A Predictive Compact Model of Bipolar RRAM Cells for Circuit Simulations

Chiang, Meng-Hsueh; Hsu, Kai-Hsiang; Ding, Wei-Wen; Yang, Bei-Chang

doi:10.1109/ted.2015.2428293

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…In order to include thermal inertia in the model, a capacitor (in the electrically equivalent thermal circuit) is added, C th , the thermal or heat capacitance. This approach is used in different RRAMs compact models (thermal model 6, TM6), [ 78 , 95 , 96 ]. We can reformulate Equation (17) to the following expression:

…”

Section: Mathematical Description Of Rram Thermal Effectsmentioning

confidence: 99%

On the Thermal Models for Resistive Random Access Memory Circuit Simulation

et al. 2021

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Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

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…”

Section: Mathematical Description Of Rram Thermal Effectsmentioning

confidence: 99%

On the Thermal Models for Resistive Random Access Memory Circuit Simulation

et al. 2021

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“…The red dots are extracted from the 3nm device of [7]. The green line is the simulated result without TB based on our previous study [17]. The current level difference in low bias regime is clearly shown here, illustrating the supressed current from the TB as an embedded selector.…”

Section: Simulation Results and Discussionmentioning

confidence: 70%

“…A compact temperature calculation model helps the evaluation of this thermal run-away process. The temperature in the active region (TRRAM) is evaluated with Joule heating (Wj) and thermal dissipation (Wd) [17] [18] as…”

Section: Temperature Calculationmentioning

confidence: 99%

Modeling of RRAM With Embedded Tunneling Barrier and Its Application in Logic in Memory

Lee

Chiang

2020

IEEE J. Electron Devices Soc.

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“…Several compact physics-based empirical models (i.e. SPICE) are the most suitable for large-scale simulations and capturing valence change [60][61][62][63][64][65][66][67][68][69][70]. Nevertheless, these models are not sufficiently accurate and lack industrial multi-simulator compatibility and interoperability.…”

Section: Modeling Framework and Comparison Of Memristive Devices And ...mentioning

confidence: 99%

Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

Zayer

Dghais

Hamdi

2019

J. Phys. D: Appl. Phys.

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This paper presents a comparative synthesis of the suitability of three memristive device technologies and their corresponding spike-timing-dependent plasticity (STDP) learning windows for neuromorphic applications. The physical mechanisms behind the nonlinear switching memristive dynamics of ReRAM, based on titanium dioxide, ferroelectric tunnel junctions, and phase change memory are analyzed towards the development of accurate and computationally efficient compact models which are implemented as a Verilog-A description. The developed Verilog-A compact models are separately validated and compared with the measurement data. Moreover, the asynchronous STDP learning rule is implemented using the above mentioned memristive devices as artificial synapse for spike-based neuromorphic computing. The considered memristive technologies are compared and discussed towards their integration in fast and/or large-scale circuit implementations.

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A Predictive Compact Model of Bipolar RRAM Cells for Circuit Simulations

Cited by 7 publications

References 18 publications

On the Thermal Models for Resistive Random Access Memory Circuit Simulation

On the Thermal Models for Resistive Random Access Memory Circuit Simulation

Modeling of RRAM With Embedded Tunneling Barrier and Its Application in Logic in Memory

Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

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