Simulation of Bipolar-Type Resistive Switching Devices Using a Recursive Approach to the Dynamic Memdiode Model

Miranda, E.; Piros, Eszter; Aguirre, Fernando; Kim, Tae Jin; Schreyer, Philipp; Gehrunger, J.; Oster, T.; Suñé, J.; Hochberger, Christian; Alff, Lambert

doi:10.1109/led.2023.3298023

Cited by 4 publications

(8 citation statements)

References 38 publications

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“…V is the input voltage, and R i is a constant series resistance [14]. After some considerations thoroughly discussed in [17], the discrete recursive approach for the I-V characteristic reads…”

Section: Dynamic Memdiode Modelmentioning

confidence: 99%

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A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

Salvador,

Rodriguez,

Miranda

2024

JLPEA

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Modeling in an emerging technology like RRAM devices is one of the pivotal concerns for its development. In the current bibliography, most of the models face difficulties in implementing or simulating unconventional scenarios, particularly when dealing with complex input signals. In addition, circuit simulators like Spice require long running times for high-resolution results because of their internal mathematical implementation. In this work, a fast, simple, robust, and versatile model for RRAM devices built in MATLAB is presented. The proposed model is a recursive and discretized version of the dynamic memdiode model (DMM) for bipolar-type resistive switching devices originally implemented in LTspice. The DMM model basically consists of two coupled equations: one for the current (non-linear current generator) and a second one for the memory state of the device (time-dependent differential equation). This work presents an easy-to-use tool for researchers to reproduce the experimental behavior of their devices and predict the outcome from non-trivial experiments. Three study cases are reported, aimed at capturing different phenomenologies: a frequency effect study, a cycle-to-cycle variability fit, and a stochastic resonance impact analysis.

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Section: Dynamic Memdiode Modelmentioning

confidence: 99%

“…According to our convention, the set occurs at positive bias and the reset at negative bias. Again, after some approaches discussed in [17], the recursively expressed and discretized version of (3) reads…”

Section: Dynamic Memdiode Modelmentioning

confidence: 99%

A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

Salvador,

Rodriguez,

Miranda

2024

JLPEA

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“…In what follows, for the sake of simplicity, we will concentrate exclusively on the set transition (potentiation) so that the second term in (2) can be dropped. As demonstrated in [18], the I-V characteristic (1) can be expressed as:…”

Section: Model Equations and Physical Considerationsmentioning

confidence: 99%

The Role of the Programming Trajectory in the Power Dissipation Dynamics and Energy Consumption of Memristive Devices

Miranda,

Piros,

Aguirre

et al. 2024

IEEE Electron Device Lett.

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Tuning the conductance of a memristive device is a process that requires energy and involves power dissipation. In this letter, the role the memory state programming strategy plays in this connection is investigated. To this end, the device model equations representing the electron transport and metal ion/oxygen vacancy displacement caused by the application of an external signal must be solved consistently. However, if instead of considering the applied voltage as the model input, a memory state trajectory is assumed, the model equations can be decoupled allowing an analytic description of the problem. In order to accomplish this objective a more accurate version of the dynamic memdiode model is used which incorporates additional physical considerations in the characteristic switching times. It is demonstrated that alternative trajectories (concave, convex, and sigmoidal) lead to a variety of energy consumption-maximum dissipated power relationships indicating the key role played by the selected programming strategy. This kind of study contributes to the basic understanding of the writing process of memristors (synaptic weight assignment) and sheds light on its electrical consequences.

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“…This approach is also used in several solid state memristor models. [64,66,[118][119][120] Inspired in Equation ( 1), we propose a model for the stationary current I dc as a function of voltage u in a single rectifying pore…”

Section: Functional Description Of Rectificationmentioning

confidence: 99%

Hysteresis, Rectification, and Relaxation Times of Nanofluidic Pores for Neuromorphic Circuit Applications

Bisquert

2024

Advanced Physics Research

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Based on the emergence of iontronic fluidic components for brain‐inspired computation, the general dynamical behavior of nanopore channels is discussed. The main memory effects of fluidic nanopores are obtained by the combination of rectification and hysteresis. Rectification is imparted by an intrinsic charge asymmetry that affects the ionic current across the nanopores. It is accurately described by a background conductivity and a higher conduction branch that is activated by a state variable. Hysteresis produces self‐crossing diagrams, in which the high current side shows inductive hysteresis, and the low current side presents capacitive hysteresis. These properties are well captured by measurements of impedance spectroscopy that show the correspondent spectra in each voltage wing. The detailed properties of hysteresis and transient response are determined by the relaxation time of the gating variable, that is inspired in the Hodgkin‐Huxley neuron model. The classification of effects based on simple models provides a general guidance of the prospective application of artificial nanopore channels in neuromorphic computation according to the measurement of complementary techniques.

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Simulation of Bipolar-Type Resistive Switching Devices Using a Recursive Approach to the Dynamic Memdiode Model

Cited by 4 publications

References 38 publications

A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

The Role of the Programming Trajectory in the Power Dissipation Dynamics and Energy Consumption of Memristive Devices

Hysteresis, Rectification, and Relaxation Times of Nanofluidic Pores for Neuromorphic Circuit Applications

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