(V)TEAM for SPICE Simulation of Memristive Devices With Improved Numerical Performance

The paper introduces a set of models of memristive devices for a reliable, accurate and fast analysis of large networks in the SPICE (Simulation Program with Integrated Circuit Emphasis) environment. The modeling starts from the recently introduced TEAM (ThrEshold Adaptive Memristor Model) and VTEAM...

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Vydáno v:IEEE access Ročník 9; s. 30242 - 30255
Hlavní autoři: Biolek, Dalibor, Kolka, Zdenek, Biolkova, Viera, Biolek, Zdenek, Kvatinsky, Shahar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Cellular communication
cellular nonlinear network
Environment models
Equations of state
Integrated circuit modeling
Integrated circuits
Mathematical model
Mathematical models
Memory devices
Memristor
Memristors
Networks
Numerical models
Simulation
SPICE
stick effect
Threshold voltage
VTEAM
window function
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:The paper introduces a set of models of memristive devices for a reliable, accurate and fast analysis of large networks in the SPICE (Simulation Program with Integrated Circuit Emphasis) environment. The modeling starts from the recently introduced TEAM (ThrEshold Adaptive Memristor Model) and VTEAM (Voltage ThrEshold Adaptive Memristor Model). A number of improvements are made towards the stick effect elimination and other numerical refinements to make the analysis of large networks fast and accurate. A set of models are proposed that utilize the synergy of several techniques such as window asymmetrization, integration with saturation, state equation preprocessing, scaling, and smoothing. The performance of models is tested in Cadence PSPICE 17.2 and particularly in HSPICE v2017, the latter on a large-scale CNN (Cellular Nonlinear Network) for detecting edges of binary images. The simulations manifest the usability of developed models for fast and reliable operation in networks containing more than one million nodes.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3059241