Fourth Circuit Element Research Articles

Resistive Switching in Ag2Te Semiconductor Modulated by Ag+‐Ion Diffusion and Phase Transition

AbstractMemristors are considered to be the fourth circuit element and have great potential in areas like logic operations, information storage, and neuromorphic computing. The functional material in a memristor, which has a nonlinear resistance, is the key component to be developed. Herein, resistive switching is demonstrated and the structural evolutions in Ag2Te are examined under an external electric field. It is shown that the electroresistance effect is originating from an electronically triggered phase transition together with directional Ag+‐ion diffusion. Using in situ transmission electron microscopy, the phase transition from the monoclinic α‐Ag2Te into the face‐centered cubic b‐Ag2Te, accompanied by a change in resistance, is directly observed. Diffusion of Ag+‐ions modulates the localized density of Ag+‐ion vacancies, leading to a change in electrical conductivity and influences the threshold voltage to trigger the phase transition. During the electric field‐driven phase transition, the spontaneous and localized multiple polarizations from the low‐symmetry α‐Ag2Te (referring to an antiferroelectric structure) are vanishing in the cubic b‐Ag2Te (referring to a paraelectric structure). The abrupt resistance change of thin Ag2Te caused by the phase transition and modulated by the applied electric field demonstrates its great potential as functional material in volatile memory and memristors with a low‐energy consumption.

Microtubules as Sub-Cellular Memristors

Memristors represent the fourth electrical circuit element complementing resistors, capacitors and inductors. Hallmarks of memristive behavior include pinched and frequency-dependent I–V hysteresis loops and most importantly a functional dependence of the magnetic flux passing through an ideal memristor on its electrical charge. Microtubules (MTs), cylindrical protein polymers composed of tubulin dimers are key components of the cytoskeleton. They have been shown to increase solution’s ionic conductance and re-orient in the presence of electric fields. It has been hypothesized that MTs also possess intrinsic capacitive and inductive properties, leading to transistor-like behavior. Here, we show a theoretical basis and experimental support for the assertion that MTs under specific circumstances behave consistently with the definition of a memristor. Their biophysical properties lead to pinched hysteretic current–voltage dependence as well a classic dependence of magnetic flux on electric charge. Based on the information about the structure of MTs we provide an estimate of their memristance. We discuss its significance for biology, especially neuroscience, and potential for nanotechnology applications.

Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks

The memristor as the fourth circuit element, it can capture some key aspects of biological synaptic plasticity. So, it is significant that the characteristic of memristors is considered in neural networks. This paper investigates input-to-state stability (ISS) of a class of memristive simplified Cohen–Grossberg bidirectional associative memory (BAM) neural networks with variable time delays. In the sense of Filippov solution, some novel sufficient criteria for ISS are obtained based on differential inclusions and differential inequalities; when the input is zero, the stability of the total system is state stable. Furthermore, numerical simulations are illustrated to show the feasibility of our results.

(Invited) Artificial Neurons and Synapses with CVD MoS2 Facilitated By Electrode Engineering

The prevalent von Neumann architecture in today’s processors involves memory and processing units to reside in physically separate locations. With memory speeds lagging behind the processor speeds, latency in accessing data from memory has resulted in the “von Neumann bottleneck”. To alleviate this issue, several alternative non-von Neumann architectures have been explored. Neuromorphic computing is one such non-von Neumann approach, inspired by the human brain’s ability of cognitive recognition. The brain operates through a network of neurons that are connected to each other by synapses. In 2008, after the discovery of memristors, the fourth circuit element, researchers have explored the idea of mimicking synaptic behavior with a single memristive device.1-6 Albeit significant advances, synaptic devices using phase change materials-based memristors3,7-9 and metal oxide-based resistive switching devices5,6,10-13 have some limitations. These devices exhibit high programming current in the range of µA to mA. Additionally, the synaptic weight update, i.e. the increase (decrease) of the synapse’s conductance with the application of a continuous stream of identical positive (negative) input voltage pulses, is non-linear. Non-linearity in weight update increases the complexity of using these devices for real-time unsupervised learning. So, it becomes necessary to employ a materials system which exhibits low programming current as well as a linear weight update.14 Recently, two-dimensional (2D) materials are being largely explored to demonstrate their viability as electronic synapses and neurons.15-21 In this talk, we shall discuss the realization of a synaptic device using graphene/MoS2 heterostructures. In these devices, CVD-grown monolayer graphene acts as an electrode to CVD MoS2. These memristive devices exhibit low programming currents and a high dynamic range from 1 nA to 1 mA. In contrast with oxide-based or PCM-based synapses, these devices exhibit a gradual set and reset process when symmetric input voltage pulses are applied, resulting in a near-linear weight update. We shall also present the demonstration of an integrate-and-fire (IF) neuron using Ag/MoS2/Au vertical structures. These devices possess the four crucial features of an IF neuron – all-or-nothing spiking, threshold-driven firing, post-firing refractory period and stimulus strength based frequency response. Realizing neurons and synapses using the same materials system allows the monolithic integration of the essential building blocks of neuromorphic hardware, and bears potential for a highly scalable spiking neural networks suitable for unsupervised learning applications.

A Memristive Hyperchaotic Multiscroll Jerk System with Controllable Scroll Numbers

A memristor is the fourth circuit element, which has wide applications in chaos generation. In this paper, a four-dimensional hyperchaotic jerk system based on a memristor is proposed, where the scroll number of the memristive jerk system is controllable. The new system is constructed by introducing one extra flux-controlled memristor into three-dimensional multiscroll jerk system. We can get different scroll attractors by varying the strength of memristor in this system without changing the circuit structure. Such a method for controlling the scroll number without changing the circuit structure is very important in designing the modern circuits and systems. The new memristive jerk system can exhibit a hyperchaotic attractor, which has more complex dynamic behavior. Furthermore, coexisting attractors are observed in the system. Phase portraits, dissipativity, equilibria, Lyapunov exponents and bifurcation diagrams are analyzed. Finally, the circuit implementation is carried out to verify the new system.

Dynamics, Analysis and Implementation of a Multiscroll Memristor-Based Chaotic Circuit

This article introduces a novel four-dimensional autonomous multiscroll chaotic circuit which is derived from the actual simplest memristor-based chaotic circuit. A fourth circuit element — another inductor — is introduced to generate the complex behavior observed. A systematic study of the chaotic behavior is performed with the help of some nonlinear tools such as Lyapunov exponents, phase portraits, and bifurcation diagrams. Multiple scroll attractors are observed in Matlab, Pspice environments and also experimentally. We also observe the phenomenon of antimonotonicity, periodic and chaotic bubbles, multiple periodic-doubling bifurcations, Hopf bifurcations, crises and the phenomenon of intermittency. The chaotic dynamics of this circuit is realized by laboratory experiments, Pspice simulations, numerical and analytical investigations. It is observed that the results from the three environments agree to a great extent. This topology is likely convenient to be used to intentionally generate chaos in memristor-based chaotic circuit applications, given the fact that multiscroll chaotic systems have found important applications as broadband signal generators, pseudorandom number generators for communication engineering and also in biometric authentication.

Modeling and Suppression of Chaotic Ferroresonance in a Power System by Using Memristor-based System

Power systems contain capacitances and inductances that can initiate chaotic ferroresonance due to the non-linear operating characteristics of ferromagnetic materials, such as power transformer cores. Chaotic ferroresonance is a highly disturbing complex, non-linear phenomenon that may cause over-voltages and over-currents; hence, it can compromise regular system operation. The memristor is a non-linear passive two-terminal electricalcomponent, foreseen and introduced as the fourth ideal circuit element. In this study's novel approach, a memristive system is proposed for modeling chaotic ferroresonance in power systems. Employing the memristive circuit as the source of ferroresonance, flexible and adjustable solutions of the dynamic system can be obtained easily. The chaotic characteristic of the proposed system is verified by using a bifurcation diagram and Lyapunov exponent analysis. To reduce the effects of chaotic ferroresonance, a memristor-based system is proposed for damping chaotic oscillations of the system as a protection element. simulation program with integrated circuit emphasis (SPICE) simulations have been carried out for the performance analysis of the proposed system during ferroresonance, and suppression modes are presented.

Design and Analysis of Low Power Hybrid Memristor-CMOS Based Distinct Binary Logic Nonvolatile SRAM Cell

Memristor is a newly found fourth circuit element for the next generation emerging nonvolatile memory technology. In this paper, design of new type of nonvolatile static random access memory cell is proposed by using a combination of memristor and complemented metal oxide semiconductor. Biolek memristor model and CMOS 180 nm technology are used to form a single cell. By introducing distinct binary logic to avoid safety margin is left for each binary logic output and enables better read/write data integrity. The total power consumption reduces from 0.407 mw (milli-watt) to 0.127 mw which is less than existing memristor based memory cell of the same CMOS technology. Read and write time is also significantly reduced. However, write time is higher than conventional 6T SRAM cell and can be reduced by increasing motion of electron in the memristor. The change of the memristor state is shown by applying piecewise linear input voltage.

A Novel Nondestructive Read/Write Circuit for Memristor-Based Memory Arrays

Emerging nonvolatile universal memory technology is vital for providing the huge storage capabilities, which is needed for nanocomputing facilities. Memristor, which is recently discovered and known as the missing fourth circuit element, is a potential candidate for the next-generation memory. Memristor has received extra attention in the last few years. To support this effort, this paper presents a novel read/write circuit that facilitates the reading and writing operation of the Memristor device as a memory element. The advantages of the proposed read/write circuit are threefold. First, the proposed circuit has a nondestructive successive reading cycle capability. Second, it occupies less die area. Finally, the proposed read/write circuit offers a significant improvement in power consumption and delay time compared with other read/write circuits.

Application of the Memristor in Reconfigurable Electromagnetic Devices

The recently popularized memristor, short for memory resistor, is investigated in this paper for its potential as a new and attractive method for enabling reconfigurable radio-frequency (RF) devices. The charge- or flux-controlled resistance of this “fourth circuit element” allows for easy reconfigurability and maintains its configured state in the absence of controlling signals. A specialized finite-difference time-domain simulation code is developed and employed to design devices with embedded memristors. The time-domain code allows observation of the nonlinear memristor switching characteristics and real-time functionality of the reconfigurable device. Several different reconfigurable RF devices are designed here to demonstrate the versatility of the memristor and determine the behavior of systems which utilize them.

Bulk mixed ion electron conduction in amorphous gallium oxide causes memristive behaviour

In thin films of mixed ionic electronic conductors sandwiched by two ion-blocking electrodes, the homogeneous migration of ions and their polarization will modify the electronic carrier distribution across the conductor, thereby enabling homogeneous resistive switching. Here we report non-filamentary memristive switching based on the bulk oxide ion conductivity of amorphous GaOx (x~1.1) thin films. We directly observe reversible enrichment and depletion of oxygen ions at the blocking electrodes responding to the bias polarity by using photoemission and transmission electron microscopies, thus proving that oxygen ion mobility at room temperature causes memristive behaviour. The shape of the hysteresis I-V curves is tunable by the bias history, ranging from narrow counter figure-eight loops to wide hysteresis, triangle loops as found in the mathematically derived memristor model. This dynamical behaviour can be attributed to the coupled ion drift and diffusion motion and the oxygen concentration profile acting as a state function of the memristor.

Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System

Here, we introduce a new class of computer which does not use any circuit or logic gate. In fact, no program needs to be written: it learns by itself and writes its own program to solve a problem. Gödel’s incompleteness argument is explored here to devise an engine where an astronomically large number of “if-then” arguments are allowed to grow by self-assembly, based on the basic set of arguments written in the system, thus, we explore the beyond Turing path of computing but following a fundamentally different route adopted in the last half-a-century old non-Turing adventures. Our hardware is a multilayered seed structure. If we open the largest seed, which is the final hardware, we find several computing seed structures inside, if we take any of them and open, there are several computing seeds inside. We design and synthesize the smallest seed, the entire multilayered architecture grows by itself. The electromagnetic resonance band of each seed looks similar, but the seeds of any layer shares a common region in its resonance band with inner and upper layer, hence a chain of resonance bands is formed (frequency fractal) connecting the smallest to the largest seed (hence the name invincible rhythm or Ajeya Chhandam in Sanskrit). The computer solves intractable pattern search (Clique) problem without searching, since the right pattern written in it spontaneously replies back to the questioner. To learn, the hardware filters any kind of sensory input image into several layers of images, each containing basic geometric polygons (fractal decomposition), and builds a network among all layers, multi-sensory images are connected in all possible ways to generate “if” and “then” argument. Several such arguments and decisions (phase transition from “if” to “then”) self-assemble and form the two giant columns of arguments and rules of phase transition. Any input question is converted into a pattern as noted above, and these two astronomically large columns project a solution. The driving principle of computing is synchronization and de-synchronization of network paths, the system drives towards highest density of coupled arguments for maximum matching. Memory is located at all layers of the hardware. Learning, computing occurs everywhere simultaneously. Since resonance chain connects all computing seeds, wireless processing is feasible without a screening effect. The computing power is increased by maximizing the density of resonance states and bandwidth of the resonance chain together. We discovered this remarkable computing while studying the human brain, so we present a new model of the human brain in terms of an experimentally determined resonance chain with bandwidth 10−15 Hz (complete brain with all sensors) to 10+15 Hz (DNA) along with its implementation using a pure organic synthesis of entire computer (brain jelly) in our lab, software prototype as proof of concept and finally a new fourth circuit element (Hinductor) based beyond Complementary metal-oxide semiconductor (CMOS) hardware is also presented.

A memristor-based architecture combining memory and image processing

Image processing is a type of memory-access-intensive application and is applied in many fields. Logic operations are very simple ones in image processing. During these operations, memory access takes a majority of the total time consumed, which puts a great pressure on memory access speed and bandwidth. However, in traditional von Neumann architecture, memory access is the inherent bottleneck of the system; that is, the speed of memory’s data supply is far lower than the data request of processor. Memristor is considered to be the fourth circuit element after resistor, capacitor and inductor. It has the capacity of both processing and memory, which supplies a new idea for solving the “memory wall” problem. In this paper, memristor is used to build an architecture combining computing and memory, where the memory has the ability to handle some simple image processing operations. This architecture can reduce readings and writings of memory effectively, which saves memory bandwidth thus improving the efficiency of the system. Logic operations of images are considered in this paper to validate the architecture. The experimental results and theoretical analysis indicate that the architecture can reduce memory access effectively.

On the simplest fractional-order memristor-based chaotic system

In 1695, G. Leibniz laid the foundations of fractional calculus, but mathematicians revived it only 300 years later. In 1971, L.O. Chua postulated the existence of a fourth circuit element, called memristor, but Williams’s group of HP Labs realized it only 37 years later. By looking at these interdisciplinary and promising research areas, in this paper, a novel fractional-order system including a memristor is introduced. In particular, chaotic behaviors in the simplest fractional-order memristor-based system are shown. Numerical integrations (via a predictor–corrector method) and stability analysis of the system equilibria are carried out, with the aim to show that chaos can be found when the order of the derivative is 0.965. Finally, the presence of chaos is confirmed by the application of the recently introduced 0-1 test.

Excitable Reaction-Diffusion Media with Memristors

We numerically investigated the dynamics of new reaction-diffusion-type excitable media where the diffusion coefficient is represented by memristive dynamics. The medium consists of an array of excitable Oregonators and each Oregonator is locally coupled with each other via memristors, which were claimed to be the fourth circuit element exhibiting a relationship between flux φ and charge q. Through extensive numerical simulations, we found that i) memristor conductances were modulated by excitable waves passing on the memristor depending on the memristor's polarity, ii) the velocity of the excitable wave propagation is thus modulated by the change in the memristor conductance, and the degree of modulation is inversely proportional to the time constant of the memristor model, and iii) nonuniform spatial patterns are generated under the periodic boundary condition (ring structure of excitable media).

Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories

Novel nonvolatile universal memory technology is essential for providing required storage for nanocomputing. As a potential contender for the next-generation memory, the recently found "the missing fourth circuit element," memristor, has drawn a great deal of research interests. In this paper, by starting from basic memristor device equations, we develop a comprehensive set of properties and design equations for memristor based memories. Our analyses are specifically targeting key electrical memristor device characteristics relevant to memory operations. Using our derived properties, we investigate the design of read and write circuits and analyze important data integrity and noise-tolerance is sues.

The fourth circuit element

Arising from: D. B. Strukov, G. S. Snider, D. R. Stewart & R. S. Williams , 80–83 (2008)10.1038/nature06932 In 1971, Chua suggested1 that there should in principle exist a circuit element linking electrical charge, q, and magnetic flux, ϕ. Strukov et al.2 claim recently to have found such a link. However, here I point out that Chua’s suggestion was in fact preceded by experimental evidence3,4,5 for magneto-electric effects whereby magnetic and electrical signals are interconverted. When the stimulus is magnetic and the response is electrical, the linear magneto-electric coupling constant5, α, is typically reported as dP/dH, where P represents the electrical polarization and H represents the applied magnetic field. The coupling constant could equally be presented as dq/dϕ if divided by the permeability of free space.