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

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.