Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications

TL;DR

This paper demonstrates parylene-based memristive devices with multilevel resistive switching suitable for neuromorphic computing, showing excellent performance and biological learning capabilities, making them promising for biomedical neural networks.

Contribution

It introduces low-cost, biocompatible parylene memristors with multilevel switching and STDP learning, advancing organic neuromorphic device technology.

Findings

Achieved at least 16 stable resistive states.

Demonstrated spike-timing-dependent plasticity training.

Suitable for hardware neural network implementation.

Abstract

In this paper, the resistive switching and neuromorphic behavior 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 about 500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (about 10^3), 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.