Multi-Terminal Nonwoven Stochastic Memristive Devices Based on Polyamide-6 and Polyaniline for Neuromorphic Computing

Author:

Prudnikov Nikita1,Malakhov Sergey1ORCID,Kulagin Vsevolod1,Emelyanov Andrey12,Chvalun Sergey1,Demin Vyacheslav1,Erokhin Victor3ORCID

Affiliation:

1. National Research Centre “Kurchatov Institute”, 123182 Moscow, Russia

2. Moscow Institute of Physics and Technology (National Research University), 141701 Dolgoprudny, Moscow Region, Russia

3. Institute of Materials for Electronics and Magnetism, Consiglio Nazionale delle Ricerche (IMEM-CNR), 43124 Parma, Italy

Abstract

Reservoir computing systems are promising for application in bio-inspired neuromorphic networks as they allow the considerable reduction of training energy and time costs as well as an overall system complexity. Conductive three-dimensional structures with the ability of reversible resistive switching are intensively developed to be applied in such systems. Nonwoven conductive materials, due to their stochasticity, flexibility and possibility of large-scale production, seem promising for this task. In this work, fabrication of a conductive 3D material by polyaniline synthesis on a polyamide-6 nonwoven matrix was shown. An organic stochastic device with a prospective to be used in reservoir computing systems with multiple inputs was created based on this material. The device demonstrates different responses (output current) when different combinations of voltage pulses are applied to the inputs. The approach is tested in handwritten digit image classification task in simulation with the overall accuracy exceeding 96%. This approach is beneficial for processing multiple data flows within a single reservoir device.

Funder

Russian Foundation for Basic Research

Publisher

MDPI AG

Subject

Molecular Medicine,Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biotechnology

Reference41 articles.

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5. Associative STDP-like Learning of Neuromorphic Circuits Based on Polyaniline Memristive Microdevices;Prudnikov;J. Phys. Appl. Phys.,2020

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