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基于光学衍射神经网络的轨道角动量深度复用全息术

Orbital angular momentum deep multiplexing holography via an optical diffractive neural network.

作者信息

Huang Zebin, He Yanliang, Wang Peipei, Xiong Wenjie, Wu Haisheng, Liu Junmin, Ye Huapeng, Li Ying, Fan Dianyuan, Chen Shuqing

出版信息

Opt Express. 2022 Feb 14;30(4):5569-5584. doi: 10.1364/OE.447337.

DOI:10.1364/OE.447337
PMID:35209516
Abstract

Orbital angular momentum (OAM) mode multiplexing provides a new strategy for reconstructing multiple holograms, which is compatible with other physical dimensions involving wavelength and polarization to enlarge information capacity. Conventional OAM multiplexing holography usually relies on the independence of physical dimensions, and the deep holography involving spatial depth is always limited for the lack of spatiotemporal evolution modulation technologies. Herein, we introduce a depth-controllable imaging technology in OAM deep multiplexing holography via designing a prototype of five-layer optical diffractive neural network (ODNN). Since the optical propagation with dimensional-independent spatiotemporal evolution offers a unique linear modulation to light, it is possible to combine OAM modes with spatial depths to realize OAM deep multiplexing holography. Exploiting the multi-plane light conversion and in-situ optical propagation principles, we simultaneously modulate both the OAM mode and spatial depth of incident light via unitary transformation and linear modulations, where OAM modes are encoded independently for conversions among holograms. Results show that the ODNN realized light field conversion and evolution of five multiplexed OAM modes in deep multiplexing holography, where the mean square error and structural similarity index measure are 0.03 and 86%, respectively. Our demonstration explores a depth-controllable spatiotemporal evolution technology in OAM deep multiplexing holography, which is expected to promote the development of OAM mode-based optical holography and storage.

摘要

轨道角动量(OAM)模式复用为重建多个全息图提供了一种新策略,它与涉及波长和偏振的其他物理维度兼容,以扩大信息容量。传统的OAM复用全息术通常依赖于物理维度的独立性,而由于缺乏时空演化调制技术,涉及空间深度的深度全息术一直受到限制。在此,我们通过设计一个五层光学衍射神经网络(ODNN)原型,在OAM深度复用全息术中引入了一种深度可控成像技术。由于具有与维度无关的时空演化的光传播为光提供了独特的线性调制,因此可以将OAM模式与空间深度相结合,以实现OAM深度复用全息术。利用多平面光转换和原位光传播原理,我们通过酉变换和线性调制同时调制入射光的OAM模式和空间深度,其中OAM模式被独立编码以在全息图之间进行转换。结果表明,ODNN在深度复用全息术中实现了五个复用OAM模式的光场转换和演化,其中均方误差和结构相似性指数分别为0.03和86%。我们的演示探索了OAM深度复用全息术中一种深度可控的时空演化技术,有望推动基于OAM模式的光学全息术和存储的发展。

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Orbital angular momentum deep multiplexing holography via an optical diffractive neural network.基于光学衍射神经网络的轨道角动量深度复用全息术
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