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arXiv · 1611.03335

Machine learning methods for nanolaser characterization

Darko Zibar·Molly Piels·Ole Winther·Jesper Moerk·Christian Schaeffer
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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

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Darko Zibar, Molly Piels, Ole Winther, Jesper Moerk, Christian Schaeffer. 2016-11-10. Machine learning methods for nanolaser characterization. https://arxiv.org/abs/1611.03335

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