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Design and modeling of niobium oxide-tantalum oxide based self-selective memristor for large-scale crossbar memory

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  • Parit, Aditya Kuber
  • Yadav, Mani Shankar
  • Gupta, Avinash Kumar
  • Mikhaylov, Alexey
  • Rawat, Brajesh

Abstract

Memristor-based crossbar architecture has emerged as a promising candidate for 3-D memory, logic, and neuromorphic computing system as it offers remarkably high integration density, low power consumption, fast operation, and easy integration with CMOS technology. However, the fundamental obstacle for their development is the sneak current, which causes misreading and write-crosstalk. In this regard, we present the TiN/NbO2/TiN/TaOx/TiN based self-selective memristor by combining the threshold switching properties of niobium oxide (NbO2) and memory switching properties of tantalum oxide (TaOx) in a single device. The performance investigation is carried out using the finite element simulation method, based on self-consistent solutions of joule heating equation, drift-diffusion continuity equation, and current continuity for accurately capturing the temperature and field-dependent transport of vacancies. The results reveal that NbO2-TaOx based self-selective memristor can allow significantly lower OFF current (1.22 μA), higher read window (32.6), and higher non-linearity (141) than that of TiN/TaOx/TiN based memristor. We demonstrate that the self-selective memristor exhibits good speed with the operation time constant of 70 ns. Furthermore, the crossbar array using a self-selective memristor has shown excellent performance with an improved readout margin up to 27 word lines. Our material-to-circuit performance analysis promises a reliable and energy-efficient crossbar array using NbO2-TaOx cell that can be further utilized for implementing 3-D cross-bar array.

Suggested Citation

  • Parit, Aditya Kuber & Yadav, Mani Shankar & Gupta, Avinash Kumar & Mikhaylov, Alexey & Rawat, Brajesh, 2021. "Design and modeling of niobium oxide-tantalum oxide based self-selective memristor for large-scale crossbar memory," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:chsofr:v:145:y:2021:i:c:s0960077921001703
    DOI: 10.1016/j.chaos.2021.110818
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    1. Guseinov, D.V. & Matyushkin, I.V. & Chernyaev, N.V. & Mikhaylov, A.N. & Pershin, Y.V., 2021. "Capacitive effects can make memristors chaotic," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Spagnolo, B. & Valenti, D. & Guarcello, C. & Carollo, A. & Persano Adorno, D. & Spezia, S. & Pizzolato, N. & Di Paola, B., 2015. "Noise-induced effects in nonlinear relaxation of condensed matter systems," Chaos, Solitons & Fractals, Elsevier, vol. 81(PB), pages 412-424.
    3. Irem Boybat & Manuel Le Gallo & S. R. Nandakumar & Timoleon Moraitis & Thomas Parnell & Tomas Tuma & Bipin Rajendran & Yusuf Leblebici & Abu Sebastian & Evangelos Eleftheriou, 2018. "Neuromorphic computing with multi-memristive synapses," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    4. Mikhaylov, A.N. & Guseinov, D.V. & Belov, A.I. & Korolev, D.S. & Shishmakova, V.A. & Koryazhkina, M.N. & Filatov, D.O. & Gorshkov, O.N. & Maldonado, D. & Alonso, F.J. & Roldán, J.B. & Krichigin, A.V. , 2021. "Stochastic resonance in a metal-oxide memristive device," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
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    1. Li, Xing & Zou, Jianxun & Feng, Zhe & Wu, Zuheng & Xu, Zuyu & Yang, Fei & Zhu, Yunlai & Dai, Yuehua, 2023. "Thermal design engineering for improving the variation of memristor threshold," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

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