Modeling Networks of Probabilistic Memristors in SPICE

Efficient simulation of stochastic memristors and their networks requires novel modeling approaches. Utilizing a master equation to find occupation probabilities of network states is a recent major departure from typical memristor modeling [Chaos, solitons fractals 142, 110385 (2021)]. In the presen...

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Bibliographic Details
Published in:	Radioengineering 2021-04, Vol.30 (1), p.157-163
Main Authors:	Dowling, V. J., Slipko, V. A., Pershin, Y. V.
Format:	Article
Language:	eng
Subjects:	memristors networks probabilistic computing spice
Online Access:	Request full text
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Summary:	Efficient simulation of stochastic memristors and their networks requires novel modeling approaches. Utilizing a master equation to find occupation probabilities of network states is a recent major departure from typical memristor modeling [Chaos, solitons fractals 142, 110385 (2021)]. 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 acdriven 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 code are included.
ISSN:	1210-2512