Laser optical field reconstruction using deep learning and phase diversity

Jikai Wang; Sonam Smitha Ravi; Manuel Schwartz; Michael Scharun; Benjamin Dannecker; Dominik Bauer; Volker Rominger; Daniel Flamm; Stefen Nolte

doi:10.1117/12.3014326

20 June 2024 Laser optical field reconstruction using deep learning and phase diversity

Jikai Wang, Sonam Smitha Ravi, Manuel Schwartz, Michael Scharun, Benjamin Dannecker, Dominik Bauer, Volker Rominger, Daniel Flamm, Stefen Nolte

Author Affiliations +

Proceedings Volume 13005, Laser + Photonics for Advanced Manufacturing; 130050E (2024) https://doi.org/10.1117/12.3014326
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

We report on the recording of the near and far field intensity beam profiles to train a convolutional neural network, which is aimed to online detect system aberrations of an ultrafast laser amplifier. We extend the state of the art by implementing a spiral phase plate to use the concept of phase diversity. It is found that the underlying optical field in amplitude and phase can be accurately revealed.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jikai Wang, Sonam Smitha Ravi, Manuel Schwartz, Michael Scharun, Benjamin Dannecker, Dominik Bauer, Volker Rominger, Daniel Flamm, and Stefen Nolte "Laser optical field reconstruction using deep learning and phase diversity", Proc. SPIE 13005, Laser + Photonics for Advanced Manufacturing, 130050E (20 June 2024); https://doi.org/10.1117/12.3014326

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available