TCO‐Based Active Dielectric Metasurfaces Design by Conditional Generative Adversarial Networks,Advanced Theory and Simulations

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TCO‐Based Active Dielectric Metasurfaces Design by Conditional Generative Adversarial Networks
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2020-11-27 , DOI: 10.1002/adts.202000196
Samad Jafar‐Zanjani ₁ , Mohammad Mahdi Salary ₁ , Dat Huynh ₂ , Ehsan Elhamifar ₂ , Hossein Mosallaei ₁

Affiliation

While researchers in the field of active flat optics continue to make groundbreaking progress by seeking novel materials and control systems, the complexity and sensitivity of the nanostructures that they aspire to design are unavoidably increasing. Inverse design of the popular class of transparent conducting oxide (TCO)‐based active metasurfaces is particularly challenging, largely due to the limited choice of the active materials, and sensitive physical mechanisms that give rise to their tunability. In this contribution, a new machine learning method based on a combination of the K‐means clustering algorithm and conditional Wasserstein generative adversarial networks (cWGANs) for broadband multi‐modal inverse design of TCO‐based active metasurfaces is developed. Multi‐objective evolutionary optimization is adopted to efficiently generate a diverse training dataset of high‐performance active metasurfaces. The training dataset includes samples that operate at specific wavelengths throughout the optical telecommunications (telecom) band. K‐means algorithm is then used to extract the clusters (modes) present in the training dataset, and exclusive cWGAN models are fit on each of them. The model is capable of generating designs operating at wavelengths that are not present in the training dataset. It also provides a clear picture of the feasibility and interplay between the design objectives.

中文翻译：

基于条件生成对抗网络的基于TCO的有源介电超表面设计

尽管有源平面光学领域的研究人员通过寻找新颖的材料和控制系统继续取得突破性进展，但他们渴望设计的纳米结构的复杂性和灵敏度却不可避免地在增加。流行的一类基于透明导电氧化物（TCO）的活性超表面的逆向设计尤其具有挑战性，这主要是由于活性材料的选择有限以及引起其可调性的敏感物理机制。在这项贡献中，开发了一种新的机器学习方法，该方法基于K-means聚类算法和条件Wasserstein生成对抗网络（cWGAN）的组合，用于基于TCO的活动超表面的宽带多模态逆设计。采用多目标进化优化可有效生成高性能主动元表面的多样化训练数据集。训练数据集包括在整个光通信（telecom）波段以特定波长工作的样本。然后使用K-means算法提取训练数据集中存在的聚类（模式），并且在每个聚类上拟合cWGAN模型。该模型能够生成以训练数据集中不存在的波长工作的设计。它还清楚地显示了设计目标之间的可行性和相互影响。然后使用K-means算法提取训练数据集中存在的聚类（模式），并且在每个聚类上拟合cWGAN模型。该模型能够生成以训练数据集中不存在的波长工作的设计。它还清楚地显示了设计目标之间的可行性和相互影响。然后使用K-means算法提取训练数据集中存在的聚类（模式），并且在每个聚类上拟合cWGAN模型。该模型能够生成以训练数据集中不存在的波长工作的设计。它还清楚地显示了设计目标之间的可行性和相互影响。

更新日期：2021-02-04

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