Photonic upconversion maximization for nonlinear meta-material enabled by deep learning

Lakshmi Raju; Zhaocheng Liu; Dayu Zhu; Andrew Kim; Ekaterina Poutrina; Augustine Urbas; Wenshan Cai

doi:10.1117/12.2651695

14 March 2023 Photonic upconversion maximization for nonlinear meta-material enabled by deep learning

Lakshmi Raju, Zhaocheng Liu, Dayu Zhu, Andrew Kim, Ekaterina Poutrina, Augustine Urbas, Wenshan Cai

Proceedings Volume 12405, Nonlinear Frequency Generation and Conversion: Materials and Devices XXII; 124050F (2023) https://doi.org/10.1117/12.2651695
Event: SPIE LASE, 2023, San Francisco, California, United States

Abstract

Photonic upconversion from the infrared regime to the visible spectrum can occur through sum-frequency generation (SFG). A second-order nonlinear optical response, such as SFG, can be produced from a nonlinear material, in this case an ABC nanolaminate. Optimization of a metamaterial consisting of a plasmonic nanolaminate device can maximize the SFG from incident wavelengths. Utilization of a deep learning framework removes the need for traditional guess and check methods and creates new possibilities for plasmonic geometries. Applications of this research include low-cost night vision or low light imaging systems for defense, autonomous vehicles, and other commercial uses.

Conference Presentation

Citation Download Citation

Lakshmi Raju, Zhaocheng Liu, Dayu Zhu, Andrew Kim, Ekaterina Poutrina, Augustine Urbas, and Wenshan Cai "Photonic upconversion maximization for nonlinear meta-material enabled by deep learning", Proc. SPIE 12405, Nonlinear Frequency Generation and Conversion: Materials and Devices XXII, 124050F (14 March 2023); https://doi.org/10.1117/12.2651695

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