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Non-Uniform Spline Quasi-Interpolation to Extract the Series Resistance in Resistive Switching Memristors for Compact Modeling Purposes

Author

Listed:
  • María José Ibáñez

    (Department of Applied Mathematics, University of Granada, 18071 Granada, Spain)

  • Domingo Barrera

    (Department of Applied Mathematics, University of Granada, 18071 Granada, Spain)

  • David Maldonado

    (Department of Electronics and Computer Technology, University of Granada, 18071 Granada, Spain)

  • Rafael Yáñez

    (Department of Applied Mathematics, University of Granada, 18071 Granada, Spain)

  • Juan Bautista Roldán

    (Department of Electronics and Computer Technology, University of Granada, 18071 Granada, Spain)

Abstract

An advanced new methodology is presented to improve parameter extraction in resistive memories. The series resistance and some other parameters in resistive memories are obtained, making use of a two-stage algorithm, where the second one is based on quasi-interpolation on non-uniform partitions. The use of this latter advanced mathematical technique provides a numerically robust procedure, and in this manuscript, we focus on it. The series resistance, an essential parameter to characterize the circuit operation of resistive memories, is extracted from experimental curves measured in devices based on hafnium oxide as their dielectric layer. The experimental curves are highly non-linear, due to the underlying physics controlling the device operation, so that a stable numerical procedure is needed. The results also allow promising expectations in the massive extraction of new parameters that can help in the characterization of the electrical device behavior.

Suggested Citation

  • María José Ibáñez & Domingo Barrera & David Maldonado & Rafael Yáñez & Juan Bautista Roldán, 2021. "Non-Uniform Spline Quasi-Interpolation to Extract the Series Resistance in Resistive Switching Memristors for Compact Modeling Purposes," Mathematics, MDPI, vol. 9(17), pages 1-12, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2159-:d:628989
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    References listed on IDEAS

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    1. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
    2. Fabien Alibart & Elham Zamanidoost & Dmitri B. Strukov, 2013. "Pattern classification by memristive crossbar circuits using ex situ and in situ training," Nature Communications, Nature, vol. 4(1), pages 1-7, October.
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