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    Stochasticity Modeling in Memristors

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    07305797.pdf
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    7.310Mb
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Naous, Rawan cc
    Al-Shedivat, Maruan cc
    Salama, Khaled N. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2015-10-26
    Online Publication Date
    2015-10-26
    Print Publication Date
    2016-01
    Permanent link to this record
    http://hdl.handle.net/10754/581778
    
    Metadata
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    Abstract
    Diverse models have been proposed over the past years to explain the exhibiting behavior of memristors, the fourth fundamental circuit element. The models varied in complexity ranging from a description of physical mechanisms to a more generalized mathematical modeling. Nonetheless, stochasticity, a widespread observed phenomenon, has been immensely overlooked from the modeling perspective. This inherent variability within the operation of the memristor is a vital feature for the integration of this nonlinear device into the stochastic electronics realm of study. In this paper, experimentally observed innate stochasticity is modeled in a circuit compatible format. The model proposed is generic and could be incorporated into variants of threshold-based memristor models in which apparent variations in the output hysteresis convey the switching threshold shift. Further application as a noise injection alternative paves the way for novel approaches in the fields of neuromorphic engineering circuits design. On the other hand, extra caution needs to be paid to variability intolerant digital designs based on non-deterministic memristor logic.
    Citation
    Stochasticity Modeling in Memristors 2015:1 IEEE Transactions on Nanotechnology
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Nanotechnology
    DOI
    10.1109/TNANO.2015.2493960
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7305797
    ae974a485f413a2113503eed53cd6c53
    10.1109/TNANO.2015.2493960
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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