Reliable Modeling of Ideal Generic Memristors via State-Space Transformation

Biolek, Dalibor; Biolková, Viera; Biolek, Zdeněk; Kolka, Zdenek

doi:10.13164/re.2015.0393

Cited by 41 publications

(42 citation statements)

References 52 publications

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“…The one-to-one correspondence (6) between the window function f w and the S-function f s is demonstrated in [37] for the well-known Joglekar and Prodromakis windows. An analytic form of the S-function with the tuning parameter a > 10 [37] …”

Section: Modified S Modelmentioning

confidence: 96%

“…Increased robustness can be achieved by substituting the window approach by a nonlinear transformation between generalized charge or flux and the physical state variable of the memristor [37]. Consider a voltage-controlled extended memristor of the first-order with port Equation (1).…”

Section: Modified S Modelmentioning

confidence: 99%

“…To illustrate this approach, consider the classical current-controlled HP model [37] with the memristance linearly dependent on the state…”

Section: Modified S Modelmentioning

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 S Modelmentioning

confidence: 96%

Section: Modified S Modelmentioning

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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show abstract

“…which can be used for a quick transition from one form of equation (6) to the other. In practice, we use an option that better matches the given PSM.…”

Section: General Form Of Differential Equation Of Ideal Memristormentioning

confidence: 99%

“…A specific type of equation (3) may lead to a structure of the state-based model which can exhibit better properties than the state space model based on a physical variable which is directly connected with the memory mechanism [5], [6]. Finding the type of DEM can also answer the question whether the memristor response to a known excitation can be computed analytically.…”

Section: Introductionmentioning

confidence: 99%

Differential Equations of Ideal Memristors

Biolek¹,

Biolek²,

Biolková

2015

RADIOENGINEERING

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Theory and Technology of Memristive Devices

Tetzlaff

Ascoli

Wouters

2022

Wiley Encyclopedia of Electrical and Electronics Engineering

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CMOS scaling is progressively and inevitably approaching atomic boundaries. The industry is thus exploring the potential of novel disruptive nanotechnologies, based on multifunctional materials, which may allow the development of integrated circuits keeping the trend predicted by Moore in 1965 in the years to come, despite further transistor shrinking being no longer possible. Memristors, originally postulated only theoretically out of a brilliant line of thought by Chua in 1971 and identified at the nanoscale almost 40 years later, back in 2008, by a team of engineers supervised by R.S. Williams at Hewlett Packard Labs, represent the key nanotechnology of the future, enabling novel forms of computation, which are closer to the principles underlying the functionalities of the human brain, given the extraordinarily peculiar capability of these devices to sense, store, and process data in the same physical miniaturized volume. Achieving progress in memristor circuit design crucially calls for the development of synergies among researchers with different academic backgrounds. In fact, only combining the technical knowledge, acquired by memristor theoreticians, with the practical know‐how of experimenters will enable to foster progress in memristor circuit and system development in the near future. In line with this necessity, the present article is the fruit of a joint cooperation between two leading memristor research units from Germany, one based at TU Dresden, which has contributed to establish solid theoretical foundations on resistance switching memories and their circuits since 2008, and the other one based at RWTH Aachen University, which has been the leading experimental research unit on nanostructures of this kind over the same time span. This article first presents a comprehensive overview of the theory of memristors, starting off from the early achievements in the pioneering studies of their father Chua, then classifies and analyses in depth the main physical mechanisms underlying the inherently nonlinear behavior of their physical realizations, and, finally, discusses their most promising applications, from the development of new nonvolatile memories based on resistance switching phenomena to the realization of novel neuromorphic circuits, capable to mimic the complex dynamics of biological entities closer than conventional purely CMOS hardware, without leaving aside an excursus on the potential to leverage the unique features of these devices for the circuit implementation of unconventional brain‐inspired time‐ and energy‐efficient sensing and memcomputing strategies, which is a great appeal for the realization of portable light‐weight technical systems for the Internet‐of‐Things industry, especially but not only to the benefit of healthcare, well‐being, and automotive market sectors.

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Reliable Modeling of Ideal Generic Memristors via State-Space Transformation

Cited by 41 publications

References 52 publications

Modeling and simulation of large memristive networks

Modeling and simulation of large memristive networks

Differential Equations of Ideal Memristors

Theory and Technology of Memristive Devices

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