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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… Click to show full abstract
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.
Journal Title: Optics express
Year Published: 2023
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