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Article

Structural and Parametric Identification of Knowm Memristors

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Department of Computer-Aided Design, St. Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
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Department of Biomedical Engineering, St. Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
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Youth Research Institute, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
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Authors to whom correspondence should be addressed.
Academic Editor: Xianfu Wang
Nanomaterials 2022, 12(1), 63; https://doi.org/10.3390/nano12010063
Received: 29 November 2021 / Revised: 23 December 2021 / Accepted: 25 December 2021 / Published: 27 December 2021
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. View Full-Text
Keywords: memristor; identification; voltage-current curve; memristive device; nonlinear component memristor; identification; voltage-current curve; memristive device; nonlinear component
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MDPI and ACS Style

Ostrovskii, V.; Fedoseev, P.; Bobrova, Y.; Butusov, D. Structural and Parametric Identification of Knowm Memristors. Nanomaterials 2022, 12, 63. https://doi.org/10.3390/nano12010063

AMA Style

Ostrovskii V, Fedoseev P, Bobrova Y, Butusov D. Structural and Parametric Identification of Knowm Memristors. Nanomaterials. 2022; 12(1):63. https://doi.org/10.3390/nano12010063

Chicago/Turabian Style

Ostrovskii, Valerii, Petr Fedoseev, Yulia Bobrova, and Denis Butusov. 2022. "Structural and Parametric Identification of Knowm Memristors" Nanomaterials 12, no. 1: 63. https://doi.org/10.3390/nano12010063

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