Stochasticity Modeling in Memristors

Handle URI:
http://hdl.handle.net/10754/581778
Title:
Stochasticity Modeling in Memristors
Authors:
Naous, Rawan ( 0000-0001-6129-7926 ) ; Al-Shedivat, Maruan ( 0000-0001-9037-1005 ) ; Salama, Khaled N. ( 0000-0001-7742-1282 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Stochasticity Modeling in Memristors 2015:1 IEEE Transactions on Nanotechnology
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Nanotechnology
Issue Date:
26-Oct-2015
DOI:
10.1109/TNANO.2015.2493960
Type:
Article
ISSN:
1536-125X; 1941-0085
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7305797
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorNaous, Rawanen
dc.contributor.authorAl-Shedivat, Maruanen
dc.contributor.authorSalama, Khaled N.en
dc.date.accessioned2015-11-05T05:51:42Zen
dc.date.available2015-11-05T05:51:42Zen
dc.date.issued2015-10-26en
dc.identifier.citationStochasticity Modeling in Memristors 2015:1 IEEE Transactions on Nanotechnologyen
dc.identifier.issn1536-125Xen
dc.identifier.issn1941-0085en
dc.identifier.doi10.1109/TNANO.2015.2493960en
dc.identifier.urihttp://hdl.handle.net/10754/581778en
dc.description.abstractDiverse 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.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7305797en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleStochasticity Modeling in Memristorsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Nanotechnologyen
dc.eprint.versionPost-printen
dc.contributor.institutionMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorNaous, Rawanen
kaust.authorSalama, Khaled N.en
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