Photonics Research, 2023, 11 (5): 695, Published Online: Apr. 13, 2023  

Self-design of arbitrary polarization-control waveplates via deep neural networks

Author Affiliations
School of Physics, State Key Lab for Mesoscopic Physics, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center of Quantum Matter, Yangtze Delta Institute of Optoelectronics, and Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, China
Abstract
The manipulation of polarization states beyond the optical limit presents advantages in various applications. Considerable progress has been made in the design of meta-waveplates for on-demand polarization transformation, realized by numerical simulations and parameter sweep methodologies. However, due to the limited freedom in these classical strategies, particular challenges arise from the emerging requirement for multiplex optical devices and multidimensional manipulation of light, which urge for a large number of different nanostructures with great polarization control capability. Here, we demonstrate a set of self-designed arbitrary wave plates with a high polarization conversion efficiency. We combine Bayesian optimization and deep neural networks to design perfect half- and quarter-waveplates based on metallic nanostructures, which experimentally demonstrate excellent polarization control functionalities with the conversion ratios of 85% and 90%. More broadly, we develop a comprehensive wave plate database consisting of various metallic nanostructures with high polarization conversion efficiency, accompanying a flexible tuning of phase shifts (02π) and group delays (0–10 fs), and construct an achromatic metalens based on this database. Owing to the versatility and excellent performance, our self-designed wave plates can promote the performance of multiplexed broadband metasurfaces and find potential applications in compact optical devices and polarization division multiplexing optical communications.

Zhengchang Liu, Zhibo Dang, Zhixin Liu, Yu Li, Xiao He, Yuchen Dai, Yuxiang Chen, Pu Peng, Zheyu Fang. Self-design of arbitrary polarization-control waveplates via deep neural networks[J]. Photonics Research, 2023, 11(5): 695.

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