Memcapacitive Systems Research Articles

Modeling of the generic memcapacitors using higher-order multi-ports

The paper introduces predictive modeling of generic memcapacitors using multi-port versions of fundamental elements of Chua’s table. Generic memcapacitors are the most common type of memcapacitive systems; they behave as state-dependent capacitors, with the capacitance being independent of the voltage and charge. The predictive model consists of a multiport capacitor and an associated dynamic system that represents the state of the dynamics. It is shown that the potential function of the multi-port is the energy of the electrostatic field of the memcapacitor. It enables incorporating this element into the framework of Lagrangian formalism. Practical implementations of the model are presented on specific examples from selected fields of science: a memory circuit with an electrostatically controlled bistable membrane, and a lipid bilayer model inspired by the processes that occur in cell membranes.

Hidden multistability in four fractional-order memristive, meminductive and memcapacitive chaotic systems with bursting and boosting phenomena

Hidden multistability in four fractional-order memristive, meminductive and memcapacitive chaotic systems with bursting and boosting phenomena

Ideal memcapacitors and meminductors are overunity devices

It is rigorously proved that ideal memcapacitors and meminductors are not passive or cyclo-passive devices. Equivalently, this implies that there exist excitation profiles that allow to extract more energy from the device than it is supplied with; so that their energy conversion efficiency exceeds 100%. This means that ideal memcapacitors and meminductors violate the First Law of thermodynamics, and thus are non-physical as they constitute so-called overunity systems. An illustrative mechanical analogue is provided for which such an excitation profile is explicitly constructed. Hence ideal memcapacitors and meminductors are mathematical artefacts, and the question arises what this implies for the properties of non-ideal memcapacitors and meminductors (or, memcapacitive systems and meminductive systems), which do satisfy the First Law.

The fabrication and application of Ni-DNA nanowire-based nanoelectronic devices

DNA is a self-assembled, double stranded natural molecule that can chelate and align nickel ions between its base pairs. The fabrication of a DNA-guided nickel ion chain (Ni-DNA) device was successful, as indicated by the conducting currents exhibiting a Ni ion redox reaction-driven negative differential resistance effect, a property unique to mem-elements (1). The redox state of nickel ions in the Ni-DNA device is programmable by applying an external bias with different polarities and writing times (2). The multiple states of Ni-DNA-based memristive and memcapacitive systems were characterized (3). As such, the development of Ni-DNA nanowire device-based circuits in the near future is proposed.

Qubit-Based Memcapacitors and Meminductors

It is shown that superconducting charge and phase qubits are quantum versions of memory capacitive and inductive systems, respectively. We demonstrate that such quantum memcapacitive and meminductive devices offer remarkable and rich response functionalities. In particular, when subjected to periodic input, qubit-based memcapacitors and meminductors exhibit unusual hysteresis curves. Our work not only extends the set of known memcapacitive and meminductive systems to qubit-based quantum devices but also highlights their unique properties potentially useful for future technological applications.

Behavior characteristics of a cap-resistor, memcapacitor, and a memristor from the response obtained of RC and RL electrical circuits described by fractional differential equations

This paper provides an analysis of RC and RL electrical circuits described by a fractional difierential equation of Caputo type. The order considered is 0<γ ≤ 1. The Laplace transform of the fractional derivative is used. To keep the dimensionality of the physical quantities, R, C, L, and an auxiliary parameter σ are introduced, characterizing the existence of fractional components in the system. The relationship between γ and σ is reported. The response obtained from the fractional RC and RL circuits exhibits the characteristic behaviors of a cap-resistor, memcapacitor, and memristor, as well as charge-voltage for memcapacitive systems and current-voltage for memristive systems. The relationship between Ohm’s law and Faraday’s laws for the charge stored in a capacitor and induction is reported. Illustrative examples are presented.

Electromechanical Emulator of Memristive Systems and Devices

This paper introduces a new approach to emulate two-terminal circuit elements with memory—memristive, memcapacitive, and meminductive systems and devices. Unlike conventional approaches requiring a power source, the suggested emulators are intrinsically passive, floating, and capable of storing information in a nonvolatile manner. In this paper, we focus on the emulator of memristive systems and report its experimental realization based on a dc micromotor mechanically coupled with a potentiometer. The emulator response to various input voltage signals demonstrates its resemblance to bipolar nonvolatile solid-state memristive devices.

Memcomputing with membrane memcapacitive systems

We show theoretically that networks of membrane memcapacitive systems—capacitors with memory made out of membrane materials—can be used to perform a complete set of logic gates in a massively parallel way by simply changing the external input amplitudes, but not the topology of the network. This polymorphism is an important characteristic of memcomputing (computing with memories) that closely reproduces one of the main features of the brain. A practical realization of these membrane memcapacitive systems, using, e.g., graphene or other 2D materials, would be a step forward towards a solid-state realization of memcomputing with passive devices.

Understanding memristors and memcapacitors in engineering mechanics applications

A significant event happened for electrical engineering in 2008, when researchers at HP Labs announced that they had found “the missing memristor,” a fourth basic circuit element that was postulated nearly four decades earlier by Dr. Leon Chua, who was also instrumental in developing the mathematical theories of memristive, memcapacitive, and meminductive systems, resulting in an entire class of “mem-models” that are the foundation of the present work. By applying well-known mechanical–electrical analogies, the mathematics of mem-models may be transferred to the setting of engineering mechanics, creating the mechanical counterparts of memristors, memcapacitors, etc. However, this transfer is nontrivial; for example, a new concept and state variable called “absement,” the time integral of deformation, emerge. We study these mem-models, which are characterized by a “zero-crossing” property that has interesting implications for nonlinear constitutive modeling, particularly hysteresis, and we identify some examples of “mem-dashpots” and “mem-springs,” which include displacement-dependent and variable dampers, the superelasticity found in shape-memory alloys, and the pinched hysteresis loops associated with self-centering structures. This work adds to the fast-growing body of literature on elements and systems labeled with “mem,” which is a basic branch of study in nonlinear dynamics.

Dynamic computing random access memory

The present von Neumann computing paradigm involves a significant amount of information transfer between a central processing unit and memory, with concomitant limitations in the actual execution speed. However, it has been recently argued that a different form of computation, dubbed memcomputing (Di Ventra and Pershin 2013 Nat. Phys. 9 200–2) and inspired by the operation of our brain, can resolve the intrinsic limitations of present day architectures by allowing for computing and storing of information on the same physical platform. Here we show a simple and practical realization of memcomputing that utilizes easy-to-build memcapacitive systems. We name this architecture dynamic computing random access memory (DCRAM). We show that DCRAM provides massively-parallel and polymorphic digital logic, namely it allows for different logic operations with the same architecture, by varying only the control signals. In addition, by taking into account realistic parameters, its energy expenditures can be as low as a few fJ per operation. DCRAM is fully compatible with CMOS technology, can be realized with current fabrication facilities, and therefore can really serve as an alternative to the present computing technology.

Mutator for transferring a memristor emulator into meminductive and memcapacitive circuits

In this paper, a concise but effective interface circuit for transforming a memristor into meminductive and memcapacitive systems is designed. This newly proposed interface circuit, constructed by only two current conveyors, is equipped with three available ports, which can provide six connecting combinations in terms of one resistor, one capacitor, and one memristor. For the sake of confirming the design effectiveness, theoretical and simulation discussions are hence introduced and all the experimental waveforms provide conclusive evidence to validate the correctness of these new mutators. The most attractive features of this new interface circuit are the floating terminals and convenient practical implementation.

An account of spin memristive and memcapacitive systems: Next generation memory devices

We explore the current state of the progress in memristive and memcapacitive devices and their possible applications in electronic, spintronic and in the field of biological neural network from the first proposition by Leon O. Chua in 1971. This demands inclusion of devices exhibiting memristive behavior due to change in internal state following variations in temperature, spin polarization, resistance and history dependent channel conductance in axons. Memcapacitive systems that can be realized from metamaterial medium with metal layer embedding, from charge transport through nanopore, and by the vibration of strained elastic membrane replacing one plate of the regular capacitor has also been discussed. Finally, memristor and memcapacitor based circuits finding application in analog memory and computing have been reported.

Memcapacitive neural networks

We show that memcapacitive (memory capacitive) systems can be used as synapses in artificial neural networks. As an example of our approach, we discuss the architecture of an integrate-and-fire neural network based on memcapacitive synapses. Moreover, we demonstrate that the spike-timing-dependent plasticity can be simply realized with some of these devices. Memcapacitive synapses are a low-energy alternative to memristive synapses for neuromorphic computation.

On the physical properties of memristive, memcapacitive and meminductive systems

We discuss the physical properties of realistic memristive, memcapacitive and meminductive systems. In particular, by employing the well-known theory of response functions and microscopic derivations, we show that resistors, capacitors and inductors with memory emerge naturally in the response of systems—especially those of nanoscale dimensions—subjected to external perturbations. As a consequence, since memristances, memcapacitances and meminductances are simply response functions, they are not necessarily finite. This means that, unlike what has always been argued in some literature, diverging and non-crossing input–output curves of all these memory elements are physically possible in both quantum and classical regimes. For similar reasons, it is not surprising to find memcapacitances and meminductances that acquire negative values at certain times during dynamics, while the passivity criterion of memristive systems imposes always a non-negative value on the resistance at any given time. We finally show that ideal memristors, namely those whose state depends only on the charge that flows through them (or on the history of the voltage), are subject to very strict physical conditions and are unable to protect their memory state against the unavoidable fluctuations, and therefore are susceptible to a stochastic catastrophe. Similar considerations apply to ideal memcapacitors and meminductors.

The parallel approach

In present day technology, storing and processing of information occur on physically distinct regions of space. Not only does this result in space limitations; it also translates into unwanted delays in retrieving and processing of relevant information. There is, however, a class of two-terminal passive circuit elements with memory, memristive, memcapacitive and meminductive systems -- collectively called memelements -- that perform both information processing and storing of the initial, intermediate and final computational data on the same physical platform. Importantly, the states of these memelements adjust to input signals and provide analog capabilities unavailable in standard circuit elements, resulting in adaptive circuitry, and providing analog massively-parallel computation. All these features are tantalizingly similar to those encountered in the biological realm, thus offering new opportunities for biologically-inspired computation. Of particular importance is the fact that these memelements emerge naturally in nanoscale systems, and are therefore a consequence and a natural by-product of the continued miniaturization of electronic devices. We will discuss the various possibilities offered by memcomputing, discuss the criteria that need to be satisfied to realize this paradigm, and provide an example showing the solution of the shortest-path problem and demonstrate the healing property of the solution path.

Lagrange formalism of memory circuit elements: Classical and quantum formulations

The general Lagrange-Euler formalism for the three memory circuit elements, namely, memristive, memcapacitive, and meminductive systems, is introduced. In addition, mutual meminductance, i.e., mutual inductance with a state depending on the past evolution of the system, is defined. The Lagrange-Euler formalism for a general circuit network, the related work-energy theorem, and the generalized Joule's first law are also obtained. Examples of this formalism applied to specific circuits are provided, and the corresponding Hamiltonian and its quantization for the case of nondissipative elements are discussed. The notion of memory quanta, the quantum excitations of the memory degrees of freedom, is presented. Specific examples are used to show that the coupling between these quanta and the well-known charge quanta can lead to a splitting of degenerate levels and to other experimentally observable quantum effects.

Teaching Memory Circuit Elements via Experiment-Based Learning

The class of memory circuit elements which comprises memristive, memcapacitive, and meminductive systems, is gaining considerable attention in a broad range of disciplines. This is due to the enormous flexibility these elements provide in solving diverse problems in analog/neuromorphic and digital/quantum computation; the possibility to use them in an integrated computing-memory paradigm, massively-parallel solution of different optimization problems, learning, neural networks, etc. The time is therefore ripe to introduce these elements to the next generation of physicists and engineers with appropriate teaching tools that can be easily implemented in undergraduate teaching laboratories. In this paper, we suggest the use of easy-to-build emulators to provide a hands-on experience for the students to learn the fundamental properties and realize several applications of these memelements. We provide explicit examples of problems that could be tackled with these emulators that range in difficulty from the demonstration of the basic properties of memristive, memcapacitive, and meminductive systems to logic/computation and cross-bar memory. The emulators can be built from off-the-shelf components, with a total cost of a few tens of dollars, thus providing a relatively inexpensive platform for the implementation of these exercises in the classroom. We anticipate that this experiment-based learning can be easily adopted and expanded by the instructors with many more case studies.

Electrically tunable sign of capacitance in planar W-doped vanadium dioxide micro-switches

Negative capacitance (NC) in a planar W-doped VO2 micro-switch was observed at room temperature in the low-frequency range 1 kHz–10 MHz. The capacitance changed from positive to negative values as the W-doped VO2 active layer switched from semiconducting to metallic state under applied voltage. In addition, a capacitance–voltage hysteresis was observed as the applied voltage was cycled from −35 to 35 V. These observations suggest that NC results from the increase of the electrically induced conductivity in the active layer. This NC phenomenon could be exploited in advanced multifunctional devices including ultrafast switches, field-effect transistors and memcapacitive systems.

Emulation of floating memcapacitors and meminductors using current conveyors

We suggest circuit realizations of emulators transforming memristive devices into effective floating memcapacitive and meminductive systems. The emulator's circuits are based on second generation current conveyors and involve either four single-output or two dual-output current conveyors. The equations governing the resulting memcapactive and meminductive systems are presented.