3D Holographic Projection Based On Machine Learning

In addition to having a basic impact on science, the three-dimensional (3D) vectorial nature of electromagnetic waves of light has inspired innovative uses for optical displays, microscopy, and manipulation.

Conventional optical holography, on the other hand, can only handle the amplitude and phase information of an optical beam, totally excluding the 3D vectorial feature of light. The use of a multilayer perceptron artificial neural network-based machine learning inverse design to exactly reconstruct any arbitrary 3D vectorial field distribution on a wavefront is demonstrated by scientists as a vectorial 3D holographic projection.

This vectorial 3D holographic projection enables the lensless reconstruction of a 3D vectorial holographic picture with the high diffraction efficiency of 78% required for floating displays and an ultrawide viewing angle of 94°. The findings lead to a novel machine learning approach for multiplexing holographic 3D vectorial fields in display and encryption, allowing an artificial intelligence-enabled holographic paradigm for utilizing light’s vectorial nature.

Since Gabor first developed it, optical holography—which enables the reconstruction of the amplitude and phase information of a three-dimensional (3D) image of an object—has accelerated the development of a wide range of cutting-edge technologies, including optical displays, data storage, optical trapping, holographic fabrication, pattern recognition, artificial neural networks, and all-optical machine learning.

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