Nonlinear response of Silicon Photonics microresonators for reservoir computing neural network

TL;DR

This paper reviews the use of silicon photonics microresonators, specifically microring resonators with nonlinear dynamics, for implementing reservoir computing neural networks capable of high-speed, real-time data processing.

Contribution

It introduces a novel approach using nonlinear silicon photonics microresonators for reservoir computing, including methodologies and experimental results from the University of Trento.

Findings

Demonstrated nonlinear dynamics in silicon microring resonators

Implemented reservoir computing with integrated photonic circuits

Achieved fast data processing using feedback delay loops

Abstract

Nowadays, Information Photonics is extensively studied and sees applications in many fields. The interest in this breakthrough technology is mainly stimulated by the possibility of achieving real-time data processing for high-bandwidth applications, still implemented through small-footprint devices that would allow for breaking the limit imposed by Moore's law. One potential breakthrough implementation of information photonics is via integrated photonic circuits. Within this approach, the most suitable computational scheme is achieved by integrated photonic neural networks. In this chapter, we provide a review of one possible way to implement a neural network by using silicon photonics. Specifically, we review the work we performed at the Nanoscience Laboratory of the University of Trento. We present methodologies, results, and future challenges about a delayed complex perceptron for fast data processing, a microring resonator exploiting nonlinear dynamics for a reservoir computing approach, and a microring resonator with the addition of a feedback delay loop for time series processing.