Yuxiang Chen 1†Fengyu Zhang 2,4†Zhibo Dang 1Xiao He 1[ ... ]Zheyu Fang 1,3,*
Author Affiliations
Abstract
1 School of Physics, Peking University, Beijing 100871, China
2 The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics & Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
3 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
4 Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
Chirality plays an important role in biological processes, and enantiomers often possess similar physical properties and different physiologic functions. In recent years, chiral detection of enantiomers become a popular topic. Plasmonic metasurfaces enhance weak inherent chiral effects of biomolecules, so they are used in chiral detection. Artificial intelligence algorithm makes a lot of contribution to many aspects of nanophotonics. Here, we propose a nanostructure design method based on reinforcement learning and devise chiral nanostructures to distinguish enantiomers. The algorithm finds out the metallic nanostructures with a sharp peak in circular dichroism spectra and emphasizes the frequency shifts caused by nearfield interaction of nanostructures and biomolecules. Our work inspires universal and efficient machine-learning methods for nanophotonic design.
chiral detection metasurface deep learning cathodoluminescence 
Opto-Electronic Science
2023, 2(1): 220019
Author Affiliations
Abstract
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
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.
Photonics Research
2023, 11(5): 695

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