SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices
Aguirre, Fernando Leonel (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Suñé, Jordi 1963- (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Miranda, Enrique (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Date:	2022
Abstract:	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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; Versió publicada
Subject:	Memristor ; Resistive switching ; Memory ; Memdiode
Published in:	Micromachines, Vol. 13, Issue 2 (February 2022) , art. 330, ISSN 2072-666X

DOI: 10.3390/mi13020330
PMID: 35208454

18 p, 4.5 MB

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