Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers. (C) 2015 Author(s).

Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning

Erokhin Victor
2015

Abstract

Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers. (C) 2015 Author(s).
2015
Istituto dei Materiali per l'Elettronica ed il Magnetismo - IMEM
Memristor
Statistical Networks
Self-Assembled
Adaptive
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/335546
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