A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks
This addresses a bottleneck in photonics design for researchers and engineers, offering a faster method for meta-atom optimization, though it is an incremental improvement over existing computational methods.
The paper tackles the computationally expensive trial-and-error design process for metasurfaces by introducing a deep learning-based modeling approach that reduces characterization time to milliseconds while maintaining accuracy.
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.