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RESEARCH PRODUCT
Modeling Networks of Probabilistic Memristors in SPICE
V. J. DowlingV. A. SlipkoY. V. Pershinsubject
FOS: Computer and information sciencesHardware_MEMORYSTRUCTURESCondensed Matter - Mesoscale and Nanoscale PhysicsFOS: Physical sciencesComputer Science - Emerging TechnologiesComputer Science::Hardware ArchitectureEmerging Technologies (cs.ET)Computer Science::Emerging TechnologiesmemristorsspicenetworksMesoscale and Nanoscale Physics (cond-mat.mes-hall)lcsh:Electrical engineering. Electronics. Nuclear engineeringprobabilistic computinglcsh:TK1-9971description
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-04-01 | Radioengineering |