Structural and Parametric Identification of Knowm Memristors.

Valerii Ostrovskii,Petr Fedoseev,Denis Butusov,Yulia Bobrova

doi:10.3390/nano12010063

Valerii Ostrovskii, Petr Fedoseev + Show 2 more

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https://doi.org/10.3390/nano12010063

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Abstract

This paper proposes a novel identification method for memristive devices using Knowm memristors as an example. The suggested identification method is presented as a generalized process for a wide range of memristive elements. An experimental setup was created to obtain a set of intrinsic I–V curves for Knowm memristors. Using the acquired measurements data and proposed identification technique, we developed a new mathematical model that considers low-current effects and cycle-to-cycle variability. The process of parametric identification for the proposed model is described. The obtained memristor model represents the switching threshold as a function of the state variables vector, making it possible to account for snapforward or snapback effects, frequency properties, and switching variability. Several tools for the visual presentation of the identification results are considered, and some limitations of the proposed model are discussed.

Highlights

The fourth fundamental two-terminal passive circuit element, the memristor, was postulated by L.O
These processes are preceded by choice of criteria and the initial model, as well as the process of parametric identification to determine the characteristics of the model under conditions approximate to the experiment
We presented a new memristor identification technique in the form of a research-based design process

Summary

Introduction

The fourth fundamental two-terminal passive circuit element, the memristor, was postulated by L.O. Chua in 1971 based on the principle of symmetry between electrical quantities [1]. Promising applications of memristive devices include non-volatile memory [4], logic circuits [5], sensing [6], cryptography [7], chaotic generators [8], and neuromorphic computing [9]. Development of the latter direction is performed from the standpoint of using memristors as synaptic connections in artificial neural networks [10], mimicking biological architectures in the nervous systems. The most recent progress in the study of memristors in bio-inspired circuits was made in [11,12]

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