Chiral Metasurface Vortex Focusing in Terahertz Band Based on Deep Learning
Sixue Chen, Zheyu Hou, Jingjing Wang, Yihang Qiu, Zhiqi Li, Jian Shen, Chaoyang Li
Abstract
This work demonstrates an on-demand design approach to reverse the design of the chiral metasurface structure by the given target circular dichroism (CD) value, which is based on deep learning. A conditional generation network (RCGN) with a mean absolute error (MAE) of 0.027 was built in this work. Using this network, the metasurface structure with a CD up to 0.471 can be designed, verifying the validity and feasibility of the reverse metasurface design structure. At a frequency of 1.126 THz, the right-hand circularly polarised wave can be phase-modulated by Pancharatnam Berry (PB) to achieve the function of focusing the vortex beam of a metasurface array. The addition of the reverse design-on-demand feature based on deep learning makes the process of designing metasurface structures much faster and more accurate.