SPICE Model of Memristor with Nonlinear Dopant Drift

1,202 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Memristor-based neural networks (2013, background): “The simulation of memristive behaviour in electric circuits is also possible [24–28].”
Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies (2014, background): “Several memristor models have been proposed to describe the behavior of physical memristors [16]–[23].”
A Memristor Device Model (2011, methodology): “Simulated I–V characteristic using the memristor model in [7].”
Memristor-based memory: The sneak paths problem and solutions (2013, background): “In addition, several models for memristors has been introduced [10–16].”
Logic Design Within Memristive Memories Using Memristor-Aided loGIC (MAGIC) (2016, unknown): “There are several memristor models proposed in the literature [31]–[37].”
On the mathematical modeling of memristors (2010, methodology): “All mathematical equations are compared with the nonlinear Memristor’s SPICE model given in [12], showing perfect matching.”
A Versatile Memristor Model With Nonlinear Dopant Kinetics (2011, methodology): “[17], in contradiction to the original HP memristor model [16].”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, background): “A multiplicative term may result in the stacking of w at one of its limits, as mentioned by Biolek et al. (2009).”