Optimizing Structured Surfaces for Diffractive Waveguides
Yuntian Wang, Yuhang Li, Tianyi Gan, Kun Liao, Mona Jarrahi, Aydogan Ozcan

TL;DR
This paper presents a universal, deep learning-optimized diffractive waveguide platform capable of versatile functionalities, low-loss guiding, and spectral scalability, validated in the terahertz spectrum with potential for broad optical applications.
Contribution
Introduces a novel diffractive waveguide design framework that is highly versatile, scalable across wavelengths, and capable of complex functionalities without traditional dispersion engineering.
Findings
Achieved low-loss, high mode purity guidance in THz spectrum
Demonstrated waveguide components like bends, filters, and splitters
Validated designs through experimental 3D-printed prototypes
Abstract
Waveguide design is crucial in developing efficient light delivery systems, requiring meticulous material selection, precise manufacturing, and rigorous performance optimization, including dispersion engineering. Here, we introduce universal diffractive waveguide designs that can match the performance of any conventional dielectric waveguide and achieve various functionalities. Optimized using deep learning, our diffractive waveguide designs can be cascaded to each other to form any desired length and are comprised of transmissive diffractive surfaces that permit the propagation of desired guided modes with low loss and high mode purity. In addition to guiding the targeted modes along the propagation direction through cascaded diffractive units, we also developed various waveguide components and introduced bent diffractive waveguides, rotating the direction of mode propagation, as well…
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