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用于主动彩色图像调谐的超表面深度学习辅助逆设计

Deep learning-assisted inverse design of metasurfaces for active color image tuning.

作者信息

Weng Qiang, Bao Yanjun

机构信息

Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China.

出版信息

Nanoscale. 2024 Oct 17;16(40):19034-19041. doi: 10.1039/d4nr02378a.

DOI:10.1039/d4nr02378a
PMID:39301625
Abstract

Metasurfaces, artificial planar nanostructures, offer numerous advantages for color printing applications, including ultra-high resolution, resistance to fading, wide color gamut coverage, and multifunctional capabilities. Due to the sensitivity of their resonance spectra to the external environment, metasurfaces have garnered significant interest for color tuning applications. However, most existing approaches are limited to passive color tuning, wherein only the color changes passively while the composite color image remains unaltered. Active color image tuning, on the other hand, requires precise matching of both color and intensity to the designed targets before and after the tuning process. In this study, we propose a novel approach for active metasurface color image tuning by modulating the environmental refractive index. Building upon a forward neural network that establishes the relationship between the metasurface geometric parameters and color/intensity information, we employ a multi-objective inverse adjoint neural network. This network not only overcomes the inherent 'one-to-many' problem in inverse design using neural networks but also facilitates active color image tuning under three distinct environmental conditions. Our work provides a new approach for the inverse design of metasurfaces and opens up possibilities for applications in dynamic color printing, information encryption, and other related fields.

摘要

超表面,即人工平面纳米结构,在彩色印刷应用中具有诸多优势,包括超高分辨率、抗褪色、宽色域覆盖以及多功能特性。由于其共振光谱对外部环境敏感,超表面在颜色调谐应用中引起了广泛关注。然而,大多数现有方法仅限于被动颜色调谐,即只有颜色被动变化,而合成彩色图像保持不变。另一方面,主动彩色图像调谐需要在调谐过程前后将颜色和强度精确匹配到设计目标。在本研究中,我们提出了一种通过调制环境折射率进行主动超表面彩色图像调谐的新方法。基于建立超表面几何参数与颜色/强度信息之间关系的前馈神经网络,我们采用了多目标逆伴随神经网络。该网络不仅克服了使用神经网络进行逆设计中固有的“一对多”问题,还促进了在三种不同环境条件下的主动彩色图像调谐。我们的工作为超表面的逆设计提供了一种新方法,并为动态彩色印刷、信息加密和其他相关领域的应用开辟了可能性。

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