Complex-valued Spectral Transfer-learning-based Hybrid Metasurface Inverse Design

Dr. Yi Xu1, Mr. Fu Li1, Prof. Jianqiang Gu1, Prof. Jiaguang Han2, Prof. Weili Zhang3
1Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China. 2Guilin University of Electronic Technology, Guangxi Key Laboratory of Optoelectronic Information Processing, School of Optoelectronic Engineering, Guilin, China. 3School of Electrical and Computer Engineering, Oklahoma State University, Oklahoma, USA


The interdisciplinary synergy of deep learning (DL) and metasurface shows great power in revealing the hidden complicated functions between physical structures and electromagnetic responses of the meta-atoms. But the conventional DL-based method is facing a conflict between computational efficiency and high demand for precise predictions, which consumes substantial time and computing resources to collect the specific dataset due to its data-hungry nature. Herein we report a complex-valued spectral transfer-learning-based hybrid metasurface design method to alleviate both ends of this dilemma. The proposed approach robustly improves the spectrum predicting accuracy on a small dataset with only 1000 homemade samples in the target terahertz (THz) waveband by migrating the knowledge learned from the open-source data in the infrared band, where the scale invariance of Maxwell’s equations is no longer applicable due to the material dispersion in different bands. Several typical THz meta-devices are demonstrated by employing the hybrid inverse model consolidating this trained target network and a global optimization algorithm as proof-of-concept applications. The simulated results clarify the reliability and scalability of our spectral transfer-learning-based metasurface design methodology assisted by complex deep neural networks, which is of great significance for balancing the efficiency and accuracy of the DL-based method, hence promoting the automated metasurface design in arbitrary bands.