Issue 19, 2024

Optical color routing enabled by deep learning

Abstract

Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors.

Graphical abstract: Optical color routing enabled by deep learning

Article information

Article type
Minireview
Submitted
09 Janv. 2024
Accepted
21 Marts 2024
First published
21 Marts 2024

Nanoscale, 2024,16, 9284-9294

Optical color routing enabled by deep learning

S. Xiong and X. Yang, Nanoscale, 2024, 16, 9284 DOI: 10.1039/D4NR00105B

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