作为一种光学处理器，衍射深度神经网络（Diffractive Deep Neural Network，D2NN）利用通过机器学习设计的工程化衍射表面执行全光信息处理，以光穿过薄光学层的传播速度完成任务。在具有足够自由度的情况下，D2NN 可以利用空间相干光执行任意复值线性变换。类似地，D2NN 也可以在空间非相干照明下执行任意线性强度变换；然而，在空间非相干光下，这些变换是非负的，作用于输入视场（FOV）内受衍射限制的光强图案。在这里，我们将空间非相干 D2NN 的用途扩展到复值信息处理，以利用空间非相干光执行任意复值线性变换。通过仿真，我们表明，当经过优化的衍射特征数量增加并超过由输入和输出空间带宽积之积所决定的阈值时，空间非相干衍射视觉处理器可以近似任意复值线性变换，并可用于在非相干照明下进行全光图像加密。这些发现对于利用各种形式的基于衍射表面的光学处理器在自然光下进行全光信息处理具有重要意义。
英文摘要：
As an optical processor, a Diffractive Deep Neural Network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees-of-freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV). Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tunable, single passband as well as dual passband spectral filt…
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；David Geffen School of Medicine（戴维·格芬医学院）
Permutation matrices form an important computational building block frequently used in various fields including e.g., communications, information security and data processing. Optical implementation of permutation operators with relatively large number of input-output interconnections based on power-efficient, fast, and compact platforms is highly desirable. Here, we present diffractive optical networks engineered through deep learning to all-optically perform permutation operations that can scale to hundreds of thousands of interconnections between an input and an output field-of-view using p…
We propose and experimentally demonstrate temporally low-coherent optical diffraction tomography (ODT) based on angle-scanning Mach-Zehnder interferometry. Using a digital micromirror device based on diffractive tilting, we successfully maintain full-field interference of incoherent light during every scan sequence. The ODT reconstruction principles for temporally incoherent illuminations are thoroughly reviewed and developed. Several limitations of incoherent illumination are also discussed, such as the nondispersive assumption, optical sectioning capacity, and illumination angle limitation. …
Fabrication of large area (sub 1 cm cross-section) micro-optical components in a short period of time (approximately 10 min) and with lesser number of processing steps is highly desirable and cost-effective. In the recent years, femtosecond laser fabrication technology has revolutionized the field of manufacturing by offering the above capabilities. In this study, a fundamental diffractive optical element, binary axicon i.e axicon with two phase or amplitude levels, has been designed in three configurations namely conventional axicon, photon sieve axicon (PSA) and sparse PSA and directly mille…
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））
Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of obje…
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）
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）
The orbital angular momentum (OAM) of light holds great promise for applications in optical communication, super-resolution imaging, and high-dimensional quantum computing. However, the spatio-temporal coherence of the light source has been essential for generating OAM beams, as incoherent ambient light would result in polychromatic and obscured OAM beams in the visible spectrum. Here, we extend the applications of OAM to ambient lighting conditions. By miniaturizing spiral phase plates and integrating them with structural color filters, we achieve spatio-temporal coherence using only an incoh…
