data flow that carries sensory information to the brain and control signals from it and from the spinal cord to the limbs.

ISRN Nanotechnology

5

Brain, spin. cord Tensor Vector Coil

Space dimens.

AND

Output

OR

Interface

Input

Array

Membrane

Scalar Plane

Channel

Curve Line Processor

Figure 5: Interaction of the natural and artificial processing bodies trough a SuFETTr-based interface.

voltage, or a concentration of organic or chemical substances (Figure 1). Moreover, this process can be executed in reverse.

Substances and/or voltages influence BS, thereby controlling or creating the said media (Figure 2).

As a result, we have achieved SuFETTr that is suitable for ascertaining the variety of values. Two directions of SuFETTr function enable decoding of the NI by comparing the result of its reaction on some process or organ with an action on them of the simulated electrical or biochemical signal after their reverse transducing through the SuFETTr(s) [44].

3.2. Arrangement of PSs in Arrays. An MF sensing array consists of primary sensors and, optionally, reference sensors.

Primary sensors use flux transformers located in a close proximity to the scalp or chest surface, where they couple to the brain’s or heart’s MFs, respectively [7, 45]. The reference sensors are used to subtract the environmental noise from the primary sensor outputs. The flux transformer design dictates its relative sensitivity to near and distant sources. Thus, the primary flux transformers can, in addition to detecting the brain signals, also provide various degrees of the environmental noise rejection. Flux transformers (magnetometers) have the highest sensitivity to both near- and far-field sources. Thus, they do not reduce the environmental noise (and must rely solely on the references or other techniques for the noise cancellation) [16].

Wire-wound gradiometers are the most conventional and are commercially available for EM systems. The PCs of wire-wound gradiometers are wound in opposition and balanced so that a uniform field links the zero net flux [37]. Conventional gradiometers, such as wire-wound, thin-film, or electronic gradiometers, are axial or planar, that is, “one dimensional” that detect the gradient of a MF in one direction. These one-dimensional gradiometers effectively reduce the ambient MF as their order increases.

However, they also reduce the biomagnetic signal. A “twodimensional” gradiometer detects the gradient of an MF in

Point Ions

Ferro Induct.

Phys. value

Dynam.

Figure 6: Interfacing ability (power) of the specific input elements.

It depends on two groups of space characteristics (form and direction) for the available transducing media.

two orthogonal directions to achieve high SNR. It focuses on a two-dimensional gradiometer that detects both the axialsecond-order and planar-first-order gradients of an MF. Figure 3 shows a PC for the two-dimensional gradiometer.

The discrete configuration space is a graph. Each node in the graph corresponds to an intermediate, and its neighbors are intermediates to which it can be folded or unfolded [38]. A common feature shared by folding of hydrophobicpolar chains on a lattice and self-folding a net through vertex connections is that in both cases the process of folding is driven by the formation of secondary links between topological neighbors (Figure 4). The best 2D arrangements, called planar nets, to create self-folding polyhedra with dimensions of a few hundred microns are determined, and optimal configurations for creating 3D geometric shapes have been found.

The importance of being able to address nanoscale elements in arrays goes beyond the area of nanocomputing and will be critical to the realization of other integrated nanosystems such as chemical/biological sensors. A regular crossed-NW FET array that consists of n-input Iin and moutput Uout NWs, in which outputs are the active channels of FETs and the inputs function as gate electrodes that turn these output lines on and off [46, 47].

3.3. Multisensor Data Fusion from Arrays. A further step should be synthesis of the said two methods in order to develop the external (nonimplantable) MCG&MEG signalsto-processor connection. The EM sensors are surface PCs, which are used in regular configuration where PCs with a small distance between each other are distributed under the heart or brain surface to pick up the local signals within the place of interest. The problem of sensing the EM signal for amplification/switching/memory with a speed of light in a

6

ISRN Nanotechnology Table 1: Dependence of the received BS parameters on the mode of SuFETTr’s functioning.

Mode

Medium

Serial External

NI Molecules DNA

!

!

ibio = 1cont. or sens. imp.

BSs → bio and chem. molec.

propagation of BS along DNA’s spirals

Parallel Implantable External ibio = ibio ( f1 ) + ibio ( f2 ) + dibio /dt, dibio /dx · · · + ibio ( fN ) " variation of BSs → BSs = 1 type of molec. concentr. of molec. decoding the BSs of space and length dynamic nucleoted recognition on both spirals

Implantable

"

"

ibio = 1 network or 1 fibre

BSs =

"

bio and chem. molec.

4 nucleoteds 4 outputs

Table 2: Measuring effects (values) and the relative nanotransducers (for interfacing).

single (passive) solid-state device EM transistor/memristor (EMTM) has been advanced [48] An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial transducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in relation to the nervous system, is performed. Because the sensing organs are exponentially better than any of analogous artificial ones, the advances in nanotechnology are opening the way to achieving direct electrical contact of nanoelectronic structures with electrically and electrochemically active neurocellular structures. The transmission of the sensors’ signals to a processing unit has been maintaining by an EM transistor/memristor (externally) and superconducting transducer of ionic currents (implantable). The arrays of the advanced sensors give us information about the space and direction dynamics of the signals’ spreading.

Recent developments in bioengineering, nanotechnology, and soft computing make it possible to create a new generation of intelligent sensing. There are developing opportunities for combining natural and artificial sensing abilities in the synthesized system. Backed up by the rapid strides of nanotechnology, nanosensor research is making a two-directional progress, firstly in evolving new sensors employing mesoscopic phenomena, and secondly in the performance enhancement of existing sensors. Nanosensors are nanotechnology-enabled sensors characterized by one of the following attributes: either the size of the sensor or its sensitivity is in the nanoscale, or the spatial interaction distance between the sensor and the object is in nanometers.

These nanosensors have been broadly classified into physical and chemical categories, with the biosensors placed on borderlines of biological signals with the remaining classes [49].

Multiprocessor data fusion is in effect intrinsically performed by animals and human beings to achieve a more

NW element PC(s) NIext EMTM induction transducer volume flowmeter

Superconducting membrane NIimpl acoustical EMTM noise absorb. universal flowmeter

A functional pattern of SuFETTr Informational flow Biological

EM field

Technical

el. current Electronic

Ionic el. voltage

Actuator

Ionic currents FerroEM Magnetic induction Magnetodynam.

SuFET channel NIimpl EMTM NIcontr gaseous flowmeter

Sensor

Physical value

Empirical data Biological

Technical Informational flow

Figure 7

accurate assessment of the processing environment. The aim of signal processing by the combined artificial-living being multiprocessor system is to acquire complete information, such as a decision or the measurement of quantity, using a selected set of input data streaming to a multiprocessor system-digital data are coming to artificial processor and the rest of information consumes by a neural system of living being (Figure 5). Thereby, a big amount of available information is managed using sophisticated data processing for the achievement of a high level of precision and reliability.

4. Results Application variety of the novel superconducting, organic, and CNTFETs allows us to design transducers of BS (nerve, biochemical, etc.) that transduce them into different quantities, including electric voltage, density of chemical and biomolecules. On the other hand, the said BS can

CNT and Organic FETs Based Two-Way Transducing of the Neurosignals R. Sklyar Verchratskogo st. 15-1, Lviv 79010 Ukraine, sklyar@tsp.lviv.ua ABSTRACT A SuFET based neurotransducer (sensor) with carbon nanotubes (CNT) or PC kind of input circuit for the nerve and neuron impulse has been designed. A nanoSuFET with a high-temperature superconducting channel is introduced into the nerve fibre or brain tissue for transducing their signals in both directions. Pickup coils are implanted into an organism in order to obtain the natural neurosignals from the organs and tissues, also the artificially excited signals to them. On the bases of depicted transducer a combined processor for the natural and artificial information is advanced.

Keywords: processor

neurosignals,

1

SuFET,

interface,

textile,

INTRODUCTION

The recent achievements in nanoelectronics can be regarded as a further step in the progress of NS transduction. They give us the possibility to create the most advanced and universal device on the basis of known micro systems. Such a sensor/transducer is suitable for picking up neurosignals (NSs)- nerve and neuronic impulses (Fig. 1)and transforming it into recognizable information in the form of electric voltage, or a concentration of organic or chemical substances [1]. Moreover, this process can be executed in reverse. Substances and/or voltages influence NSs, thereby controling or creating them (NSs).

structures with electrically and electrochemically active subcellular structures- including ion channels, and receptors. Interfacing of nerve cells and field-effect transistors (FETs) is determined by current flow along the electrical resistance of the cell-chip junction. A spectral power density of the junction is 5·10-14 V2/Hz and can be interpreted as Nyquist noise of the cell-chip junction with a resistance of 3 MOhm by measuring the fluctuations of extracellular voltage with a low-noise transistor [2]. The thermal noise allows us to elucidate the properties of cell adhesion and it sets a thermodynamical limit for the signalto-noise ratio of neuroelectronic interfacing.

2

DESIGN OF THE TRANSDUCER

Proceeding from the previously mentioned difficulties, including superconducting element of the sensor/transducer into an electric current could be the solution to the problem [3]. Electronic or ionic currents in conductors or axons respectively, passing through the superconducting FET’s (SuFET’s) channel induce the output voltage on its gate (Fig. 2). The method of combining the bioelectric nature of NSs with body-temperature pickup coil (PC) and reverse input of the SuFET device in order to obtain most advantageous biosensor/transducer was recently advanced [4] (Fig. 3). The SuFET is used as a zero-resistance ammeter which converts drain currents into gate voltages.

U out

í Fig. 2

Fig. 1 Long itudinal sect ion of an axon showing a few lines of current flow

The advances in nanotechnology are opening the way to achieving direct electrical contact of nanoelectronic

An organic Su FET device and its electrodes

Among the variety of the organic FET devices [1] there are majority of them, mainly modifications of nanoFETs, which allow simultaneous processing of a number of NSs directly or from the PC. There are two factors that make simultaneous processing possible. First of all, the sizes of nanoFETs and nanoPCs are in the same order as the transmitting substances of NSs, such as axons and neurons.

Secondly, the crossed-nanowire FET or textile (Fig. 4)

in the chain in order to transduce the NSs into different physical and chemical quantities and vice versa [5].

arrays are, in itself, multiinput. The remaining part of FET devices are applicable for serial connection to the said mediums. In addition, some of these FETs can be arranged

A

myelin c hannel ‘s

nerve NI impulse

i bio

Vg Vout

ionic currents

SuFET SFET axons

PC

B

°

gate

°

i bio

R

°

E transmit0

receiving C unit

~

ting unit

in vivo

Draine

visualization and memory units

C0

L

SuFET

H

° °

° Gate

Vo u t

Source

(Bio)Telemetr y

following external units

Cryostat

Shie ld

Fig. 1. An SFET based biotransducer and its signal behavior

Fig 3. A neurotransducer as the inclusion of a SuFET device into the nerve fibre: A- implantation of the whole high-Tc SuFET variant; B and C- tapping of NSs i bio on the external SuFET by implantable wire contacts.

a)

b)

Fig. 4 Technological solutions for textile electronics: a) a ribbon; b) a cylindrical yarn.

3

COMBINING OF THE NATURAL AND ARTIFICIAL PROCESSING ABILITIES

Multiprocessor data fusion is in effect intrinsically performed by animals and human beings to achieve a more accurate assessment of the processing environment. The aim of signal processing by the combined artificial-living being multiprocessor system is to acquire complete information, such as a decision or the measurement of quantity, using a selected set of input data stemming to a multiprocessor system- digital data are coming to artificial processor and the rest of information consumes by a neural system of living being. Thereby, a big amount of available information is managed using sophisticated data processing

for the achievement of a high level of precision and reliability.

3.1

Arrangement of the Combined System

It is possible to substitute the microcomputer in an object-oriented problem solution scheme by the natural processing organ- brain or spinal cord. As a result, the software component will be eliminated and the most general characterization of the processing problem in onecoordinate dimensional calculations could be acquired naturally, according to the feedback reaction on the input exposure for calibration, error correction, scaling up or down, range extension, sampling, resolution, etc (see Appendix).

Application variety of the novel superconducting, organic and CNT transducers allows us to design processors of the biosignals (nerve, neuronic, DNA, etc.) that transduce them into different quantities, including electric voltage, density of chemical and biomolecules. On the other hand, the said NSs can be controlled vice versa by the applied electrical signals, or bio and chemical mediums [4].

3.2 Application of a Solid-State Electromagnetic (Optical) Transistor The known ferromagnetic materials have a hysteresis loop. That is why their application do not possible because of ambiguity of characteristic. The device for controlling the magnitude of optical flux by the application of an electromagnetic (EM) field is based on a ferroelectric (FE) or ferroelectromagnetic (FEM) polarizer that allows the rotation of the polarizing plane according to a hysteresis loop [6]. Besides, the controlling possibility of the known ferromagnetic and FE devices is substantially restricted by an ambiguous part of a hysteresis. That is why it is possible only switching of an EM flux between two stages.

The invented transistor consists of FE or FEM processing crystal, source of an EM energy, and the analysing element (Fig. 5). Control of an EM flux or OB has been carried out by splitting them on two parts: a passed one through the processing body and reflected part by this body. An EF or MF is applied by the carbonic nanotubes (CNT) or polymer nanowires to the crystal which is rotating a polarization angle of EM flow or OB. The electrical transport between the contact metal and the nanotube occurs along the entire nanotube under the contact electrode. This suggests that the transfer length, which is defined as the distance required for current to flow into or out of the contact electrode is thought to be less than 50 nm [7]. This angle is rotated according to the linear part of a FE or FEM crystal’s characteristic. The amplified signal is analysed in the units of a polarization angle. The reflected part is used as polarization P2(φ) (Fig. 5) for creating the logical (optical and magnetic) elements “AND”, “OR”, and “NOT”. This device is switching or amplifying of EM flow and OB as by magnitude, as well as by an angle of polarization.

As a result, it will be possible to overcome the said disadvantages of the known devices, namely: 1) to raise the fast-action up to the maximal possible- the speed of spreading an electromagnetic (optical) wave; 2) to reduce the noise level in an amplified signal at the expense of using the material with a unique fashion amplifying (without a hysteresis) performance- without the internal mechanical transformations; 3) to improve an ease of manufacture by using of printing nanotechnologies and expulsion of an additional optical planes or mirrors.

Opt rev P2(φ)

N

+

Opt

trans

Ferro-el/magn P1(φ)

Opt in

_

S

Fig. 5 An Electromagnetic Transistor based on CNT Exploitation of the parallel input to multiprocessor allows determination of space and time dynamics of NSs in the nerve fibre and neurons and also the amplification of output signal Uout by multiplying the concentration of molecules according to a number of input NSs. After the implantation of parallel SuFET(s) or optical transistors, the averaging or summation of this dynamic among the whole flow network, nerve fibre or neurons is possible.

REFERENCES [1] R. Sklyar, “Superconducting Organic and CNT FETs as a Biochemical Transducer”, ISMCR 2004: 14th International Symposium on Measurement and Control in Robotics, NASA Johnson Space Center, Houston, Texas, IEEE (ISMCR), section 24, (13 pages), 2004.

[2] F. Patolsky, B. P. Timko, G. Yu et al., Detection, Stimulation, and Inhibition of Neuronal Signals with High-Density Nanowire Transistor Arrays, Science 313 (2006) 1100–1104.

[3] R.Sklyar, Superconducting Induction Magnetometer, IEEE Sensors Journal, April 2006, Vol. 6, Iss. 2, pages 357- 364.

[4] R. Sklyar, Sensors with a Bioelectronic Connection, IEEE Sensors Journal (Special Issue), May 2007, Vol. 7, Iss. 5, pp. 835-841.

[5] R. Sklyar, The Microfluidic Sensors of Liquids, Gases, and Tissues, Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS), 2007, No. 2, pp. 20-34.

[6] R. Sklyar, Patent UA #76691 "The control method of the electromagnetic flow intensity and amplifying elements on its bases”, Bull. 9, 2006.

[7] Yo. Nosho et al., Evidence of Edge Conduction at Nanotube/Metal Contact in Carbon Nanotube Devices, Jap. J. of Appl. Phys., vol. 46, 2007, pp.

L474–L476.

Appendix

A Combined Processor for the Natural and Artificial Information.

n a t u r a l

En = kT / gDS ⋅ γ noise

sensing signals

VGS =

jπ µ 0 S eq h ω T (Q G ) f eI 0 z c

Vout Knerv e= i

H

[Su(O)FETTr] -1

* Su(O)FETTr

voltage signal

AND

output

OR

interface

co mlex p ro blem

input

B ra in, spin. co rd

synthesized solutio n

Proc es s or analytical inform ation

U ncNo iWs e = n 4 k T / Q

[Su(O)FETTr] -1

Vg =

current signal

Su(O)FETTr

j h ω T (Q G ) ID 2 eI 0

K cNW =

V out V sup Q + i

a r t i f i c i a l *

Su(O)FETT r- supercond. (orga nic) field-effect transistor based transduc er