Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所（CNSI））
Jingxi Li, Yi-Chun Hung, Onur Kulce, Deniz Mengu, Aydogan Ozcan
由于机器学习的变革性进展，光学计算的研究最近引起了人们的极大关注。在不同的方法中，由空间工程透射表面组成的衍射光学网络已被证明用于全光学统计推断和使用无源自由空间光学层执行任意线性变换。在这里，我们介绍了一种偏振复用衍射处理器，通过使用深度学习训练的单个衍射网络来全光学地执行多个任意选择的线性变换。在该框架中，预先选择的线性偏振器的阵列被定位在各向同性的可训练透射衍射材料之间，并且不同的目标线性变换（复值）被唯一地分配给输入/输出偏振状态的不同组合。通过使用与分配给不同输入/输出偏振组合的复值线性变换中的每一个对应的输入/输出场的数千个示例，经由深度学习和误差反向传播来训练和优化该偏振复用衍射网络的传输层。我们的结果和分析表明，当可训练的衍射特征/神经元数目（N）接近N_p x N_i x N_o时，单个衍射网络可以成功地逼近并全光学地实现一组任意选择的目标变换，误差可以忽略不计，其中N_i和N_o分别表示输入和输出视场的像素数，N_p是指分配给不同输入/输出偏振组合的唯一线性变换的数目。这种偏振复用全光衍射处理器可以在光学计算和基于偏振的机器视觉任务中找到各种应用。
英文摘要：
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.
Wavelength-multiplexed massively parallel diffractive optical information storage and image projection
Electrical and Computer Engineering Department, University of California, Los Angeles（加州大学洛杉矶分校电气与计算机工程系）；Bioengineering Department, University of California, Los Angeles（加州大学洛杉矶分校生物工程系）；California NanoSystems Institute (CNSI), University of California, Los Angeles（加州大学洛杉矶分校加州纳米系统研究所）；Department of Computer Science, University of California, Los Angeles（加州大学洛杉矶分校计算机科学系）
Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
我们报告了一个大规模并行宽带衍射神经网络的基于深度学习的设计，用于全光学地执行输入和输出视场之间的一大组任意选择的复值线性变换，每个变换分别具有N_i和N_o像素。这种宽带衍射处理器由N_w个波长通道组成，每个通道被唯一地分配给不同的目标变换。可以通过相同的衍射网络在不同的照明波长下同时或顺序地（波长扫描）单独执行大量任意选择的线性变换。我们证明了这样一个宽带衍射网络，无论其材料色散如何，当其设计中的衍射神经元数目（N）匹配或超过2 x N_w x N_i x N_o时，可以成功地逼近N_w个唯一的复值线性变换，误差可以忽略不计。我们进一步报告，频谱复用能力（N_w）可以通过增加N来增加;我们的数值分析证实了N_w > 180的这些结论，其可以进一步增加到例如，~2000取决于近似误差的上限。大规模并行，波长复用衍射网络将是有用的设计高通量智能机器视觉系统和高光谱处理器，可以执行统计推断和分析具有独特的光谱特性的对象/场景。
Wavelength-Multiplexed 2D Beam Steering via a Passive Diffractive Network
Electrical and Computer Engineering Department, University of California, Los Angeles（加州大学洛杉矶分校电气与计算机工程系）；Bioengineering Department, University of California, Los Angeles（加州大学洛杉矶分校生物医学工程系）；California NanoSystems Institute (CNSI), University of California, Los Angeles（加州大学洛杉矶分校加州纳米系统研究所）
Combined spatially and temporally multiplexed photonic reservoir computer with a diffractively coupled VCSEL-array
University of Strathclyde（斯特拉斯克莱德大学）；Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)（跨学科物理与复杂系统研究所，IFISC（巴利阿里群岛大学-西班牙国家研究委员会））
Edge-enhanced diffractive neural networks based on spin-multiplexed nonlocal metasurfaces
School of Information Engineering, Nanchang University（南昌大学信息工程学院）；School of Physics and Materials Science, Nanchang University（南昌大学物理与材料学院）；Institute for Advanced Study, Nanchang University（南昌大学高等研究院）
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）
DNNsolver: Accurate and Efficient Deep Learning Modeling of Polarization-Sensitive Diffractive Neural Networks
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（清华大学精密仪器系精密测量技术与仪器国家重点实验室）
Meta-training of diffractive meta-neural networks for super-resolution direction of arrival estimation
Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心，清华大学）
Relative Wavefront Error Correction Over a 2.4 km Free-Space Optical Link via Machine Learning
School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气与电信工程学院）；Defence Science and Technology Group（国防科学与技术集团）；International Centre for Radio Astronomy Research, University of Western Australia（西澳大利亚大学国际射电天文研究中心）
3D aperture-engineered diffractive neural networks for super-resolution electromagnetic wave computing
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心）
BIFROST: A First-Principles Model of Polarization Mode Dispersion in Optical Fiber
Joint Quantum Institute, National Institute of Standards and Technology and University of Maryland（联合量子研究所，美国国家标准与技术研究院和马里兰大学）；Department of Physics, University of Maryland（马里兰大学物理系）；CCDC Army Research Laboratory（CCDC陆军研究实验室）；Quantum Technology Center, University of Maryland（马里兰大学量子技术中心）
Hunan University（湖南大学）；National University of Singapore（新加坡国立大学）；Greater Bay Area Institute for Innovation, Hunan University（湖南大学粤港澳大湾区创新研究院）；Shanghai Jiao Tong University（上海交通大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Aalto University（阿尔托大学）；National University of Singapore Suzhou Research Institute（新加坡国立大学苏州研究院）
