On the Switching Parameter Variation of Metal-Oxide RRAM—Part I: Physical Modeling and Simulation Methodology

Guan, Xialing; Yu, Shimeng; Wong, H.-S. Philip

doi:10.1109/ted.2012.2184545

Cited by 321 publications

(207 citation statements)

References 31 publications

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“…where e R 0 tunnel is the tunnel coupling strength between a trap and an electrode, N L,R is the number of states at a given energy in an electrode and E v þ /E v • are the energy of an empty/ filled trap given by Guan et al [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

“…T n L,R,þ• is the tunnel probability from the left/right electrode into a trap given by the WentzelKramers-Brilloin approximation [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

“…On the other hand, during the equilibrium state (absence of F eq ), the oxygen vacancy recombination rate is given by Guan et al [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

“…where β is a parameter related to concentration of oxygen ions which can be computed by the following approximation [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

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Physical Models for Resistive Switching Devices

Procel‐Moya¹

2018

Memristor and Memristive Neural Networks

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We present a classification and description of the principal resistive switching and transport mechanisms in chalcogonides materials. We classify the model according to how many material dimensions are involved in the resistive switching mechanism. In this way, we describe the phase change model (3D), the interface modulation model (2D) and models where the switching mechanism depends on the formation of a conduction filament (1D). Among the conduction filament models, we include the thermochemical oxygen diffusion mechanism, the oxidation/reduction mechanism and the quantum point effect.

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Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

“…T n L,R,þ• is the tunnel probability from the left/right electrode into a trap given by the WentzelKramers-Brilloin approximation [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

“…On the other hand, during the equilibrium state (absence of F eq ), the oxygen vacancy recombination rate is given by Guan et al [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

“…where β is a parameter related to concentration of oxygen ions which can be computed by the following approximation [6]:…”

Section: Trap-assisted-tunnelling Modelmentioning

confidence: 99%

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Physical Models for Resistive Switching Devices

Procel‐Moya¹

2018

Memristor and Memristive Neural Networks

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“…The unique properties of the memristor can be beneficial for many applications, such as threshold switch [3], dynamic load [4] and memory, which is considered the primary application for these devices. Resistive random-access memory [5][6][7][8][9][10][11] is one interesting type of memristive device [12]. Unlike standard nonvolatile memory cells such as Flash, resistive random-access memory can be smaller, vertically stacked (three-dimensional crossbar array structures [13,14]), with lower energy and lower read/write latency [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the row grounding technique in a memristor‐based crossbar array

Dozortsev

Goldshtein

Kvatinsky

2017

Circuit Theory & Apps

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SUMMARYUsing memristive devices within a crossbar array could pave the way for memories with higher density and speed than state-of-the-art Flash memory, while maintaining relatively low energy. However, memristive crossbar arrays have great difficulty distinguishing logical states because of sneak path currents. The row grounding technique eliminates the sneak path effect, allowing reliable sampling of the memristor state.In this paper, we analyze the row grounding technique and propose several methods and constraints for the design of memristive crossbar arrays. When the row grounding technique is used for these arrays, our analysis shows that increasing the number of rows can help reduce read latency and energy, in contrast to the case of capacitive memory arrays. Simulation results confirm the theoretical analysis proposed in this paper.

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Resistive‐Switching Memory

Ielmini

2014

Wiley Encyclopedia of Electrical and Electronics Engineering

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Resistive‐switching random‐access memory (RRAM) based on ion migration in insulating layers might revolutionize future nanoelectronic circuits for both memory and logic. The high‐speed, low‐power change of resistance in RRAM can be exploited not only for data storage but also for reconfiguration of programmable logic circuits and for neuromorphic computation. In this review, the strengths and limitations of RRAM will be discussed. First, the RRAM concept will be reviewed in terms of memory operation and physical mechanisms for switching. The RRAM architectures will be discussed with emphasis on the crossbar array structure allowing for high density and reliable operation through proper selector devices. Finally, the RRAM applications in computing systems will be reviewed, addressing the RRAM switch in field‐programmable gate array (FPGA) and neuromorphic circuits.

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On the Switching Parameter Variation of Metal-Oxide RRAM—Part I: Physical Modeling and Simulation Methodology

Cited by 321 publications

References 31 publications

Physical Models for Resistive Switching Devices

Physical Models for Resistive Switching Devices

Analysis of the row grounding technique in a memristor‐based crossbar array

Resistive‐Switching Memory

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