Deep‐Learning Enabled Multicolor Meta‐Holography. Issue 15 (2nd May 2022)

• Record Type: Journal Article
• Authors: Ma, Dina; Li, Zhancheng; Liu, Wenwei; Geng, Guangzhou; Cheng, Hua; Li, Junjie; Tian, Jianguo; Chen, Shuqi
• Is Part Of: Advanced optical materials. Volume 10:Issue 15(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-02
• Subjects: evolution strategy -- inverse design -- meta‐holography -- multicolor -- neural networks; Optical materials -- Periodicals; Photonics -- Periodicals; 620.11295
• DOI: 10.1002/adom.202102628
• Languages: English
• ISSNs: 2195-1071

Abstract

Abstract: Multicolor holography, which can store and reconstruct wavefront information of optical waves at multiple wavelength channels, is demonstrated as a powerful platform for colorful image display. Recently, interleaved and segmented metasurfaces have emerged as appealing alternatives to realize the multicolor holography. However, the crosstalk among different wavelength channels can severely lower their performance. How to obtain the nanostructures with on‐demand resonance wavelength, bandwidth, and phase delay is the key to overcome this challenge. Here, a hybrid framework composed of a neural network and an evolutionary strategy is proposed to implement the inverse design of nanostructures with desired resonance wavelength, bandwidth, and phase delay. With the proposed hybrid framework, the crosstalk between different wavelength channels can be eliminated by precisely controlling the resonance wavelength and the bandwidth of every nanostructure. As a proof of concept, a multicolor meta‐holography for linear polarized light is experimentally and theoretically validated. The proposed hybrid framework provides a powerful platform for the design of metasurfaces for multi‐frequency optical manipulation and multiplexing. Abstract : An inverse design algorithm based on deep‐learning technology and evolution strategy is proposed to realize the rapid design of dielectric metasurfaces for multicolor meta‐holography. The proposed algorithm can predict the entire reflection spectra of a dielectric nanostructure in the visible regime with an acceptable accuracy and realize the real‐time inverse design of dielectric nanostructure with desired resonance wavelength, bandwidth, and phase delay.