Ma Hongfeng, Dalloz Nicolas, Habrard Amaury, Sebban Marc, Sterl Florian, Giessen Harald, Hebert Mathieu, Destouches Nathalie
Laboratoire Hubert Curien, CNRS UMR 5516, Institut d'Optique Graduate School, Université Lyon, 42000 St-Etienne, France.
HID Global CID SAS, 48 rue Carnot, 92150 Suresnes, France.
ACS Nano. 2022 Jun 28;16(6):9410-9419. doi: 10.1021/acsnano.2c02235. Epub 2022 Jun 3.
Structural colors of plasmonic metasurfaces have been promised to a strong technological impact thanks to their high brightness, durability, and dichroic properties. However, fabricating metasurfaces whose spatial distribution must be customized at each implementation and over large areas is still a challenge. Since the demonstration of printed image multiplexing on quasi-random plasmonic metasurfaces, laser processing appears as a promising technology to reach the right level of accuracy and versatility. The main limit comes from the absence of physical models to predict the optical properties that can emerge from the laser processing of metasurfaces in which random metallic nanostructures are characterized by their statistical properties. Here, we demonstrate that deep neural networks trained from experimental data can predict the spectra and colors of laser-induced plasmonic metasurfaces in various observation modes. With thousands of experimental data, produced in a rapid and efficient way, the training accuracy is better than the perceptual just noticeable change. This accuracy enables the use of the predicted continuous color charts to find solutions for printing multiplexed images. Our deep learning approach is validated by an experimental demonstration of laser-induced two-image multiplexing. This approach greatly improves the performance of the laser-processing technology for both printing color images and finding optimized parameters for multiplexing. The article also provides a simple mining algorithm for implementing multiplexing with multiple observation modes and colors from any printing technology. This study can improve the optimization of laser processes for high-end applications in security, entertainment, or data storage.
由于其高亮度、耐久性和二向色性，等离子体超表面的结构色有望产生强大的技术影响。然而，制造超表面仍然是一个挑战，因为在每次实施时都必须对其空间分布进行定制，而且要覆盖大面积。自从在准随机等离子体超表面上展示了印刷图像复用以来，激光加工似乎是一种有前途的技术，可以达到所需的精度和通用性水平。主要限制来自于缺乏物理模型来预测超表面激光加工可能产生的光学特性，其中随机金属纳米结构由其统计特性来表征。在这里，我们证明，从实验数据训练的深度神经网络可以预测各种观察模式下激光诱导等离子体超表面的光谱和颜色。通过快速高效地生成数千个实验数据，训练精度优于感知上的最小可察觉变化。这种精度使得可以使用预测的连续颜色图表来找到打印复用图像的解决方案。我们的深度学习方法通过激光诱导双图像复用的实验演示得到了验证。这种方法极大地提高了激光加工技术在打印彩色图像和寻找复用优化参数方面的性能。本文还提供了一种简单的挖掘算法，用于通过任何打印技术实现具有多种观察模式和颜色的复用。这项研究可以改进激光工艺在安全、娱乐或数据存储等高端应用中的优化。
