Nanowerk April 23, 2024 Almost all the experimentally created electron vortex beam (EVBs) manifest isotropic doughnut intensity patterns. Based on the correlation between local divergence angle of electron beam and phase gradient along azimuthal direction, researchers in China showed that free electrons could be tailored to EVBs with customizable intensity patterns independent of the carried OAM. As proof-of-concept, by using computer generated hologram and designing phase masks to shape the incident free electrons they tailored three structured EVBs carrying identical OAM to exhibit completely different intensity patterns. Through the modal decomposition, they quantitatively investigated their OAM spectral distributions and revealed […]
Category Archives: Holography
Keeping objects levitated by sound airborne despite interference
Phys.org June 20, 2022 Researchers in the UK developed a computational technique that allows high-speed multipoint levitation even with arbitrary sound-scattering surfaces and demonstrated a volumetric display that works in the presence of any physical object. Their technique has a two-step scattering model and a simplified levitation solver, which together could achieve more than 10,000 updates per second to create volumetric images above and below static sound-scattering objects. They explained the technique achieved its speed with minimum loss in the trap quality and illustrate how it brought digital and physical content together by demonstrating mixed-reality interactive applications…read more. Open Access […]
Diffractive optical networks reconstruct holograms instantaneously without a digital computer
Phys.org November 2, 2021 Researchers at UCLA have developed a computer-free, all-optical process for the reconstruction of holograms using diffractive networks, diffractive network is an all-optical processor composed of a set of spatially engineered diffractive surfaces that collectively compute a desired transformation of an input light field through light-matter-interaction and diffraction. The spatial features of a diffractive network are trained and optimized for a given task using deep learning in a computer. After the training is complete, the diffractive surfaces can be fabricated and assembled to form a physical network that can all-optically reconstruct an input hologram of an unknown […]