Lensless complex amplitude demodulation based on deep learning in holographic data storage

To increase the storage capacity in holographic data storage(HDS),the information to be stored is encoded into a complex amplitude.Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS.In this study,we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase.By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages,the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks(CNNs)to demodulate amplitude and phase respectively.The experimental system is simple,stable,and robust,and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase.To our investigation,this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

3D nano-printed geometric phase metasurfaces for generating accelerating beams with complex amplitude manipulation

Metasurface,a forefront in emerging optical devices,has demonstrated remarkable potential for complex amplitude manipulation of light beams.However,prevailing approaches face challenges in spatial resolution and complexities associated with integrating dynamic phases,impeding the simplified design and reproducible fabrication of metasurfaces.Here,we introduce an innovative approach for complex amplitude modulation within 3D nano-printed geometric phase metasurfaces.Our approach enables the generation of self-accelerating beams by encoding amplitude through phase-only manipulation,achieving high spatial resolution.Notably,this method circumvents the conventional need to adjust the geometric parameters of metasurface unit structures for amplitude manipulation,offering a streamlined and efficient route for design and fabrication complexity.This novel methodology holds promise for expedited and low-cost manufacturing of complex amplitude manipulation metasurfaces.

Direct field-to-pattern monolithic design of holographic metasurface via residual encoderdecoder convolutional neural network

Complex-amplitude holographic metasurfaces(CAHMs)with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level,leading to higher image-reconstruction quality compared with their natural counterparts.However,prevailing design methods of CAHMs are based on Huygens-Fresnel theory,meta-atom optimization,numerical simulation and experimental verification,which results in a consumption of computing resources.Here,we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design.A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory,which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images.The training results show that the normalized mean pixel error is about 3%on dataset.As verification,the metasurface prototypes are fabricated,simulated and measured.The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field,which demonstrates the effectiveness of our design.Encouragingly,this work provides a monolithic field-to-pattern design method for CAHMs,which paves a new route for the direct reconstruction of metasurfaces.

Spatiotemporal double-phase hologram for complex-amplitude holographic displays

This Letter describes an approach to encode complex-amplitude light waves with spatiotemporal double-phase holograms(DPHs) for overcoming the limit of the space-bandwidth product(SBP) delivered by existing methods. To construct DPHs, two spatially macro-pixel encoded phase components are employed in the SBP-preserved resampling of complex holograms. Four generated sub-DPHs are displayed sequentially in time for high-quality holographic image reconstruction without reducing the image size or discarding any image terms when the DPHs are interweaved. The reconstructed holographic images contain more details and less speckle noise, with their signal-to-noise ratio and structure similarity index being improved by 14.64% and 78.79%,respectively.