Reconfigurable design of a thermo-optically addressed liquid-crystal phase modulator by a neural network

Reconfigurable design of a thermo-optically addressed liquid-crystal phase modulator by a neural network

We present a machine learning approach to program the light phase modulation function of an innovative thermo-optically addressed, liquid-crystal based, spatial light modulator (TOA-SLM). The designed neural network is trained with a little amount of experimental data and is enabled to efficiently g...

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Veröffentlicht in:Optics express 2023-04, Vol.31 (8), p.12597-12608
Hauptverfasser: Barland, Stéphane, Ramousse, Loic, Chériaux, Gilles, Femy, Vincent, Claudet, Cyrille, Jullien, Aurélie
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Optics
Physics
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container_end_page 12608
container_issue 8
container_start_page 12597
container_title Optics express
container_volume 31
creator Barland, Stéphane
Ramousse, Loic
Chériaux, Gilles
Femy, Vincent
Claudet, Cyrille
Jullien, Aurélie
description We present a machine learning approach to program the light phase modulation function of an innovative thermo-optically addressed, liquid-crystal based, spatial light modulator (TOA-SLM). The designed neural network is trained with a little amount of experimental data and is enabled to efficiently generate prescribed low-order spatial phase distortions. These results demonstrate the potential of neural network-driven TOA-SLM technology for ultrabroadband and large aperture phase modulation, from adaptive optics to ultrafast pulse shaping.
doi_str_mv 10.1364/OE.483141
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subjects Optics
Physics
title Reconfigurable design of a thermo-optically addressed liquid-crystal phase modulator by a neural network
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