A Surrogate-Assisted Extended Generative Adversarial Network for
  Parameter Optimization in Free-Form Metasurface Design
Manna Dai Yang Jiang Feng Yang Joyjit Chattoraj Yingzhi Xia Xinxing Xu Weijiang Zhao My Ha Dao Yong Liu
Metasurfaces have widespread applications in fifth-generation (5G) microwave
communication. Among the metasurface family, free-form metasurfaces excel in
achieving intricate spectral responses compared to regular-shape counterparts.
However, conventional numerical methods for free-form metasurfaces are
time-consuming and demand specialized expertise. Alternatively, recent studies
demonstrate that deep learning has great potential to accelerate and refine
metasurface designs. Here, we present XGAN, an extended generative adversarial
network (GAN) with a surrogate for high-quality free-form metasurface designs.
The proposed surrogate provides a physical constraint to XGAN so that XGAN can
accurately generate metasurfaces monolithically from input spectral responses.
In comparative experiments involving 20000 free-form metasurface designs, XGAN
achieves 0.9734 average accuracy and is 500 times faster than the conventional
methodology. This method facilitates the metasurface library building for
specific spectral responses and can be extended to various inverse design
problems, including optical metamaterials, nanophotonic devices, and drug
discovery.