Control and stabilization of spatial mode quality in a radially polarized solid-state laser using machine learning
Control and stabilization of spatial mode quality in a radially polarized solid-state laser using machine learning
The automated selection and stabilization of the transverse mode of a radially polarized Ho:YAG laser is reported. A convolutional neural network (CNN) was developed to analyze the modal composition of the laser output in real-time. Calculated error signals from the CNN are compared to the desired mode, allowing a PID control algorithm to dynamically optimize the position of an intracavity lens and therefore maintain desired modal content over pump power changes. This CNN based diagnostic system provides a fast method for selection and stabilization of transverse modes in order to advance radially polarized sources for applications such as laser processing.
Jefferson-Brain, Thomas Lewis
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Barber, Matthew James
5682d70c-71a4-4875-a714-55704b8ac20c
Coupe, Azaria Deborah
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Clarkson, William
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Shardlow, Peter
9ca17301-8ae7-4307-8bb9-371df461520c
10 March 2020
Jefferson-Brain, Thomas Lewis
8bce2a02-37a4-4277-a8cb-0c40bde57837
Barber, Matthew James
5682d70c-71a4-4875-a714-55704b8ac20c
Coupe, Azaria Deborah
a94ae3f1-b6ad-4f69-8765-335aedb780e9
Clarkson, William
3b060f63-a303-4fa5-ad50-95f166df1ba2
Shardlow, Peter
9ca17301-8ae7-4307-8bb9-371df461520c
Jefferson-Brain, Thomas Lewis, Barber, Matthew James, Coupe, Azaria Deborah, Clarkson, William and Shardlow, Peter
(2020)
Control and stabilization of spatial mode quality in a radially polarized solid-state laser using machine learning.
Clarkson, W. Andrew and Shori, Ramesh K.
(eds.)
In Solid State Lasers XXIX: Technology and Devices.
vol. 11259,
SPIE..
(doi:10.1117/12.2551145).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The automated selection and stabilization of the transverse mode of a radially polarized Ho:YAG laser is reported. A convolutional neural network (CNN) was developed to analyze the modal composition of the laser output in real-time. Calculated error signals from the CNN are compared to the desired mode, allowing a PID control algorithm to dynamically optimize the position of an intracavity lens and therefore maintain desired modal content over pump power changes. This CNN based diagnostic system provides a fast method for selection and stabilization of transverse modes in order to advance radially polarized sources for applications such as laser processing.
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Published date: 10 March 2020
Venue - Dates:
Photonics West, Moscone Center, San Francisco, United States, 2020-02-01 - 2020-02-06
Identifiers
Local EPrints ID: 443573
URI: http://eprints.soton.ac.uk/id/eprint/443573
ISSN: 0277-786X
PURE UUID: 05245d02-c965-4b01-8777-ab233d35e28b
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Date deposited: 03 Sep 2020 01:46
Last modified: 17 Mar 2024 03:27
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Contributors
Author:
Thomas Lewis Jefferson-Brain
Author:
Matthew James Barber
Author:
Azaria Deborah Coupe
Author:
William Clarkson
Author:
Peter Shardlow
Editor:
W. Andrew Clarkson
Editor:
Ramesh K. Shori
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