Phase space framework enables a variable-scale diffraction model for coherent imaging and display

The fast algorithms in Fourier optics have invigorated multifunctional device design and advanced imaging technologies.However,the necessity for fast computations limits the widely used conventional Fourier methods,where the image plane has a fixed size at certain diffraction distances.These limitations pose challenges in intricate scaling transformations,3D reconstructions,and full-color displays.Currently,the lack of effective solutions makes people often resort to pre-processing that compromises fidelity.In this paper,leveraging a higher-dimensional phase space method,a universal framework is proposed for customized diffraction calculation methods.Within this framework,a variable-scale diffraction computation model is established for adjusting the size of the image plane and can be operated by fast algorithms.The model’s robust variable-scale capabilities and its aberration automatic correction capability are validated for full-color holography,and high fidelity is achieved.The tomography experiments demonstrate that this model provides a superior solution for holographic 3D reconstruction.In addition,this model is applied to achieve full-color metasurface holography with near-zero crosstalk,showcasing its versatile applicability at nanoscale.Our model presents significant prospects for applications in the optics community,such as beam shaping,computer-generated holograms(CGHs),augmented reality(AR),metasurface optical elements(MOEs),and advanced holographic head-up display(HUD)systems.

Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible

Replacing electrons with photons is a compelling route toward high-speed,massively parallel,and low-power artificial intelligence computing.Recently,diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical transformations.However,the existing architectures often comprise bulky components and,most critically,they cannot mimic the human brain for multitasking.Here,we demonstrate a multi-skilled diffractive neural network based on a metasurface device,which can perform on-chip multi-channel sensing and multitasking in the visible.The polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items.The areal density of the artificial neurons can reach up to 6.25×10^(6)mm^(-2) multiplied by the number of channels.The metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor,providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision,autonomous driving,and precision medicine.

3D-Integrated metasurfaces for full-colour holography

Metasurfaces enable the design of optical elements by engineering the wavefront of light at the subwavelength scale.Due to their ultrathin and compact characteristics,metasurfaces possess great potential to integrate multiple functions in optoelectronic systems for optical device miniaturisation.However,current research based on multiplexing in the 2D plane has not fully utilised the capabilities of metasurfaces for multi-tasking applications.Here,we demonstrate a 3D-integrated metasurface device by stacking a hologram metasurface on a monolithic Fabry–Pérot cavity-based colour filter microarray to simultaneously achieve low-crosstalk,polarisation-independent,high-efficiency,full-colour holography,and microprint.The dual functions of the device outline a novel scheme for data recording,security encryption,colour displays,and information processing.Our 3D integration concept can be extended to achieve multi-tasking flat optical systems by including a variety of functional metasurface layers,such as polarizers,metalenses,and others.