2020
DOI: 10.36227/techrxiv.12479426
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Fundamentals and SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

Abstract: This paper reports the fundamentals and SPICE implementation of the dynamic memdiode model (DMM) for the conduction characteristics of bipolar resistive switching devices. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources.

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Cited by 2 publications
(2 citation statements)
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“…These fingerprints indicate that both the amplitude and frequency effects are interrelated, i.e., the ion/vacancy displacement rates within the insulating layer and the formation and rupture of the CF [50][51][52]. A model capable of satisfying these constraints was recently proposed by Miranda [53], considering an extension of the conventional memristive approach suggested by Prof. Chua. This proposal again involves a system of two coupled equations, one for the electron transport (transport equation, TE) and a second equation for the memory state of the device (memory equation, ME).…”
Section: Dynamic Memdiode Modelmentioning
confidence: 99%
“…These fingerprints indicate that both the amplitude and frequency effects are interrelated, i.e., the ion/vacancy displacement rates within the insulating layer and the formation and rupture of the CF [50][51][52]. A model capable of satisfying these constraints was recently proposed by Miranda [53], considering an extension of the conventional memristive approach suggested by Prof. Chua. This proposal again involves a system of two coupled equations, one for the electron transport (transport equation, TE) and a second equation for the memory state of the device (memory equation, ME).…”
Section: Dynamic Memdiode Modelmentioning
confidence: 99%
“…As mentioned in the Introduction, the one-way sensitivity analysis was applied to the QMM model [7]. This model describes the conduction characteristics of bipolar resistive switching devices.…”
Section: The Quasi-static Memdiode Modelmentioning
confidence: 99%