Low frequency noise in nanoparticle-molecule networks and implications for in-materio reservoir computing

TL;DR

This paper investigates low-frequency flicker noise in nanoparticle-molecule networks to identify optimal molecules for in-materio reservoir computing devices, providing insights into noise behaviors and device suitability.

Contribution

It characterizes and compares 1/f noise in various functionalized nanoparticle networks to guide molecular selection for reservoir computing applications.

Findings

Different molecules exhibit distinct 1/f noise behaviors.

Oleic acid networks show lower noise levels suitable for computing.

Redox molecules demonstrate unique switching noise characteristics.

Abstract

We study the low-frequency noise (LFN), i.e. flicker noise, also referred to as 1/f noise, in 2D networks of molecularly functionalized gold nanoparticles (NMN: nanoparticle-molecule network). We examine the noise behaviors of the NMN hosting alkyl chains (octanethiol), fatty acid oleic acids (oleylamine), redox molecule switches (polyoxometalate derivatives) or photo-isomerizable molecules (azobenzene derivatives) and we compare their 1/f noise behaviors. These noise metrics are used to evaluate which molecules are the best candidates to build in-materio reservoir computing molecular devices based on NMNs.