Control and stabilization of spatial mode quality in a radially polarized solid-state laser using machine learning (Conference Presentation)

Thomas L. Jefferson-Brain; Matthew J. Barber; Azaria D. Coupe; William A. Clarkson; Peter C. Shardlow

doi:10.1117/12.2551145

10 March 2020 Control and stabilization of spatial mode quality in a radially polarized solid-state laser using machine learning (Conference Presentation)

Thomas L. Jefferson-Brain, Matthew J. Barber, Azaria D. Coupe, William A. Clarkson, Peter C. Shardlow

Author Affiliations +

Proceedings Volume 11259, Solid State Lasers XXIX: Technology and Devices; 112590F (2020) https://doi.org/10.1117/12.2551145
Event: SPIE LASE, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Thomas L. Jefferson-Brain, Matthew J. Barber, Azaria D. Coupe, William A. Clarkson, and Peter C. Shardlow "Control and stabilization of spatial mode quality in a radially polarized solid-state laser using machine learning (Conference Presentation)", Proc. SPIE 11259, Solid State Lasers XXIX: Technology and Devices, 112590F (10 March 2020); https://doi.org/10.1117/12.2551145

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