Fourier phase retrieval using physics-enhanced deep learning.

Fourier phase retrieval (FPR) aims to reconstruct an object image from the magnitude of its Fourier transform. Despite its widespread utility in various fields of engineering and science, the inherent ill-posed nature of the FPR problem poses a significant challenge. Here we propose a learning-based approach that incorporates the physical model of the FPR imaging system with a deep neural network.

Multifunctional Metasurfaces Enabled by Multifold Geometric Phase Interference.

Geometric phase is frequently used in artificially designed metasurfaces; it is typically used only once in reported works, leading to conjugate responses of two spins. Supercells containing multiple nanoantennas can break this limitation by introducing more degrees of freedom to generate new modulation capabilities. Here, we provide a method for constructing supercells for geometric phases using triple rotations, each of which achieves a specific modulation function.