Machine-Learning Enabled Optimization for Robust Soliton Crystal Generation in Microring Resonators

C. Mazoukh; L. Di Lauro; B. Fischer; A. Aadhi; I. Alamgir; A. Eshaghi; B. E. Little; S. T. Chu; D. J. Moss; R. Morandotti; R. Morandotti

doi:10.1364/CLEO_AT.2023.JTu2A.91

CLEO 2023
Technical Digest Series (Optica Publishing Group, 2023),
paper JTu2A.91
•https://doi.org/10.1364/CLEO_AT.2023.JTu2A.91

Machine-Learning Enabled Optimization for Robust Soliton Crystal Generation in Microring Resonators

C. Mazoukh, L. Di Lauro, B. Fischer, A. Aadhi, I. Alamgir, A. Eshaghi, B. E. Little, S. T. Chu, D. J. Moss, and R. Morandotti

CLEO: Fundamental Science 2023

San Jose, CA United States
7–12 May 2023
ISBN: 978-1-957171-25-8

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Joint Poster Session I (JTu2A)

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C. Mazoukh, L. Di Lauro, B. Fischer, A. Aadhi, I. Alamgir, A. Eshaghi, B. E. Little, S. T. Chu, D. J. Moss, and R. Morandotti, "Machine-Learning Enabled Optimization for Robust Soliton Crystal Generation in Microring Resonators," in CLEO 2023, Technical Digest Series (Optica Publishing Group, 2023), paper JTu2A.91.

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Abstract

We illustrate a novel strategy to robustly generate soliton states in Hydex microring resonators pumped with a continuous-wave laser source, by employing genetic algorithms to optimize the parameters required for coherent state generation.

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