ArticleMicromachines2022
SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices.
Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Memristance and transmemristance in multiterminal memristive systems.Scientific reports · 2026Article
- Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis.Chemphyschem : a European journal of chemical physics and physical chemistry · 2024Article
- Hysteresis in memristors produces conduction inductance and conduction capacitance effects.Physical chemistry chemical physics : PCCP · 2024Article
- Resistance Tracking Control of Memristors Based on Iterative Learning.Entropy (Basel, Switzerland) · 2023Article
- Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices.ACS applied materials & interfaces · 2022Article
- Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks.Micromachines · 2022Article
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Authors and funding
3 authors.
Funding
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.
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