arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.00121physics.opticscs.LG
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基于深度神经网络的自由介质元表面建模方法

A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks

University of Massachusetts Lowell(马萨诸塞大学洛厄尔分校);Massachusetts Institute of Technology(麻省理工学院);East China Normal University(华东师范大学);Lockheed Martin Corporation(洛克希德·马丁公司);University of Central Florida(中佛罗里达大学)
Sensong An, Bowen Zheng, Mikhail Y. Shalaginov, Hong Tang, Hang Li, Li Zhou, Jun Ding, Anuradha Murthy Agarwal, Clara Rivero-Baleine, Myungkoo Kang, Kathleen A.… 展开作者

Sensong An, Bowen Zheng, Mikhail Y. Shalaginov, Hong Tang, Hang Li, Li Zhou, Jun Ding, Anuradha Murthy Agarwal, Clara Rivero-Baleine, Myungkoo Kang, Kathleen A. Richardson, Tian Gu, Juejun Hu, Clayton Fowler, Hualiang Zhang

摘要:

超表面在成形光学波前方面显示出有前途的潜力,同时与庞大的几何光学器件相比保持紧凑。作为超颖表面的基本构件,超原子的设计依赖于试错法来实现目标电磁响应。这个过程包括表征大量具有不同物理和几何参数的不同元原子设计,这通常需要巨大的计算资源。本文介绍了一种基于深度学习的元表面/元原子建模方法,可以在保持准确性的同时显著缩短表征时间。基于卷积神经网络(CNN)结构,所提出的深度学习网络能够对具有自由形式的2D图案和不同晶格尺寸、材料折射率和厚度的元原子进行建模。此外,所提出的方法具有在毫秒级的时间尺度内预测元原子的宽光谱响应的能力,这使得它对于诸如快速元原子/元表面按需设计和优化的应用具有吸引力。

英文摘要:

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand designs and optimizations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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