Event Date
Event Date
We introduce a universal and versatile diffractive waveguide framework that utilizes a series of thin, structured surfaces optimized through deep learning. These diffractive layers are cascaded to spatially modulate the phase of light, enabling the creation of task-specific cascadable waveguide topologies without the need for complex material dispersion engineering. Our framework demonstrates low-loss guiding of both single and multi-mode light, with performance matching that of conventional dielectric waveguides. Furthermore, it facilitates the design of components for various complex operations, including spatial and spectral mode filtering, mode splitting, and mode-specific polarization maintenance for both monochromatic and multi-wavelength light. A key advantage is the platform's scalability across the electromagnetic spectrum, from terahertz to visible light, achieved by scaling the diffractive features proportional to the operational wavelength. We experimentally validated the concept with 3D-printed diffractive waveguides in the terahertz spectrum, achieving a good agreement with numerical simulations.
Presenter
Yuntian Wang
Univ. of California, Los Angeles (United States)
Yuntian Wang is a Ph.D. student in the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), where he conducts research as a member of Professor Aydogan Ozcan's laboratory. His research is centered on the intersection of deep learning and computational imaging. Mr. Wang earned his Bachelor of Engineering from the Southern University of Science and Technology in 2023 before commencing his doctoral studies at UCLA.