Improved Model of TiO2 Memristor

Biolek, Dalibor; Biolková, Viera; Kolka, Zdenek

doi:10.13164/re.2015.0378

Cited by 91 publications

(121 citation statements)

References 14 publications

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“…corresponds to a series connection of a resistor R s and a nonlinear tunneling barrier represented by Simmons' model i T (v t ,w) [26], where v t is the voltage across the barrier and w is the barrier width. The model was proposed as symmetrical, giving the same current for both positive and negative v M .…”

Section: Modified Pickett Modelmentioning

confidence: 99%

“…The originally proposed SPICE implementation [38] has a number of limitations. In [26], we have identified that the port Equation (12) may have a multiple or no solution, depending on the applied voltage v M . During the time-domain simulation, the solver may evaluate the model equations far from the typical operating voltages or currents.…”

Section: Modified Pickett Modelmentioning

confidence: 99%

“…It turns out that strongly nonlinear memristor models are often a source of difficult-to-solve numerical problems associated with the simulation of large memristive circuits [26]. The problem consists not only in the large number of internal equations of the model but also in their mathematical form: The endeavor to model the extremely nonlinear memristor dynamics as a function of the signal level raises the model propensity for data overflow/underflow and nonconvergence issues.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and simulation of large memristive networks

Biolek

Kolka

Biolková

et al. 2017

Circuit Theory & Apps

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SUMMARYThe paper deals with the modeling of memristors operating in extremely large memristive networks such as crossbar structures for memory and computational circuits, memristor-based neural networks or circuits for massively parallel analog computations. Because the non-convergence and other numerical problems increase with increasing complexity of the simulated circuit, suitable models of the individual memristors need to be choicely developed and optimized. Three different models are considered, each representing a specific trade-off between speed and accuracy. Benchmark circuits for testing the applications of various complexities are used for the transient analysis in HSPICE. It is shown how the models can be modified to minimize the simulation time and improve the convergence.

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Section: Modified Pickett Modelmentioning

confidence: 99%

Section: Modified Pickett Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and simulation of large memristive networks

Biolek

Kolka

Biolková

et al. 2017

Circuit Theory & Apps

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“…This model indicates that, in addition to pragmatic reasons, there are also technical arguments for considering the nonlinear memristance (memductance)-to-state variable relationship. An approximation of the I-V characteristics from the original paper [25] is suggested in [28], indicating that the memductance depends exponentially on the state variable. Such type of the nonlinearity is also obvious from the paper [29].…”

Section: Introductionmentioning

confidence: 99%

“…As follows from the Pickett model from [25], the v-i characteristic is linear only for voltages v in the immediate vicinity of the origin, namely, for |v| ≪ φ I /e, where φ I is the so-called mean barrier height, and e is the electron charge [28]. It can be anticipated that this linear region can be broadened in order to fabricate the memristive form of the classical potentiometer with nonlinear regulation of its resistance.…”

Section: Introductionmentioning

confidence: 99%

Analysis of memristors with nonlinear memristance versus state maps

Biolek

Biolková

et al. 2017

Circuit Theory & Apps

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Summary According to the axiomatic definition of the memristor from 1971, its properties are unambiguously determined by the memristance versus charge (or flux) map. The original model of the ‘HP memristor’ introduces this map via a linear function that represents this memristor as a variable resistor whose resistance is linearly dependent on the amount of charge flowing through. However, some analog applications require nonlinear, frequently exponential or logarithmic dependence of the resistance on an external controlling variable. The memristor with nonlinear memristance versus charge map is analyzed in the paper. The results are specified for the exponential type of this nonlinearity, which may be useful for future applications. Analytic formulae of the area of the pinched hysteresis loop of such a memristor are derived for harmonic excitation. It is also shown that the current flowing through such a memristor, which is driven by a voltage of arbitrary waveform, conforms to the Abel differential equation, and its closed‐form solution is found. Copyright © 2017 John Wiley & Sons, Ltd.

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A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision

Wu,

Deng,

et al. 2024

Advanced Materials

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Intelligent vision necessitates the deployment of detectors that are always‐on and low‐power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this goal by the continuous processing of massive image frames and executing intricate algorithms, thereby expending substantial computational power and energy. In contrast, biological data processing, based on event‐triggered spiking, has higher efficiency and lower energy consumption. Here, we propose an artificial vision architecture consisting of spiking photodetectors and artificial synapses, closely mirroring the intricacies of the human visual system. Distinct from previously reported techniques, the photodetector is self‐powered and event‐triggered, outputting light‐modulated spiking signals directly, thereby fulfilling the imperative for always‐on with low‐power consumption. With the spiking signals processing through the integrated synapse units, recognition of graphics, gestures, and human action has been implemented, illustrating the potent image processing capabilities inherent within this architecture. The results prove the 90% accuracy rate in human action recognition within a mere 5 epochs utilizing a rudimentary artificial neural network. This novel architecture, grounded in spiking photodetectors, offers a viable alternative to the extant models of always‐on low‐power artificial vision system.This article is protected by copyright. All rights reserved

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Improved Model of TiO2 Memristor

Cited by 91 publications

References 14 publications

Modeling and simulation of large memristive networks

Modeling and simulation of large memristive networks

Analysis of memristors with nonlinear memristance versus state maps

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision

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