论文标题
极化多路复用衍射计算：通过极化编码的衍射网络的一组线性变换的全光实现
Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
论文作者
论文摘要
由于机器学习的变革性进步，有关光学计算的研究最近引起了极大的关注。在不同的方法中，已经证明了由空间工程透射表面组成的衍射光网络已证明了全光统计推断和使用被动的自由空间光学层进行任意线性变换的衍射。在这里，我们通过单个使用深度学习训练的单个衍射网络介绍了极化多路复用的衍射处理器，以全力以赴地执行多个任意选择的线性转换。在此框架中，一系列预选的线性极化器位于各向同性的可训练的透射式衍射材料之间，并且不同的目标线性变换（复数值）唯一地分配给了输入/输出极化状态的不同组合。该极化多路复用衍射网络的传输层通过深度学习和错误折叠式训练和优化，通过使用与分配给不同输入/输出偏光组合的复杂值相关的线性转换相对应的输入/输出字段的数千个示例。 Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons (N) approaches N_p x N_i x N_o, where N_i and N_o represent the number of pixels at the input and output fields-of-view, respectively, and N_p refers to the number of unique分配给不同输入/输出极化组合的线性转换。这种偏振化的全光衍射处理器可以在光学计算和基于极化的机器视觉任务中找到各种应用。
Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive, free-space optical layers. Here, we introduce a polarization multiplexed diffractive processor to all-optically perform multiple, arbitrarily-selected linear transformations through a single diffractive network trained using deep learning. In this framework, an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic, and different target linear transformations (complex-valued) are uniquely assigned to different combinations of input/output polarization states. The transmission layers of this polarization multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations. Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons (N) approaches N_p x N_i x N_o, where N_i and N_o represent the number of pixels at the input and output fields-of-view, respectively, and N_p refers to the number of unique linear transformations assigned to different input/output polarization combinations. This polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks.
