Machine learning methods for nanolaser characterization

TL;DR

This paper explores the application of Bayesian machine learning and digital coherent detection techniques to improve the sensitivity and detail of nanolaser noise and dynamics characterization, aiming to advance integrated photonic networks.

Contribution

It introduces novel machine learning-based tools and concepts for highly-sensitive nanolaser noise analysis and dynamic inference, bridging machine learning and nanophotonics.

Findings

Enhanced noise characterization sensitivity

Novel inference methods for laser dynamics

Potential for integrated photonic network applications

Abstract

Nanocavity lasers, which are an integral part of an on-chip integrated photonic network, are setting stringent requirements on the sensitivity of the techniques used to characterize the laser performance. Current characterization tools cannot provide detailed knowledge about nanolaser noise and dynamics. In this progress article, we will present tools and concepts from the Bayesian machine learning and digital coherent detection that offer novel approaches for highly-sensitive laser noise characterization and inference of laser dynamics. The goal of the paper is to trigger new research directions that combine the fields of machine learning and nanophotonics for characterizing nanolasers and eventually integrated photonic networks