Memristive State Equation for Bipolar Resistive Switching Devices Based on a Dynamic Balance Model and Its Equivalent Circuit Representation

Miranda, E.; Suñé, J.

doi:10.1109/tnano.2020.3039391

Cited by 25 publications

(29 citation statements)

References 41 publications

(42 reference statements)

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“…As reported in [46], a very convenient differential equation for the memory state variable λ that complies with a number of experimental observations in memristive structures is:…”

Section: Memory State Equationmentioning

confidence: 86%

“…If for any reason, the SB and SF effects do not need to be considered, taking V S (λ) = V S a constant reference SET voltage and λ γ = 1 in Equations ( 10) and (11), respectively, the switching dynamics becomes exclusively voltage-controlled, as originally assumed in [46]. This behavior is typical of ECM cells in which abrupt RESET transitions are observed [47].…”

Section: Memory State Equationmentioning

confidence: 95%

“…As the second criterion for assessing the model behavior, the DMM switching dynamics is discussed next. It is worth emphasizing that the switching properties of the memory Equation ( 8) for constant and ramped voltage signals were reported in [46]. Briefly, Equation ( 8) complies with the expected characteristic switching times (SET and RESET) for a constant bias condition (Figure 7a): 𝜏 𝑆,𝑅 (𝑉) = 𝜏 0𝑆,𝑅 𝑒 ∓𝑉 𝑉 0𝑆,𝑅 ⁄ (13) and with the switching voltage value as a function of the applied signal ramp rate (RR) (Figure 7a):…”

Section: Switching Dynamicsmentioning

confidence: 99%

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SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

2022

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This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed.

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“…As reported in [46], a very convenient differential equation for the memory state variable λ that complies with a number of experimental observations in memristive structures is:…”

Section: Memory State Equationmentioning

confidence: 86%

Section: Memory State Equationmentioning

confidence: 95%

Section: Switching Dynamicsmentioning

confidence: 99%

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SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

2022

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“…Although Eq. 2 has already been used in connection with physical parameters of the CF [58][59][60][61], it was first employed as a behavioral memory equation in [62]. A central feature of Eq.…”

Section: Memory Equationmentioning

confidence: 99%

SPICE Simulation of RRAM-Based Cross-Point Arrays Using the Dynamic Memdiode Model

et al. 2021

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We thoroughly investigate the performance of the Dynamic Memdiode Model (DMM) when used for simulating the synaptic weights in large RRAM-based cross-point arrays (CPA) intended for neuromorphic computing. The DMM is in line with Prof. Chua’s memristive devices theory, in which the hysteresis phenomenon in electroformed metal-insulator-metal structures is represented by means of two coupled equations: one equation for the current-voltage characteristic of the device based on an extension of the quantum point-contact (QPC) model for dielectric breakdown and a second equation for the memory state, responsible for keeping track of the previous history of the device. By considering ex-situ training of the CPA aimed at classifying the handwritten characters of the MNIST database, we evaluate the performance of a Write-Verify iterative scheme for setting the crosspoint conductances to their target values. The total programming time, the programming error, and the inference accuracy obtained with such writing scheme are investigated in depth. The role played by parasitic components such as the line resistance as well as some CPA’s particular features like the dynamical range of the memdiodes are discussed. The interrelationship between the frequency and amplitude values of the write pulses is explored in detail. In addition, the effect of the resistance shift for the case of a CPA programmed with no errors is studied for a variety of input signals, providing a design guideline for selecting the appropriate pulse’s amplitude and frequency.

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“…(3) expresses that the behavior of the I-V characteristic is dictated by two frequency-dependent factors, G C and R S . The second equation deals with the memory state of the device [22] and is expressed in terms of the low voltage-normalized conductance g as a symmetric balance model:…”

Section: Model Description and Discussionmentioning

confidence: 99%

Understanding the Input Signal Frequency Effects on the Resistive Window of Memristors

Izquierdo¹,

González²,

Campabadal³

et al. 2020

Preprint

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As theoretically predicted by Prof. Chua, the input signal frequency has a major impact on the electrical behavior of memristors. According with one of the so-called fingerprints of such devices, the resistive window, <i>i.e.</i> the difference between the low and high resistance states, shrinks as the frequency increases for a given input signal amplitude. Physically, this effect stems from the incapability of ions/vacancies to follow the external electrical stimulus. In terms of the electrical behavior, the collapse of the resistive window can be ascribed to the shift of the set/reset voltages toward higher values. Moreover, for a given frequency, the resistance window increases with the signal amplitude. In this letter, we show that both phenomena are the two sides of the same coin and that can be consistently explained after considering the snapback effect and a balance model equation for the device memory state.

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Memristive State Equation for Bipolar Resistive Switching Devices Based on a Dynamic Balance Model and Its Equivalent Circuit Representation

Cited by 25 publications

References 41 publications

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

SPICE Simulation of RRAM-Based Cross-Point Arrays Using the Dynamic Memdiode Model

Understanding the Input Signal Frequency Effects on the Resistive Window of Memristors

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