EAGER: All-Optical Information Processing Device for Seeing Through Diffusers at the Speed of Light

提案编号：2054102主要研究人员：Aydogan Ozcan(PI)和Mona Jarrahi(co-PI)机构：加州大学洛杉矶分校标题：EIGER：用于以光速透过漫射器的全光信息处理设备计划描述：EIGER：电子学、光子学和磁性设备非技术摘要：通过散射和扩散介质(如雾、云或人体组织)进行成像几十年来一直是一个重要的问题。无一例外，所有以前的方法都是基于其核心的数字计算机，使得信号首先由设备检测，然后使用数字计算机进行处理，以重建漫射器失真的图像。存在对新一代光学设备的重要且迫切的需求，该光学设备能够以光速并且不使用任何耗电的数字计算来通过例如人体组织、墙壁、包裹、云、雾等来观察、检测和量化目标对象。这种独特的能力一旦得到充分展示和开发，可能会在自主系统、生物医学成像、天文学、天体物理学、大气科学、安全、机器人等许多领域开辟各种新的应用。技术摘要：在这项提议中，将开发一种无需计算机的全光设备，它将以光速透过未知的漫射体，而不需要任何数字计算设备。与以前利用计算机重建未知漫射器后面的输入对象的图像的数字方法不同，当漫射器失真的输入信号通过连续训练的衍射层时，将使用一组衍射面/层来创建无源设备，以全光重建未知对象的图像，即，图像重建将以光的速度通过该设备。所设计的给定装置的每个衍射面将具有数千个衍射特征(称为神经元)，其中这些神经元的各个相位值将通过误差反向传播在训练阶段通过最小化地面真实图像和输出视场处的衍射图案之间的定制损失函数来调整。在这些衍射层的这种基于深度学习的设计之后，所得到的无源器件将被制造成位于未知漫射器和输出/像平面之间的物理衍射光网络。当输入物体的光通过一个未知的漫射体时，散射光将被训练好的衍射器收集起来，被动地重建失真的图像。这种衍射器的成功将在0.1-3太赫兹频段得到验证。与其他设备不同，提出的衍射图像重建设备以光速运行，除照明光外不需要任何功率。这种由被动衍射层实现的全光学图像重建将能够通过未知的漫射器看到物体，并且与现有的基于深度学习的或在计算机上实现的迭代图像重建方法相比，提供了一种极低功耗的设备。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Proposal Number: 2054102Principal Investigator: Aydogan Ozcan (PI) and Mona Jarrahi (co-PI)Institution: University of California, Los AngelesTitle: EAGER: All-Optical Information Processing Device for Seeing Through Diffusers at the Speed of LightProgram Description: EAGER: Electronics, Photonics, and Magnetic DevicesNon-Technical Abstract:Imaging through scattering and diffusive media such as fog, clouds or human tissue has been an important problem for many decades. Without an exception, all the previous methods are based on, at their core, digital computers, such that the signals are first detected by a device and then processed using digital computers to reconstruct the diffuser-distorted images. There is an important and pressing need for a new generation of optical devices that can see, detect and quantify target objects through for example human tissues, walls, packages, clouds, fogs, etc., at the speed of light and without using any power-hungry digital computation. This unique capability, once fully demonstrated and developed, might open various new applications in autonomous systems, biomedical imaging, astronomy, astrophysics, atmospheric sciences, security, robotics, and many other fields.Technical Abstract:In this proposal, a computer-free, all-optical device that will see through unknown diffusers at the speed of light, without the need for any digital computation device will be developed. Unlike previous digital approaches that utilized computers to reconstruct an image of the input object behind unknown diffusers, a passive device will be created using a set of diffractive surfaces/layers to all-optically reconstruct the image of an unknown object as the diffuser-distorted input signals diffract through successive trained diffractive layers, i.e., the image reconstruction will be processed at the speed of light through this device. Each diffractive surface of a given device designed will have thousands of diffractive features (termed as neurons), where the individual phase values of these neurons will be adjusted in the training phase through error back-propagation, by minimizing a customized loss function between the ground truth image and the diffracted pattern at the output field-of-view. After this deep learning-based design of these diffractive layers, the resulting passive device will be fabricated to form a physical diffractive optical network that is positioned between an unknown diffuser and the output/image plane. As the input object light passes through an unknown diffuser, the scattered light will be collected by the trained diffractive device to passively reconstruct the distorted image. The success of this diffractive device will be demonstrated in 0.1-3 THz frequency band. Unlike other devices, the proposed diffractive image reconstruction device operates at the speed of light and does not require any power except for the illumination light. This all-optical image reconstruction that will be achieved by passive diffractive layers will enable to see objects through unknown diffusers and present an extremely low power device compared with existing deep learning-based or iterative image reconstruction methods implemented in computers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.