Concepedia

TLDR

Metasurfaces enable exotic electromagnetic manipulation, yet their design remains time‑consuming, resource‑intensive, and requires specialized knowledge. This study introduces REACTIVE, a deep‑learning–based method to automate metasurface design. REACTIVE directly computes the metasurface structure from a specified design target using a deep‑learning model, streamlining the process and reducing computational demands. REACTIVE delivers a triple‑band absorber in 200× less time, demands minimal expert knowledge, and proves to be an effective tool for both laymen and engineers.

Abstract

Metasurfaces provide unprecedented routes to manipulations on electromagnetic waves, which can realize many exotic functionalities. Despite the rapid development of metasurfaces in recent years, the design process of metasurface is still time-consuming and computational resource-consuming. Moreover, it is quite complicated for layman users to design metasurfaces as plenty of specialized knowledge is required. In this work, a metasurface design method named REACTIVE is proposed on the basis of deep learning, as deep learning method has shown its natural advantages and superiorities in mining undefined rules automatically in many fields. REACTIVE is capable of calculating metasurface structure directly through a given design target; meanwhile, it also shows the advantage in making the design process automatic, more efficient, less time-consuming, and less computational resource-consuming. Besides, it asks for less professional knowledge, so that engineers are required only to pay attention to the design target. Herein, a triple-band absorber is designed using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate. The whole design process is achieved 200 times faster than the conventional one, which convincingly demonstrates the superiority of this design method. REACTIVE is an effective design tool for designers, especially for laymen users and engineers.

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