Alignment of higher-order mode solid-state laser systems with machine learning diagnostic assistance

Jefferson-Brain, Thomas, Lewis, Coupe, Azaria, Deborah, Burns, Mark, Clarkson, William and Shardlow, Peter (2019) Alignment of higher-order mode solid-state laser systems with machine learning diagnostic assistance. 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019, ICM – International Congress Centre, Munich, Germany. 23 - 27 Jun 2019.

Record type: Conference or Workshop Item (Poster)

Abstract

A machine learning algorithm based on a constitutional neural network was trained to be able to predict transverse modal composition in real-time. It can analyse a 128x128 pixel greyscale intensity image in under 3 ms. This provides a fast metric for transverse modal composition - a metric that was lacking for modal content when compared to other characteristics of lasers.

Text

TLJB_CLEO_poster_17_06_2019 - Version of Record

Available under License Creative Commons Attribution.

Download (1MB)

More information

Published date: 24 June 2019

Venue - Dates: 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019, ICM – International Congress Centre, Munich, Germany, 2019-06-23 - 2019-06-27

Keywords: machine learning, lasers, mode, transverse mode, alignment, neural network

Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about School of Physics and Astronomy research