Modeling and Simulation of Resistive Random Access Memory With Graphene Electrode

Xie, Hao; Chen, Wenchao; Zhang, Shuo; Zhu, Guodong; Khaliq, Afshan; Hu, Jun; Wang, Yin

doi:10.1109/ted.2020.2965182

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…In this section, the RRAM mechanisms of different GE-RRAMs are firstly introduced. Then, multiphysic and compact models of GE-RRAM are further described [32,33,48,49].…”

Section: Graphene Rrammentioning

confidence: 99%

“…Besides, the integration of RRAM is highly increased because the thickness of monolayer graphene is only 0.3 nm. Our research group studied the mechanism of GE-RRAM and established its multi-physical simulation model [32]. Different from conventional Pt-RRAM, the oxygen ions can migrate horizontally into graphene electrode rather than accumulating near the electrode during the set process.…”

Section: Graphene Edge Electrodementioning

confidence: 99%

“…Our research group developed the electrical conductivity model of graphene electrode with different degrees of oxidation [32]. Further, the in-house developed finite difference algorithm is used to solve the current transport, oxygen vacancy migration, and heat conduction equations to simulate the GE-RRAM cell.…”

Section: Multi-physic Modelmentioning

confidence: 99%

“…where V is the volume of conductive filament, S the area of graphene oxide, n(⃗ r, t) the oxygen vacancy density of HfO x , and o(⃗ r, t) the oxygen atoms density of graphene oxide. The electrical conductivity of graphene oxide is highly dependent on its degree of oxidation [32]. An electrical conductivity model of graphene oxide, in which the conductivity is exponentially dependent on the density of oxygen atoms in the partially oxidized graphene oxide, is proposed as follows,…”

Section: Multi-physic Modelmentioning

confidence: 99%

“…In 2016, Lee et al demonstrated that the movement of oxygen ions can be controlled by modulating the opening of graphene nanopores [31]. In 2020, our research group developed the electrical conductivity model of graphene electrode and further built multi-physical model by solving current transport equation, oxygen vacancy migration equation, and heat conduction equation [32]. In 2020, Alimkhanuly et al built a compact model of GE-RRAM to fit experimental I-V characteristics and study electric potential distribution [33].…”

Section: Introductionmentioning

confidence: 99%

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Mechanisms and Modeling of 2d-Materials-Based Resistive Random Access Memory Devices (Invited Review)

Xie¹,

Wang²,

Yang³

et al. 2021

PIER

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Resistive random access memory (RRAM) devices are promising candidates for next generation high capacity data storage due to their superior properties such as cost-effective fabrication, high operating speed, low power consumption, and long data retention. Particularly, the two dimensional (2D)-materials-based RRAM has attracted researchers' attention because of its unique physical and chemical properties without the constraint of lattice matching. In this review, the switching mechanisms and modeling of RRAM devices based on the 2D materials such as hexagonal-boron nitride (h-BN) and graphene are discussed. Firstly, the monolayer and multilayer h-BN RRAMs are introduced, and their mechanisms and compact model are further described. Then, the mechanisms of graphene electrodebased RRAM (GE-RRAM) for different applications are also introduced and compared. Furthermore, the electrical conductivity, multi-physic and compact models of GE-RRAM are introduced. This review paper provides the guidance for the design and optimization of the 2D-materials-based RRAM in the next generation memories.

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“…In this section, the RRAM mechanisms of different GE-RRAMs are firstly introduced. Then, multiphysic and compact models of GE-RRAM are further described [32,33,48,49].…”

Section: Graphene Rrammentioning

confidence: 99%

Section: Graphene Edge Electrodementioning

confidence: 99%

Section: Multi-physic Modelmentioning

confidence: 99%

Section: Multi-physic Modelmentioning

confidence: 99%