Understanding memristors and memcapacitors in engineering mechanics applications

Pei, Jin‐Song; Wright, Joseph P.; Todd, Michael D.; Masri, Sami F.; Gay–Balmaz, François

doi:10.1007/s11071-014-1882-3

Cited by 35 publications

(32 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mem-models for dampers and springs are briefly introduced here, following Pei et al 31 The general mem-dashpot model, also called memristive system model, is defined as follows for a flow-controlled setting:…”

Section: Definitions For Mem-modelsmentioning

confidence: 99%

“…[32][33][34] Specifically, in engineering mechanics applications, mem-models are for modeling memory effects, that is, hysteresis. For example, the use of a and p as state variables is introduced in previous studies 30,31,35 to capture history/path dependency.…”

Section: Definitions For Mem-modelsmentioning

confidence: 99%

“…Mem-dampers and mem-springs are generalized dampers and springs, respectively. Relative velocity and relative displacement are specified for dampers and springs, for example, in Karnopp et al 36 Consider the following mem-dashpot and mem-spring example defined using D and S, respectively, each as a simple time-invariant system model 30,31,35 :…”

Section: Definitions For Mem-modelsmentioning

confidence: 99%

“…The "figure of eight" structure of the response can be seen from the corresponding plots. Furthermore, the orientation of a loop can be observed following Pei et al 31 For dashpots, r versus x rotates clockwise-it is symmetrical for a linear dashpot, while anti-symmetrical for a F I G U R E 3 Three particular mem-inerters (following subscripts 1, 2, and 3) with each subject to the prescribed sinusoidal displacement input mem-dashpot. Similarly for springs, r versus _…”

Section: Inerter Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Modeling a helical fluid inerter system with time‐invariant mem‐models

Wagg

Pei

2020

Struct Control Health Monit

View full text Add to dashboard Cite

Summary In this paper, experimental data from tests of a helical fluid inerter are used to model the observed hysteretic behavior. The novel idea is to test the feasibility of employing mem‐models, which are time‐invariant herein, to capture the observed phenomena by using physically meaningful state variables. First, we use a Masing model concept, identified with a multilayer feedforward neural network to capture the physical characteristics of the hysteresis functions. Following this, a more refined problem formulation based on the concept of a multi‐element model including a mem‐inerter is developed. This is compared with previous definitions in the literature and shown to be a more general model. Throughout this paper, numerical simulations are used to demonstrate the type of dynamic responses anticipated using the proposed time‐invariant mem‐models. Corresponding experimental measurements are processed to demonstrate and justify the new mem‐modeling concepts. Focusing on identifying the unknown function forms in the proposed problem formulations, the results show that it is possible to formulate a unified model constructed using both the damper and inerter from the mem‐model family. This model captures many of the more subtle features of the underlying physics, not captured by other forms of existing model.

show abstract

Section: Definitions For Mem-modelsmentioning

confidence: 99%

Section: Definitions For Mem-modelsmentioning

confidence: 99%

Section: Definitions For Mem-modelsmentioning

confidence: 99%

Section: Inerter Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Modeling a helical fluid inerter system with time‐invariant mem‐models

Wagg

Pei

2020

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…As a result, the changes of magnetic flux can adjust the spatial distribution of membrane potentials of cells, here; a memristor 40 is used to realize the coupling and modulation on membrane potential from magnetic flux so that the consistency of physical dimension (or unit) can be kept. Memristor is a new electrical device composed of complex nonlinearity, and it is often used for nonlinear circuits 41–43 with memory effect because the memductance is dependent on the external forcing current, the nonlinear memductance function 44,45 for memristor is described bywhere q ( φ ) is the memristor constitutive relation, the parameters α, β are often selected by appropriate values such as α = 0.2, β = 0.3, so that the memristor-coupled nonlinear circuit can generate chaotic state. When the effect of electromagnetic induction is considered, the dynamical equations for the three-variable Fitzhugh-Nagumo model are described by …”

Section: Model Descriptionsmentioning

confidence: 99%

Model of electrical activity in cardiac tissue under electromagnetic induction

Wang

et al. 2016

Sci Rep

138

View full text Add to dashboard Cite

Complex electrical activities in cardiac tissue can set up time-varying electromagnetic field. Magnetic flux is introduced into the Fitzhugh-Nagumo model to describe the effect of electromagnetic induction, and then memristor is used to realize the feedback of magnetic flux on the membrane potential in cardiac tissue. It is found that a spiral wave can be triggered and developed by setting specific initials in the media, that is to say, the media still support the survival of standing spiral waves under electromagnetic induction. Furthermore, electromagnetic radiation is considered on this model as external stimuli, it is found that spiral waves encounter breakup and turbulent electrical activities are observed, and it can give guidance to understand the occurrence of sudden heart disorder subjected to heavily electromagnetic radiation.Accompanied by rhythmical relaxation of heart, complex electrophysiological activities 1-9 can be detected in cardiac tissue. Spatial pattern 10-14 can be reproduced and observed by collecting the sampled membrane potentials in different areas of cardiac tissue, and many theoretical models [15][16][17][18][19][20][21] have been proposed to investigate the emergence, phase transition and selection of these spatial patterns. It is believed that local heart ischemia can generate "defects" which can block the propagation of target wave emitted from the sinoatrial node 22 ; as a result, self-sustained spiral wave can be induced to block the normal wave propagation. Furthermore, the instability and breakup of spiral wave [23][24][25] in cardiac tissue and can cause possible heart disease. Therefore, many schemes [26][27][28][29][30][31] have been proposed to remove and suppress the spiral wave in cardiac tissue and chemical media. As mentioned in ref. 29, the external forcing can change the excitability of the media thus the spiral wave can be suppressed in a possible way. On the other hand, external field has also been confirmed to be effective in suppressing spiral wave and turbulence, and the possible mechanism can be associated with depolarization effect. The authors in refs 30 and 31 proposed a scheme of phase compression to suppress the spiral wave in excitable and oscillatory media, particularly, the control mechanism is confirmed as a class of intermittent feedback scheme. Indeed, it is more difficult to suppress and control the pinned spiral wave than the meandering spiral wave because these pinned spiral waves are often attracted to a local area such as heterogeneity. As a result, Zhang and Chen et al. [32][33][34][35] proposed some effective schemes to suppress the pinned spiral wave, and the tip dynamics of spiral wave 36 has also been discussed.Indeed, these cardiac tissue models used to emphasize the effect of ion currents across trans-membrane and the membrane potential is calculated, while the effect of electromagnetic induction is left out. As mentioned in refs 37 and 38, complex electrophysiological activities can induce time-varying electromagnetic field and thus the ...

show abstract

Thickness‐Related Analog Switching in SiO_x/Cu/SiO_x Memristive Devices for Neuromorphic Applications

Lamprecht,

Vialetto,

Gergs

et al. 2024

Adv Eng Mater

View full text Add to dashboard Cite

This study examines the development of TiN/SiOx/Cu/SiOx/TiN memristive devices for neuromorphic applications using wedge‐type deposition and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial‐and‐error methods, which often obscure the effects of varying layer compositions. By employing an off‐center thermal evaporation method, a thickness gradient of SiOx and Cu on a 4 inch wafer is created, facilitating detailed resistance map analysis through semiautomatic measurements. This approach allows for investigating the influence of layer composition and thickness while keeping other process conditions constant. Combining experimental data with simulations provides a precise understanding of layer thickness distribution and its impact on device performance. Optimizing the SiOx layers to be below 12 nm, coupled with a discontinuous Cu layer with a nominal thickness under 0.6 nm, exhibits analog switching properties with an Ron/Roff ratio of >100, suitable for neuromorphic applications, while R × A and power exponent γ analysis show signs of multiple conduction mechanisms. The findings highlight the importance of SiOx and Cu thickness in determining switching behavior, offering insights for developing high‐performance analog switching components for bioinspired computing systems.

show abstract

Understanding memristors and memcapacitors in engineering mechanics applications

Cited by 35 publications

References 36 publications

Modeling a helical fluid inerter system with time‐invariant mem‐models

Modeling a helical fluid inerter system with time‐invariant mem‐models

Model of electrical activity in cardiac tissue under electromagnetic induction

Thickness‐Related Analog Switching in SiO_x/Cu/SiO_x Memristive Devices for Neuromorphic Applications

Contact Info

Product

Resources

About

Understanding memristors and memcapacitors in engineering mechanics applications

Cited by 35 publications

References 36 publications

Modeling a helical fluid inerter system with time‐invariant mem‐models

Modeling a helical fluid inerter system with time‐invariant mem‐models

Model of electrical activity in cardiac tissue under electromagnetic induction

Thickness‐Related Analog Switching in SiOx/Cu/SiOx Memristive Devices for Neuromorphic Applications

Contact Info

Product

Resources

About

Thickness‐Related Analog Switching in SiO_x/Cu/SiO_x Memristive Devices for Neuromorphic Applications