arXiv subjects

Y. V. Pershin

Publications and source records attributed to Y. V. Pershin.

At least 19 recordsLinked to original sources

arXiv2023

SPICE Modeling of Memcomputing Logic Gates

Memcomputing logic gates generalize the traditional Boolean logic gates for operation in the reverse direction. According to the literature, this functionality enables the efficient solution of computationally-intensive problems including factorization and NP-complete problems. To approach the deployment of memcomputing gates in hardware, this paper introduces SPICE models of memcomputing logic gates following their original definition. Using these models, we demonstrate the behavior of single gates as well as small self-organizing circuits. We also correct some inconsistencies in the prior literature. Importantly, the correct schematics of dynamic correction module is reported here for the first time. Our work makes memcomputing more accessible to those who are interested in this emerging computing technology.

cs.ET↗

arXiv2023

A probabilistic model of resistance jumps in memristive devices

Resistance switching memory cells such as electrochemical metallization cells and valence change mechanism cells have the potential to revolutionize information processing and storage. However, the creation of deterministic resistance switching devices is a challenging problem that is still open. At present, the modeling of resistance switching cells is dominantly based on deterministic models that fail to capture the cycle-to-cycle variability intrinsic to these devices. Herewith we introduce a state probability distribution function and associated integro-differential equation to describe the switching process consisting of a set of stochastic jumps. Numerical and analytical solutions of the equation have been found in two model cases. This work expands the toolbox of models available for resistance switching cells and related devices, and enables a rigorous description of intrinsic physical behavior not available in other models.

cond-mat.mes-hall↗

arXiv2022

Memristive Ising Circuits

The Ising model is of prime importance in the field of statistical mechanics. Here we show that Ising-type interactions can be realized in periodically-driven circuits of stochastic binary resistors with memory. A key feature of our realization is the simultaneous co-existence of ferromagnetic and antiferromagnetic interactions between two neighboring spins -- an extraordinary property not available in nature. We demonstrate that the statistics of circuit states may perfectly match the ones found in the Ising model with ferromagnetic or antiferromagnetic interactions, and, importantly, the corresponding Ising model parameters can be extracted from the probabilities of circuit states. Using this finding, the Ising Hamiltonian is re-constructed in several model cases, and it is shown that different types of interaction can be realized in circuits of stochastic memristors.

cond-mat.mes-hall↗

arXiv2022

Holy memristor

It was recently shown that when a drop of Glenlivet whiskey evaporates, it leaves behind a uniform deposit [PRL 116, 124501 (2016)]. We utilize this fascinating finding in the fabrication of electrochemical metallization memory (ECM) cells. The top (Ag) and bottom (Co) electrodes in our structure are separated by a layer of Glenlivet whiskey deposit (an insulator). Measurements show the device response is typical of ECM cells that involve threshold-type switching, pinched hysteresis loops, and a large difference between the high- and low-resistance states. The surface coating process used in our experiments simplifies the device fabrication and results in a biodegradable insulating layer, which may facilitate the recovery of recyclable materials at the end of the device's use.

physics.app-ph↗

arXiv2022

Analytic and SPICE modeling of stochastic ReRAM circuits

The modeling of conventional (deterministic) electronic circuits - ones consisting of transistors, resistors, capacitors, inductors, and other traditional electronic components - is a well-established subject. The cycle-to-cycle variability of emerging electronic devices, in particular, certain ReRAM cells, has led to the concept of stochastic circuits. Unfortunately, even in relatively simple cases, the direct transient analysis of stochastic circuits is computationally demanding and potentially impractical, if possible at all. An important development in this area has been the application of a master equation that is easily implemented in SPICE. In this conference paper, we briefly review the master equation approach and present an improved implementation of this approach in SPICE. Moreover, we find an attractor state in a periodically driven memristive circuit - a stochastic counterpart of deterministic memristor attractors.

cs.ET↗

arXiv2022

Kinks in buckled graphene uncompressed and compressed in the longitudinal direction

In this Chapter we provide a review of the main results obtained in the modeling of graphene kinks and antikinks, which are elementary topological excitations of buckled graphene membranes. We introduce the classification of kinks, as well as discuss kink-antikink scattering, and radiation-kink interaction. We also report some new findings including i) the evidence that the kinetic energy of graphene kinks is described by a relativistic expression, and ii) demonstration of damped dynamics of kinks in membranes compressed in the longitudinal direction. Special attention is paid to highlight the similarities and differences between the graphene kinks and kinks in the classical scalar $ϕ^4$ theory. The unique properties of graphene kinks discussed in this Chapter may find applications in nanoscale motion.

cond-mat.mes-hall↗

arXiv2021

The influence of constriction on the motion of graphene kinks

Graphene kinks are topological states of buckled graphene membranes. We show that when a moving kink encounters a constriction, there are three general classes of behavior: reflection, trapping, and transmission. Overall, constriction is characterized by an attractive potential. In the case of a simple symmetric constriction, the kink potential energy has a relatively deep minimum surrounded by energy barriers. However, the potential energy alone does not fully define the class of behavior: the effect of a resonant reflection was observed in our simulations. Moreover, we demonstrate that asymmetric constrictions can transform kinks from one type into another. MD simulation results are compared with predictions of the classical $ϕ^4$ model.

cond-mat.mes-hall↗

arXiv2021

The Fourier signatures of memristive hysteresis

While resistors with memory, sometimes called memristive elements (such as ReRAM cells), are often studied under conditions of periodic driving, little attention has been paid to the Fourier features of their memory response (hysteresis). Here we demonstrate experimentally that the hysteresis of memristive systems can be unambiguously distinguished from the linear or non-linear response of systems without hysteresis by the values of certain Fourier series coefficients. We also show that the Fourier series convergence depends on driving conditions, and introduce a measure of hysteresis. These results may be used to quantify the memory content of resistive memories, and tune their Fourier spectrum according to the excitation signal.

physics.app-ph↗

arXiv2021

An experimental demonstration of the memristor test

A simple and unambiguous test has been recently suggested [J. Phys. D: Applied Physics, 52, 01LT01 (2018)] to check experimentally if a resistor with memory is indeed a memristor, namely a resistor whose resistance depends only on the charge that flows through it, or on the history of the voltage across it. However, although such a test would represent the litmus test for claims about memristors (in the ideal sense), it has yet to be applied widely to actual physical devices. In this paper, we experimentally apply it to a current-carrying wire interacting with a magnetic core, which was recently claimed to be a memristor (so-called `$Φ$ memristor') [J. Appl. Phys. 125, 054504 (2019)]. The results of our experiment demonstrate unambiguously that this `$Φ$ memristor' is not a memristor: it is simply an inductor with memory. This demonstration casts further doubts that ideal memristors do actually exist in nature or may be easily created in the lab.

cond-mat.mes-hall↗

arXiv2021

Theory of heterogeneous circuits with stochastic memristive devices

We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous stochastic circuits, namely, the circuits combining binary or multi-state stochastic memristive devices and continuum reactive components (capacitors and/or inductors). Such circuits are described in terms of occupation probabilities of memristive states that are functions of reactive variables. As an illustrative example, the series circuit of a binary memristor and capacitor is considered in detail. Some analytical solutions are found. Our work offers a novel analytical/numerical tool for modeling complex stochastic networks, which may find a broad range of applications.

cs.ET↗

arXiv2021

Comment on "$Φ$ memristor: Real memristor found" by F. Z. Wang, L. Li, L. Shi, H. Wu, and L. O. Chua [J. Appl. Phys. 125, 054504 (2019)]

Wang et al. claim [J. Appl. Phys. 125, 054504 (2019)] that a current-carrying wire interacting with a magnetic core represents a memristor. Here, we demonstrate that this claim is false. We first show that such memristor "discovery" is based on incorrect physics, which does not even capture basic properties of magnetic core materials, such as their magnetic hysteresis. Moreover, the predictions of Wang et al.'s model contradict the experimental curves presented in their paper. Additionally, the theoretical pinched hysteresis loops presented by Wang et al. can not be reproduced if their model is used, and there are serious flaws in their "negative memristor" emulator design. Finally, a simple gedanken experiment shows that the proposed $Φ$-memristor would fail the memristor test we recently suggested in J. Phys. D: Appl. Phys. 52, 01LT01 (2019). The device "discovered" by Wang et al. is just an inductor with memory.

cs.ET↗

arXiv2020

Probabilistic Memristive Networks: Application of a Master Equation to Networks of Binary ReRAM cells

The possibility of using non-deterministic circuit components has been gaining significant attention in recent years. The modeling and simulation of their circuits require novel approaches, as now the state of a circuit at an arbitrary moment in time cannot be precisely predicted. Generally, these circuits should be described in terms of probabilities, the circuit variables should be calculated on average, and correlation functions should be used to explore interrelations among the variables. In this paper, we use, for the first time, a master equation to analyze the networks composed of probabilistic binary memristors. Analytical solutions of the master equation for the case of identical memristors connected in-series and in-parallel are found. Our analytical results are supplemented by results of numerical simulations that extend our findings beyond the case of identical memristors. The approach proposed in this paper facilitates the development of probabilistic/stochastic electronic circuits and advance their real-world applications.

cs.ET↗

arXiv2020

Modeling networks of probabilistic memristors in SPICE

Efficient simulation of probabilistic memristors and their networks requires novel modeling approaches. One major departure from the conventional memristor modeling is based on a master equation for the occupation probabilities of network states [arXiv:2003.11011 (2020)]. In the present article, we show how to implement such master equations in SPICE - a general-purpose circuit simulation program. In the case studies, we simulate the dynamics of ac-driven probabilistic binary and multi-state memristors, and dc-driven networks of probabilistic binary and multi-state memristors. Our SPICE results are in perfect agreement with known analytical solutions. Examples of LTspice codes are included.

cs.ET↗

arXiv2020

An experimental proof that resistance-switching memories are not memristors

It has been suggested that all resistive-switching memory cells are memristors. The latter are hypothetical, ideal devices whose resistance, as originally formulated, depends only on the net charge that traverses them. Recently, an unambiguous test has been proposed [J. Phys. D: Appl. Phys. {\bf 52}, 01LT01 (2019)] to determine whether a given physical system is indeed a memristor or not. Here, we experimentally apply such a test to both in-house fabricated Cu-SiO2 and commercially available electrochemical metallization cells. Our results unambiguously show that electrochemical metallization memory cells are not memristors. Since the particular resistance-switching memories employed in our study share similar features with many other memory cells, our findings refute the claim that all resistance-switching memories are memristors. They also cast doubts on the existence of ideal memristors as actual physical devices that can be fabricated experimentally. Our results then lead us to formulate two memristor impossibility conjectures regarding the impossibility of building a model of physical resistance-switching memories based on the memristor model.

cond-mat.mes-hall↗

arXiv2019

A Demonstration of Implication Logic Based on Volatile (Diffusive) Memristors

Implication logic gates that are based on volatile memristors are demonstrated experimentally with the use of relay-based volatile memristor emulators of an original design. The fabricated logic circuit involves two volatile memristors and it is capable of performing four fundamental logic functions (two types of material implication and the negations thereof). Moreover, current-voltage characteristics of individual emulators are recorded and self-sustained oscillations in a resistor-volatile memristor circuit are found. The developed emulator offers a great potential for memristive circuit experiments because of its simplicity, similarity of response with volatile memristors, and low cost. Our findings, which are based on emulators, can easily be reproduced with physical volatile memristors and, thus, open up possibilities for emerging in-memory computing architectures.

cs.ET↗

arXiv2019

Bifurcation analysis of a TaO memristor model

This paper presents a study of bifurcation in the time-averaged dynamics of TaO memristors driven by narrow pulses of alternating polarities. The analysis, based on a physics-inspired model, focuses on the stable fixed points and on how these are affected by the pulse parameters. Our main finding is the identification of a driving regime when two stable fixed points exist simultaneously. To the best of our knowledge, such bistability is identified in a single memristor for the first time. This result can be readily tested experimentally, and is expected to be useful in future memristor circuit designs.

cs.ET↗

arXiv2019

Comment on "If it's pinched it's a memristor" by L. Chua [Semicond. Sci. Technol. 29, 104001 (2014)]

In his paper "If it's pinched it's a memristor" [Semicond. Sci. Technol. 29, 104001 (2014)] L. Chua claims to extend the notion of memristor to all two-terminal resistive devices that show a hysteresis loop pinched at the origin. He also states that memcapacitors and meminductors can be defined by a trivial replacement of symbols in the memristor relations, and, therefore, there should be a correspondence between the hysteresis curves of different types of memory elements. This leads the author to the erroneous conclusion that charge-voltage curves of any memcapacitive devices should be pinched at the origin. The purpose of this Comment is to correct the wrong statements in Chua's paper, as well as to highlight some other inconsistencies in his reasoning. We also provide experimental evidence of a memcapacitive device showing non-pinched hysteresis.

cs.ET↗

arXiv2019

On the validity of memristor modeling in the neural network literature

An analysis of the literature shows that there are two types of non-memristive models that have been widely used in the modeling of so-called "memristive" neural networks. Here, we demonstrate that such models have nothing in common with the concept of memristive elements: they describe either non-linear resistors or certain bi-state systems, which all are devices without memory. Therefore, the results presented in a significant number of publications are at least questionable, if not completely irrelevant to the actual field of memristive neural networks.

cs.NE↗