A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks

• URL: http://arxiv.org/abs/2001.00121v1
• Date: Wed, 1 Jan 2020 01:21:36 GMT
• Title: A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks
• Authors: 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 and Hualiang Zhang
• Abstract summary: In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time. 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. The presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds.
• Score: 7.039798390237901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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