Machine-Learning-Optimized OAM Excitation in Optical Fibers

Jeffrey Demas, Mathilde Hary, Goëry Genty, Siddharth Ramachandran

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We develop a machine learning algorithm for in situ control and optimization of OAM excitation in optical fibers, ensuring high-purity and low-loss coupling. The algorithm can correct intentional misalignments, as well as compensate alignment drift.

Original languageEnglish
Title of host publicationProceedings: CLEO 2024
PublisherOptica Publishing Group
ISBN (Electronic)9781957171395
DOIs
Publication statusPublished - 2024
Publication typeA4 Article in conference proceedings
EventConference on Lasers and Electro-Optics - Charlotte, United States
Duration: 5 May 202410 May 2024

Publication series

NameTechnical Digest Series

Conference

ConferenceConference on Lasers and Electro-Optics
Abbreviated titleCLEO
Country/TerritoryUnited States
CityCharlotte
Period5/05/2410/05/24

Keywords

  • Couplings
  • Electro-optical waveguides
  • Fiber lasers
  • Laser excitation
  • Lasers and electrooptics
  • Machine learning algorithms
  • Optimization

Publication forum classification

  • Publication forum level 0

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Computer Networks and Communications
  • Civil and Structural Engineering
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

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