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Nano Converg. 2016;3(1):16. doi: 10.1186/s40580-016-0076-8. Epub 2016 Jul 18.

TiO.

Nano convergence

T D Dongale, P J Patil, N K Desai, P P Chougule, S M Kumbhar, P P Waifalkar, P B Patil, R S Vhatkar, M V Takale, P K Gaikwad, R K Kamat

Affiliations

  1. Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004 India.
  2. Rajarambapu Institute of Technology, Sakharale, 415414 India.
  3. Department of Physics, Shivaji University, Kolhapur, 416004 India.
  4. Embedded System and VLSI Research Laboratory, Department of Electronics, Shivaji University, Kolhapur, 416004 India.

PMID: 28191426 PMCID: PMC5271148 DOI: 10.1186/s40580-016-0076-8

Abstract

We report simulation of nanostructured memristor device using piecewise linear and nonlinear window functions for RRAM and neuromorphic applications. The linear drift model of memristor has been exploited for the simulation purpose with the linear and non-linear window function as the mathematical and scripting basis. The results evidences that the piecewise linear window function can aptly simulate the memristor characteristics pertaining to RRAM application. However, the nonlinear window function could exhibit the nonlinear phenomenon in simulation only at the lower magnitude of control parameter. This has motivated us to propose a new nonlinear window function for emulating the simulation model of the memristor. Interestingly, the proposed window function is scalable up to

Keywords: Memristor; Neuromorphic applications; RRAM; Window function

References

  1. Nature. 2008 May 1;453(7191):80-3 - PubMed
  2. Nat Nanotechnol. 2008 Jul;3(7):429-33 - PubMed

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