Nano-scale reservoir computing

TL;DR

This paper explores the development of nano-scale reservoir computing using quantum dots, aiming to create molecular-level neural networks for environmental sensing and material monitoring.

Contribution

It introduces a novel approach to nano-scale reservoir computing with quantum dots, including experimental methods to make systems sensitive to environmental triggers.

Findings

Quantum dot systems exhibit nonlinear responses suitable for neural network implementation

Successful rendering of quantum dots sensitive to pH, redox potential, and ion concentration

Potential for miniaturized, molecular-level sensors for material monitoring

Abstract

This work describes preliminary steps towards nano-scale reservoir computing using quantum dots. Our research has focused on the development of an accumulator-based sensing system that reacts to changes in the environment, as well as the development of a software simulation. The investigated systems generate nonlinear responses to inputs that make them suitable for a physical implementation of a neural network. This development will enable miniaturisation of the neurons to the molecular level, leading to a range of applications including monitoring of changes in materials or structures. The system is based around the optical properties of quantum dots. The paper will report on experimental work on systems using Cadmium Selenide (CdSe) quantum dots and on the various methods to render the systems sensitive to pH, redox potential or specific ion concentration. Once the quantum dot-based systems are rendered sensitive to these triggers they can provide a distributed array that can monitor and transmit information on changes within the material.