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Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks Y. Nishitani, Y. Kaneko, M. Ueda, T. Morie, and E. Fujii Citation: Journal of Applied Physics 111, 124108 (2012); doi: 10.1063/1.4729915 View online: http://dx.doi.org/10.1063/1.4729915 View Table of Contents: http://scitation.aip.org/content/aip/journal/jap/111/12?ver=pdfcov Published by the AIP Publishing

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JOURNAL OF APPLIED PHYSICS 111, 124108 (2012)

Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks Y. Nishitani,1 Y. Kaneko,1 M. Ueda,1 T. Morie,2 and E. Fujii1 1

Advanced Technology Research Laboratories, Panasonic Corporation, Seika, Kyoto 619-0237, Japan Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Wakamatsu-ku, Kitakyushu 808-0196, Japan 2

(Received 13 March 2012; accepted 19 May 2012; published online 22 June 2012) Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O3 (PZT)/SrRuO3. In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission C 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4729915] for learning. V I. INTRODUCTION

Artificial neural networks are inspired by biological systems such as the human brain. Unlike conventional von Neumann-type computers, these networks enable a new paradigm of adaptable, high-efficiency computing.1,2 Such a high efficiency of neural network is brought by its operating principle, where a large number of neurons connected via synapses each other can compute in parallel.3 Adaptability of neural networks is mainly brought by learning function.

Many learning rules have been proposed in which the learning function of brains is based on the plasticity of synaptic weights.4 For information processing in neural networks, inputs to a neural network are converted to output through signal propagation among neurons. The way how the signals are converted is determined by synaptic weights memorized by synapses, meaning that the intended input-output relationship can be obtained by adjusting each of synaptic weights.

In other words, synapses work as memory devices in the brain. However, the synaptic weights are not binary unlike the conventional digital memory, but analog in general, which enables neural networks to have high functionality.

Traditionally, the functions of neurons and synapses were reproduced by using software techniques on conventional computers. In this approach, true parallel computation could not be realized because only a few central processing units in conventional computers have to emulate the behavior of many neurons by time-sharing operation. On the other hand, if electronic devices can mimic the function of neurons, each of these devices can operate independently as occurs in biological systems. The resultant hardware-based 0021-8979/2012/111(12)/124108/6/$30.00

neural network composed of such devices thus enables highefficiency parallel processing. Accordingly, to take full advantage of the capability of a true artificial neural network, a hardware neural network must be achieved. Indeed, there have been lots of reports about neural networks using CMOS technology.5–7 However, it is difficult to implement the functions of synapses in CMOS-based circuits because the synaptic weights are non-volatile and adjustable, which is not common in the conventional CMOS devices. Since these features are important for the practical learning, in the conventional CMOS neural networks, weights are stored in separated memory devices, resulting in the slow access speed. Therefore new synapse devices with high controllability of memorized synaptic weights are needed.

Simpler learning rules are preferable when learning functions are implemented in synapse devices. In particular, it is desirable that synapses can adjust their weights by themselves without using central processing units to control synaptic weights for learning. One of the examples of such self-learning functions observed in the brains is spike-timing-dependent plasticity (STDP). In STDP, the change in the weight of a synapse is dominated by the timing difference between the spike signals from the neurons connected via the synapse.8–11 By utilizing STDP, a learning function can be implemented to neural networks using only a few kinds of spikes, which is desirable for making the network simple.

Figure 1(a) illustrates the concept of STDP. Here, the preneuron sends a spike to the post-neuron through a synapse at t ¼ tPRE, and the post-neuron sends a spike to other neurons at t ¼ tPOST. In STDP, the synaptic weight w changes as a function of the timing difference of the two spikes tD ¼ tPOST – tPRE.

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FIG. 2. Schematics of neurons (pre- and post-) connecting via (a) (b) a twoterminal synapse device, or (c) (d) an FeFET-based synapse device. Schematics of signal flow (a) (c) at signal processing and (b) (d) at learning process.

FIG. 1. Schematics of neurons (pre- and post-) connecting via a synapse.

Characteristics of (b) asymmetric and (c) symmetric STDP.

Figures 1(b) and 1(c) show the characteristics of asymmetric and symmetric STDP, respectively. The vertical axis shows the change of the synaptic weight Dw. In asymmetric STDP, w is increased (Dw > 0) when the pre-neuron outputs a spike before the post-neuron does (tD > 0). When the order of the spikes is reversed (tD < 0), w is decreased (Dw < 0). In symmetric STDP, Dw depends on only the absolute value of tD. These simple learning functions determined only by the spike timing are suitable for constructing huge artificial neural networks such as the one sought here.

In electronic synapse devices, currents are used as signals transmitting among neurons by assuming synaptic weights are regarded as electric conductance, considering that in the biological system the synaptic weight is expressed as how much neurotransmitters are emitted in response to the input stimulation on the neuron. Accordingly, the controllability of the electric conductance is required for the effective operation of synapse devices. For example, it was recently reported that resistive switching devices, composed of a metal-insulator-metal structure, can provide STDP function as an electronic synapse device.12–16 However, the learning process that uses the STDP function can be performed only when the signal transmission among neurons is aborted in an artificial neural network using such devices as synapses despite learning and signal transmission in brain systems are not performed separately in general. This constraint is due to the fact that the resistive switching device is a two-terminal component, in which one of the terminals is connected to the pre-neuron and the other is connected to the post-neuron. Figures 2(a) and 2(b) show schematics of preand post-neurons connected via such a two-terminal synapse.

The output signal from the post-neuron is fed back to the synapse device terminal during the learning as shown in Fig. 2(b), while the terminal outputs signals from the pre-neuron during the signal processing as shown in Fig. 2(a). Accordingly, the signal processing and learning cannot be performed simultaneously.

With a goal of attaining a new electronic synapse device capable of more flexible processing and learning, the research discussed here focuses on a ferroelectric-gate field-effect

transistor (FeFET). This transistor uses a ferroelectric material as a gate insulator for metal-insulator-semiconductor FET (MISFET). In principle, the channel conductance of an FeFET can be controlled by applying gate voltage through the change on the polarization of the ferroelectric layer in a non-volatile manner. Consequently, the variable conductance of the FeFET can be used as a synaptic weight as proposed in Ref. 17.

When FeFET-based synaptic devices are used in a hardwarebased neural network, neurons transmit signals thorough the FeFET channel, where the transistor’s drain and source electrodes are connected to the pre- and post-neurons, respectively. Figures 2(c) and 2(d) show schematics of pre- and post-neurons connected via an FeFET-based synapse. The gate electrode of the FeFET is used to control its channel conductance independently of the signal propagation through the channel, which is different from the resistive switching device (see Figs. 2(a) and 2(b)). Thus, concurrent learning is possible, in which the weight update can be performed without canceling the signal transmission among neurons by using an FeFET-based synapse device.

In this work, we report the learning function of an FeFET-based synapse device with an eye toward developing an artificial neural network with a concurrent learning function. An FeFET composed of a ZnO/Pb(Zr,Ti)O3 (PZT) stacked structure was fabricated and the multi-valued modulation of its non-volatile channel conductance was demonstrated using pulse gate voltage. Finally, the STDP function was implemented in an electronic synapse device composed of the FeFET. II. FERROELECTRIC-GATE FIELD EFFECT TRANSISTOR (FeFET)

Figure 3 shows schematics of the fabricated FeFET used in this study. The fabrication process is as follows. A 675 nm-thick PZT/30 nm-thick SrRuO3 (SRO) stacked structure was deposited on a single-crystal SrTiO3 (001) (STO) substrate by the pulsed-laser deposition (PLD) method using a KrF excimer laser source (k ¼ 248 nm). The substrate temperature was kept at 700 C during the deposition. After the substrate temperature was decreased to 400 C, a 30 nmthick ZnO film was deposited on the PZT/SRO/STO stacked substrate by PLD method. Then, the ZnO film was etched

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FIG. 5. Change of the drain current before (IDSinit) and after (IDSpulse) applying the pulsed gate voltage with various heights (VGSpulse) and widths (DGSpulse). The dotted line represents the maximum drain current IDSmax. IDS is measured with applying the drain-source voltage VDS ¼ 0.1 V. The inset shows the schematic illustration of the pulsed gate voltage.

and DGSpulse ¼ 1.0 ls with the source and drain electrodes grounded. The PZT film at the initialized state was assumed to have a multi-domain state as expected above.

This initialization sequence was the same for the all subsequent measurements (Figs. 8 and 9).

3. Following initialization, the pulse gate voltage with various VGSpulse and DGSpulse was applied with the source and drain electrodes grounded, and then IDS was measured as IDSpulse under VGS ¼ 0 V. IDSmax and IDSinit are plotted as dotted and broken lines in Fig. 5, respectively. As can been seen, the multi-valued modulation of the non-volatile channel conductance was demonstrated by the application of a pulse gate voltage. When VGSpulse was positive, IDS was increased (IDSpulse > IDSinit), which indicated that the additional part of the downward polarization was switched upward. On the other hand, when VGSpulse was negative, IDS was decreased (IDSpulse < IDSinit) indicating that the additional part of the upward polarization was switched to downward. The IDS change became enhanced as DGSpulse and/or the absolute value of VGSpulse was increased. Here, IDS became equal to IDSmax at VGSpulse > 4 V; this was caused by the fact that the PZT polarization was oriented fully upward by 10 ls VGSpulse. This polarization implied that the switching time ts was less than 10 ls at VGSpulse > 4 V. III. FeFET-BASED SYNAPSE DEVICE

As described in Sec. I, STDP is the change in the synaptic weight which is the function of the timing of VPRE and VPOST, where VPRE and VPOST correspond to the signals from the pre- and post-neuron, respectively (see Fig. 1). To develop an FeFET-based synapse device with the STDP function, conductance modulation by the pulse height (Fig. 5) was available. Here, we devised a synapse device composed of the FeFET and a switch. The schematic of the synapse device is shown in Fig. 6(a). The gate electrode of the FeFET is grounded or connected to the input terminal of VPRE. These two states are switched by the second input voltage

J. Appl. Phys. 111, 124108 (2012)

FIG. 6. (a) Circuit diagram of the synapse device composed of an FeFET and a multiplexer. (b) Shape of pre-pulse VPRE. (c) Schematics of the operation of the multiplexer with applied VPRE and VPOST.

VPOST. In this study, in order to verify the operation of this proposed device, a commercially available multiplexer was used as the switch. Specifically, the multiplexer had VPRE input to the gate as VGS when VPOST > 0.1 V (HI state), and VGS was grounded when VPOST < 0.1 V (LO state).

To obtain an asymmetric STDP function (see Fig. 1(b)), the bipolar saw-edged single pulse shown in Fig. 6(b) was used as a pulse shape of VPRE. The VPRE shape used in this study was temporally point symmetric, of which the maximum absolute height was |VPRE| ¼ 2.0 V and the maximum width was DPRE ¼ 40 ls (see Fig. 6(b)). By using this uniquely shaped pulse VPRE, the height of VGS, the output of the multiplexer, changed depending on the input timing of VPRE and VPOST. Figure 6(c) shows the conceptual diagram of the behavior of the proposed device operation. As an example, the case when VPOST is a square pulse with the pulse width DPOST is illustrated. The pulse timing difference tD is defined as the applied timing difference between VPRE and VPOST, shown as wide arrows in Fig. 6(c). In the case of |tD| < (DPRE þ DPOST)/2, VPRE and VPOST temporally overlapped one another. As VPRE is applied to the gate electrode only when VPOST is applied (VPOST is at HI state), part of VPRE overlapped with VPOST is output as VGS. At the same time, the sign of VGS is determined by the sign of tD; i.e., VGS is positive when tD > 0 and negative when tD < 0. The amplitude of VGS is increased as |tD| is decreased. In contrast, in case of |tD| > (DPRE þ DPOST)/2, VPRE is zero when the gate electrode is connected to the terminal of VPRE by the multiplexer; this makes VGS ¼ 0 V, as shown in the leftmost and rightmost sections of Fig. 6(c). As described above, the pulse timing difference was translated into pulse height by inputting VPRE and VPOST to the multiplexer. Such behavior of the multiplexer can be described as “sampling VPRE by VPOST.” Next, the sampling behavior of the multiplexer described in the previous paragraph was verified experimentally. VPRE and VPOST were generated by a function generator

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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

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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).

Appendix

A Combined Processor for the Natural and Artificial Information.

n a t u r a l

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sensing signals

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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 biosignalsâ&#x20AC;&#x2122; 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 biosignalsâ&#x20AC;&#x2122; (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.

2 Moreover, the complex view on BS requires further stages of precise processing in order to decode the received or control information. There are different kinds of transducers/sensors for picking up NI: room-temperature and superconducting, external, and implantable. Development of such devices is increasing the penetration into bioprocess while simultaneously simplifying the exploitation of the measuring systems in order to bring them closer to the wide range of applications. For this reason, the magnetometer with a room-temperature pickup coil (PC) for detecting signals, which can clearly be detected in higher frequency range, was developed in order to simplify the SQUID system. The PC is set outside the cryostat and is connected to the input coil of the SQUID [9] or a channel of superconducting field-effect transistor (SuFET) [10]. On the other hand, implantableinto-nerve fiber transducers are evolving from the ordinary Si-chip microelectronics devices [11] into superconducting and nanodevices [12, 13].

The recent achievements in nanoelectronics can be regarded as a further step in the progress of BS transduction.

They give us the possibility to create the most advanced and universal device on the basis of known microsystems.

Such a sensor/transducer is suitable for picking up BS— NI, electrically active (ionized) molecules, and the basepair recognition event in DNA sequences—and transforming it into recognizable information in the form of electric voltage, or a concentration of organic or chemical substances.

Moreover, this process can be executed in reverse. Substances and/or voltages influence BSs, thereby controling or creating them (BS) [14]. Steady and rapid progress in the robotics field requires ever quicker and better human-machine interaction and the development of a new generation of interfaces for intelligent systems. Such advances give rise to markedly increased biophysical research on the one hand and the need for new bioelectronic devices on the other. Transduction and measurement of BS are key elements of MIs design.

There are two means involved in signal transduction: (1) biochemical—by hormones and enzymes; (2) biophysical— by nerve impulses (ionic currents). Let us consider the biophysical ones as useful for the said interfaces design above.

There are two values—voltage and electric current—which characterize the pathway of transduction [15].

Calculations of PC arrays were performed with the primary sensor flux transformer sites distributed uniformly on a spherical sensor shell, extending from the vertex to a maximum angle max [16]. The radial magnetometers and gradiometers occupy one site each, there are two orthogonal planar gradiometers at each site and there are three orthogonal magnetometers at each site for vector magnetometers. Coverage can be achieved by designing some kind of density control mechanism, that is, scheduling the sensors to work alternatively to minimize the power wastage due to the overlap of active nodes’ sensing areas. The sensing area of a node is a disk of a given radius (sensing range). The sensing energy consumption is proportional to the area of sensing disks or the power consumption per unit [17].

There are two broad ways of brain-computer interface (BCI): invasive and noninvasive. The invasive technique can capture intracortical action potentials of neurons and thus,

ISRN Nanotechnology provides high signal strength spatiotemporally, for example, prediction of movement trajectory. In noninvasive technique, EEG and MEG have emerged as viable options; both of them have time resolutions in milliseconds. Any activity in brain is accompanied by change in ionic concentrations in neuron leading to polarization and depolarization. Such an electrical activity is measured by EEG, while MEG measures the magnetic field associated with these currents. Electric and magnetic fields are oriented perpendicular to each other [18].

Application of organic-, chemical-, and carbonnanotubes- (CNT-) based FETs for design of the superconducting transducers (SuFETTrs) of BS into different quantities (electrical and biochemical) is the proposed variant of interfacing [19]. The placement of the devices can be carried out both in vivo and in vitro with the possibility of forming the controlling BS from the said quantities. The range of picked up BS varies from 0.6 nA to 10 µA with frequencies from 20 to 2000 Hz. A further step should be the synthesis of the said two methods in order to develop the internal (implantable) nano-bio-interface arrays. This means wrapping of molecular nanowired PC around the axons of a nerve fibre or synapses of neurons in order to obtain the natural biosignals from the nervous system and brain. This leads to sensing access across a vast range of spatial and temporal scales, including the ability to read neural signals from a select subset of single neural cells in vivo. Moreover, this process can be executed in reverse for introducing the artificial control signals with the local neural code into the single cell electrical activity.

2. Biosignals and Nanoelements for Their Transduction As an electrical signal, the biosignal has two components: electrical potential or voltage and ionic or electronic currents. The first component is sufficiently developed and does not require penetration into the substances of biosignal propagation. The marketable progress in transducing of the second component began when the necessary instrumentation for measurement of micro- and nanodimensions had been created [14].

Short platinum nanowires (NWs) already have been used in submicroscopic sensors and other applications. A method of making long (cm) Pt NW of a few nanometers in diameter from electrospinning was described [20]. Those wires could be woven into the first self-supporting webs of pure platinum. Double-gated silicon NW structures (DGSiNW), where the position and/or type of the charge could be tuned within the NW by electric field, have been studied [21]. Self-assembled molecular nanowires were found to be composed of a single crystal, allowing good electrical transport with low resistivity [22].

An interesting structure is that of helical CNT or nanocoils for PCs. Nanocoils offer unique electronic properties that straight CNT do not have. The plasticity of CNT will be relevant to their use in nanoscale devices [23].

Carbon nanocoils (CNCs), as a new type of promising

ISRN Nanotechnology nanomaterials, have attracted considerable attention because of their potential applications, such as parts for nanoor micro-electromechanical systems, EM wave absorbers, reinforced composites, nanosolenoids, and field emission devices [24] Integrated CMOS image sensor device for in vivo neural imaging has been developed. Improvement in the packaging process has resulted in a compact single-chip device for minimally invasive imaging inside the mouse brain [25]. Application of the SuFET’s modifications such as CMOSuFET (low Tc ) [26] and coplanar SuFET (high Tc ) [27] broadens the range of requirements, which are being satisfied by the SuFETTr. Alternatively, an FET-based neurotransducer with CNT or PC kind of input circuit for the nerve and neuron impulse has been designed. A CNTFET with a high-temperature superconducting channel is introduced into the nerve fibre or brain tissue for transducing their signals in both directions [28].

Flexible antennas have the potential to enhance the emerging field of flexible electronics, which is primarily motivated by the desire to incorporate electronics into flexible substrates such as textiles, displays, and bandages [29]. The ability to reversibly deform antennas may also enable new capabilities (e.g., rolling and unrolling for remote deployment, enhanced durability). Relative to conventional copper antennas, fluidic antennas have several advantages [30]. Furthermore, it has been shown that ultrathin layers of metal can display superconductivity, but any limits on the size of superconducting systems remain a mystery.

On the other hand, (BETS) 2GaCl4, where BETS is bis (ethylenedithio) tetraselenafulvalene, is an organic superconductor, and in bulk it displays a superconducting gap that increases exponentially with the length of the molecular chain [31].

Graphene-solution-gated FET (G-SGFET) fabricated on copper foil offers outstanding electronic performance, is chemically stable and biologically inert, and can readily be processed on flexible substrates. Not only were the “action potentials” of individual cells detectable above the intrinsic electrical noise of the transistors, but these cellular signals could be recorded with high spatial and temporal resolution.

The analysis of the recorded cell signals and the electronic noise of the transistors confirm that graphene transistors surpass state-of-the-art devices for bioelectronic applications [32].

An organic FET (OFET) is characterized by textile process fully compatible size and geometry. This transistor has shown very interesting performances, with typical values of the electronic parameters very similar to those of planar devices. This result is very promising in view of innovative applications in the field of smart textiles [33]. Also FETs with single- and doublewire channels (NWFET) were fabricated to give some indication of the potential application of the molecular wires [22]. Finally, inkjet-printed FETs using carboxyl-functionalized nanotubes as source, drain, and gate electrodes, poly (ethylene glycol) (PEG-) functionalized nanotubes as the channel, and PEG as the gate dielectric were also tested and characterized. Considerable nonlinear transport in conjunction with a high channel current on/off

3

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Figure 1: Diagnostics of the biomedium with the necessary drugs delivering.

ratio of 70 was observed with PEG-functionalized nanotubes.

The positive temperature coefficient of channel resistance shows the nonmetallic behavior of the inkjet-printed films [34].

Finally, FETs with single- and doublewire channels were fabricated to give some indication of the potential application of the molecular wires. Substantial progress has been made in defining the performance limits and exploring applications based on NWFETs [22]. A five-channel FET structure is composed of two double-gate transistors and a bottom single-gate transistor on a silicon-on-insulator. 3D transistor structures such as multiple-gate FETs have been proposed and extensively studied as a promising solution to overcome the scaling limitations of planar bulk devices. They offer excellent multigate control of the channels and higher current drive [35]. In high-performance n-channel OFETs, charge carrier injection at the interface between the organic film and source/drain electrodes plays a crucial role [36].

3. An SuFETTr-Based Devices

Magnetic

Interface

The advent of semiconductor devices with nanoscale dimensions creates the potential to integrate nanoelectronics and optoelectronic devices with a great variety of biological systems. In such a case, 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 measurement problem in one-coordinate-dimensional measurements could be acquired naturally, according to the feedback reaction on the input exposure. Moreover, the organs of the senses of living beings could be regarded in the same way as the natural