Modeling networks of probabilistic memristors in SPICE

Abstract

Efficient simulation of probabilistic memristors and their networks requires novel modeling approaches. One major departure from the conventional memristor modeling is based on a master equation for the occupation probabilities of network states [arXiv:2003.11011 (2020)]. In the present article, we show how to implement such master equations in SPICE - a general-purpose circuit simulation program. In the case studies, we simulate the dynamics of ac-driven probabilistic binary and multi-state memristors, and dc-driven networks of probabilistic binary and multi-state memristors. Our SPICE results are in perfect agreement with known analytical solutions. Examples of LTspice codes are included.

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Cited by in corpus (5)

• Probabilistic Memristive Networks: Application of a Master Equation to Networks of Binary ReRAM cells
• Theory of heterogeneous circuits with stochastic memristive devices
• Memristive Ising Circuits
• A probabilistic model of resistance jumps in memristive devices
• Reduction of Joule Losses in Memristive Switching Using Optimal Control