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dc.contributor.advisorÖzbay, Ekmel
dc.contributor.authorDemirağ, Yiğit
dc.date.accessioned2018-07-30T11:27:21Z
dc.date.available2018-07-30T11:27:21Z
dc.date.copyright2018-07
dc.date.issued2018-07
dc.date.submitted2018-07
dc.identifier.urihttp://hdl.handle.net/11693/47694
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 65-76).en_US
dc.description.abstractModeling nanoscale devices that emulate the functionality of synapses of the biological brain is a fundamental operation for developing brain-inspired computational systems. Phase-change material based synaptic devices offer promising performance in speed, spatial and power efficiency metrics, up to human brain level, when connected in a massively parallel crossbar array architecture. In this work, we modeled electrothermal characteristics of a single synaptic device consisting of phase-change material based memory and its selector. First, we proposed a finite element method based simulation framework for modeling electrical, thermal and probabilistic crystallization dynamics of the memory unit. Gradual phase transitions that form device memory between amorphous and crystalline states are studied under nanosecond voltage pulses. Second, we implemented time and temperature dependent resistance drift saturation model for phase-change material based selector device. Our model is in close agreement with the ultrafast saturation phenomena which is observed for the first time in fabricated devices with 8 nm node technology.en_US
dc.description.statementofresponsibilityby Yiğit Demirağ.en_US
dc.format.extentxiii, 76 leaves : illustrations (some color) ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPhase Change Memoryen_US
dc.subjectDevice Modelingen_US
dc.subjectSynaptic Deviceen_US
dc.subjectNeuromorphic Computingen_US
dc.subjectGSTen_US
dc.subjectMultiphysicsen_US
dc.titleMultiphysics modeling of Ge2Sb2Te5 based synaptic devices for brain inspired computingen_US
dc.title.alternativeBeyinden esinlenen hesaplamalar için Ge2Sb2Te5 tabanlı sinaptik cihazların multifizik modellenmesien_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158641
dc.embargo.release2021-07-10


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