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Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications

In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerizati...

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Detalles Bibliográficos
Autores principales: Minnekhanov, Anton A., Emelyanov, Andrey V., Lapkin, Dmitry A., Nikiruy, Kristina E., Shvetsov, Boris S., Nesmelov, Alexander A., Rylkov, Vladimir V., Demin, Vyacheslav A., Erokhin, Victor V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658497/
https://www.ncbi.nlm.nih.gov/pubmed/31346245
http://dx.doi.org/10.1038/s41598-019-47263-9
Descripción
Sumario:In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti of ~500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (up to 10(4)), retention (≥10(4) s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes). We have experimentally shown that parylene-based memristive elements can be trained by a biologically inspired spike-timing-dependent plasticity (STDP) mechanism. The obtained results have been used to implement a simple neuromorphic network model of classical conditioning. The described advantages allow considering parylene-based organic memristors as prospective devices for hardware realization of spiking artificial neuron networks capable of supervised and unsupervised learning and suitable for biomedical applications.