Affiliation:
1. Beijing Institute of Technology
2. East China Normal University
Abstract
Metasurface provides an unprecedented means to manipulate electromagnetic waves within a two-dimensional planar structure. Traditionally, the design of meta-atom follows the pattern-to-phase paradigm, which requires a time-consuming brute-forcing process. In this work, we present a fast inverse meta-atom design method for the phase-to-pattern mapping by combining the deep neural network (DNN) and genetic algorithm (GA). The trained classification DNN with an accuracy of 92% controls the population generated by the GA within an arbitrary preset small phase range, which could greatly enhance the optimization efficiency with less iterations and a higher accuracy. As proof-of-concept demonstrations, two reflective functional metasurfaces including an orbital angular momentum generator and a metalens have been numerically investigated. The simulated results agree very well with the design goals. In addition, the metalens is also experimentally validated. The proposed method could pave a new avenue for the fast design of the meta-atoms and functional meta-devices.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Basic Research Foundation of Beijing Institute of Technology
111 Project
Subject
Atomic and Molecular Physics, and Optics
Cited by
4 articles.
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