Metasurface meta-atoms design based on DNN and LightGBM algorithms

Metasurfaces is one of the research hotspots in the field of micro/nanometer technologies during the last decade. Whereas, it remains challenging to inverse design optics devices according to desired physical response. Here we demonstrate an attempt to design a metasurface meta-atoms with machine learning approach. We use the metasurface structure parameters, nanofin material, phase, and transmission as model data of the deep neural networks (DNN) and light gradient boosting machine (LighGBM), and the mapping relationship between geometric parameters and phase and transmission is established. The two models are highly accurate in forward design, achieving the best regression coefficient of 0.969, while for the inverse design the best regression coefficient is 0.918. Using the trained inverse design network, the structural parameters and nanofin composition of the metasurface meta-atoms can be obtained for a given phase and transmittance, and the two models show strong generalization ability. This method may facilitate the viability of complex metasurface design and it can be also potentially applied in optical zoom imaging, perfect absorbers, metasurface filters, and other nanophysics fields. •The highlights of this paper are mainly the following three points.•We demonstrate the application of LightGBM and DNN algorithms in the forward and inverse design of metasurface meta-atoms.•The transmittance and phase are predicted in the forward design.•The nanofins and structural parameter of the metasurface meta-atoms is design by inverse design.