Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces

Abstract Metasurfaces have provided a novel and promising platform for realizing compact and high‐performance optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since near‐field coupling effects between elements will change when the element is surrounded by nonidentical structures. In this paper, a deep learning approach is proposed to predict the actual electromagnetic (EM) responses of each target meta‐atom placed in a large array with near‐field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta‐atom and its neighbors as input, and calculates its actual phase and amplitude in milliseconds. This approach can be used to optimize metasurfaces’ efficiencies when combined with optimization algorithms. To demonstrate the efficacy of this methodology, large improvements in efficiency for a beam deflector and a metalens over the conventional design approach are obtained. Moreover, it is shown that the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, it is envisioned that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs.