ERI: Sub-diffractive Optical Trapping Enabled by Deep-Learning-Assisted Metasurface Design

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。光与不同材料和结构的复杂相互作用使几种日常应用成为可能：用太阳能电池发电，用光学显微镜放大非常小的物体，或通过光纤传输大量数据。除了这些众所周知的电磁(EM)相互作用，光还可以对物体施加压力(即力)，如果控制得当，可以允许非常小的粒子移动和定位。例如，纳米粒子、细胞和分子比人类头发的大小小10s到1000s，因此需要按比例精确控制施加在它们身上的力，才能实现所需的移动或定位。这直接取决于光被限制在小区域的程度，以及塑造粒子周围能量局部分布的能力。传统的光学捕获设备(称为光镊子)很适合操作微米大小的物体(大小约为百万分之一米的物体，如细胞)，但不能产生纳米级的限制(大小约为十亿分之一米的物体，如蛋白质)，因此不适合操作单个纳米尺度的物体。为了绕过这些限制并在光学纳米捕获中实现新的自由度，该项目旨在利用深度学习算法在图案精细的表面(称为超表面)附近创建最佳能量分布，以捕获纳米颗粒。这些表面由低损耗电介质材料制成，其轮廓设计用于创建纳米级的光限制，并有效地捕获不同大小和形状的粒子。此外，PI还从事教育和推广活动，包括为K-12和大学生组织本科生研究指导和工作坊，重点促进科学、技术、工程和数学(STEM)，并向学生介绍计算机科学在其他科学领域的应用。技术描述：该项目旨在利用亚表面的波形特性设计一类新型的紧凑纳米结构，用于亚衍射光捕获，能够产生所需的具有巨大光学力的捕获潜力，该潜力是为所选粒子量身定做的。传统的光学捕获方法不容易允许光的纳米级聚焦，并且在实现对小于100 nm的粒子的有效光学纳米捕获方面面临主要障碍。这些障碍包括要求高强度激光产生局部力，这反过来可能会对样品造成光热损害，以及由于焦区较大而无法区分和捕获单个纳米粒子。通过使用人工神经网络来捕捉高度非线性和复杂的粒子场相互作用，同时利用变形曲面提供的巨大设计空间来解决这些限制。该项目的具体目标包括：(I)在准表面附近产生高度受限的捕获势，(Ii)能够为各种形状/大小的颗粒量身定做捕获势，(Iii)在表面的不同方向和位置产生局部排斥力和吸引力，以及(Iv)产生适合于亚波长纳米粒子的增强的光学力，同时防止高激光功率造成的永久性光热损害。此外，PI从事研究、指导和组织研讨会，重点是促进STEM和向学生介绍深度学习对光子结构设计的影响。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The complex interactions of light with different materials and structures enable several everyday applications: generating electricity with solar cells, magnifying very small objects with light microscopes, or transmitting large amounts of data through optical fibers. Beyond these well-known electromagnetic (EM) interactions, light can also exert pressure (i.e., force) on objects which, if properly controlled, can allow for movement and positioning of very small particles. For instance, nanoparticles, cells, and molecules are 10s to 1000s of times smaller than the size of the human hair and thus require a proportionally accurate control of the force exerted on them to enable the desired movement or positioning. This directly depends on how well light can be confined into small areas as well as the ability to shape the local distribution of the energy around the particles. Conventional optical trapping devices (called optical tweezers) are a good candidate for manipulating micron-sized objects (sizes in the order of one millionth of a meter such as cells) but cannot create nanoscale confinement (sizes in the order of one billionth of a meter such as proteins), thus are not suitable for manipulating individual nanoscale objects. To circumvent these limitations and enable new degrees of freedom in optical nano-trapping, this project is designed to utilize deep learning algorithms for creating optimal energy distributions near a finely patterned surface (called metasurfaces) to trap nanoparticles. These surfaces are made of low-loss dielectric materials whose profile is designed to create nanoscale light confinement and to efficiently trap particles with different sizes and shapes. In addition, the PI engages in educational and outreach activities including undergraduate research mentoring and workshop organization for K-12 and college students, focusing on promoting science, technology, engineering, and mathematics (STEM), and introducing students to applications of computer science in other scientific fields.Technical description: This project aims to leverage the wave-shaping properties of metasurfaces to design a novel class of compact nanostructures for sub-diffractive optical trapping, able to generate desired trapping potentials with giant optical forces that are tailored for the particles of choice. Conventional approaches for optical trapping do not readily allow nanoscale focusing of light and face major barriers in achieving effective optical nano-trapping for particles smaller than 100 nm. These barriers include requirement for high intensity lasers to generate local force which in return may cause photothermal damage to the sample as well as failure to distinguish and trap single nanoparticles due to large focal area. These limitations are addressed by using artificial neural networks to capture the highly nonlinear and complex particle-field interactions while leveraging the large design space offered by metasurfaces. The specific objectives of the project include: (i) generating highly confined trapping potentials near metasurfaces, (ii) ability to tailor the trapping potential for various particle shapes/sizes, (iii) creating local repulsive and attractive forces in different directions and locations on the surface, and (iv) creating enhanced optical forces suitable for subwavelength nanoparticles while preventing permanent photothermal damage resulted from high laser power. In addition, PI engages in research mentoring and organization of symposia focused on promoting STEM and introducing students to implications of deep learning in the design of photonic structures.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.