Noise on resistive switching: a Fokker–Planck approach

Patterson, G. A.; Grosz, D. F.; Fierens, Pablo Ignacio

doi:10.1088/1742-5468/2016/05/054043

Cited by 7 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method of stochastic differential equations [5] is the standard way to take account for fluctuations in otherwise deterministic models. Some applications of this method to the problem of stochasticity in ReRAM cells have been reported [6], [7], [8], [9] including the postulation of stochastic memory elements by YVP and Di Ventra in 2011 [10]. However, the method of stochastic differential equations has yet to be adopted widely in the ReRAM community, possibly because of its relative complexity.…”

Section: Introductionmentioning

confidence: 99%

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

Dowling,

Slipko,

Pershin

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Dowling,

Slipko,

Pershin

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The relevant supercurrent differences ( ) are then calculated between the averaged diffraction patterns. Recently, the effects of the noise on the performance of several memory devices has been investigated 15 18 39 40 41 42 43 44 45 .…”

Section: Resultsmentioning

confidence: 99%

Solitonic Josephson-based meminductive systems

Guarcello

Solinas

Ventra

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Memristors, memcapacitors, and meminductors represent an innovative generation of circuit elements whose properties depend on the state and history of the system. The hysteretic behavior of one of their constituent variables, is their distinctive fingerprint. This feature endows them with the ability to store and process information on the same physical location, a property that is expected to benefit many applications ranging from unconventional computing to adaptive electronics to robotics. Therefore, it is important to find appropriate memory elements that combine a wide range of memory states, long memory retention times, and protection against unavoidable noise. Although several physical systems belong to the general class of memelements, few of them combine these important physical features in a single component. Here, we demonstrate theoretically a superconducting memory based on solitonic long Josephson junctions. Moreover, since solitons are at the core of its operation, this system provides an intrinsic topological protection against external perturbations. We show that the Josephson critical current behaves hysteretically as an external magnetic field is properly swept. Accordingly, long Josephson junctions can be used as multi-state memories, with a controllable number of available states, and in other emerging areas such as memcomputing, i.e., computing directly in/by the memory.

show abstract

“…T HE correlation of the memristor, the theorised fourth passive circuit element [1], with resistive memories as realised by Strukov et al [2], has received remarkable attention due to the broad range of potential applications that have been put forward some of which include, but are not limited to, nonvolatile multi-bit memories [3], neuromorphic systems [4] and reconfigurable circuits [5]. Resistive memories, however, are not immune to the effects of noise [6] which is present in all physical systems whether it is internal or external to them [7]. In certain cases [8] memristive response can be enhanced by the presence of noise.…”

Section: Introductionmentioning

confidence: 99%

A Memristive Switching Uncertainty Model

Stathopoulos

Serb

Khiat

et al. 2019

IEEE Trans. Electron Devices

View full text Add to dashboard Cite

In this paper we endeavour to evaluate and model switching noise in resistive random access memory devices (RRAM). Although noise is always present in physical systems, the sources of which can be attributed to many different effects, in this study we are focusing our attention on a specific typeswitching noise. Using alternating pulse programming and read trains across different voltages we acquire a large dataset below and above the switching threshold and construct what we define as increment plots, ∆R vs. R. Then, through a detailed statistical analysis, we quantify the localised uncertainty among consecutive points using a sliding window of up to N points accounting for any statistical artefacts that arise. By separating the data accumulated from programming and read-out and analysing them individually we can subtract a baseline noise floor from the overall switching uncertainty. In this way we effectively decouple it from other noise sources that affect the device at rest. In the end an F (R, V ) surface can be extracted that closely follows the behaviour of uncertainty of the device during programming. This modelled surface can be used as an approximation of the noise behaviour of the device or it can be readily incorporated as an additional component to existing switching models.

show abstract

Noise on resistive switching: a Fokker–Planck approach

Cited by 7 publications

References 15 publications

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

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

Solitonic Josephson-based meminductive systems

A Memristive Switching Uncertainty Model

Contact Info

Product

Resources

About