Polarization Multiplexing Bifunctional Metalens Designed by Deep Neural Networks - Zhengchang Liu; Pu Peng; Xiao He; Zhibo Dang; Yuchen Dai; Yuxiang Chen; Xinyuan Shao; Yu Li; Yijing Huang; Donglin Liu; Guangyi Tao; Yunhao Zhang; et al. (Advanced Physics Research, 2023)
Abstract As planar optical elements, metasurfaces confer an unprecedented potential to manipulate light, which benefits from the deep control of the interactions between nanostructures and light. In the past decade, considerable progress has been made in various metasurfaces for on‐demand functions, drawing great interest from the scientific community. However, it is a great challenge to integrate different functions into a single metasurface, due to the incapability of manipulating light at different dimensions and the lack of universal intelligent design strategy. Here, an intelligent design platform based on deep neural networks is proposed, which can map between structure parameters and optical response. The well‐trained network model can intelligently retrieve nanostructures to meet multidimensional optical requirements of metasurfaces. Four metalenses for chiral focusing are realized by the design platform and the simulation results are highly consistent with the design target. In addition, metalenses based on arbitrary polarization at various working wavelength are also demonstrated, showing that the method has powerful design ability. Various optical properties of nanostructures, such as phase shift and polarization, are manipulated by deep neural networks, which can greatly promote the development of multifunctional devices and further pave the way for optical display, communication, computing, sensing, and other applications.