2016/11/10 by Darko Zibar, Molly Piels, Zibar, Darko +6
Computer Science · Engineering · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Mechanical and Optical Resonators #Neural Networks and Reservoir Computing #Photonic and Optical Devices #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1611.03335
openalex publication_date 2016/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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