Photonic upconversion maximization for nonlinear meta-material enabled by deep learning
Photonic upconversion maximization for nonlinear meta-material enabled by deep learning
复制标题
通过深度学习实现非线性超材料的光子上转换最大化
DOI：
10.1117/12.2651695
复制
发表时间：
2023
期刊：
影响因子：
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通讯作者：
Cai, Wenshan
中科院分区：
文献类型：
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作者：
Raju, Lakshmi;Liu, Zhaocheng;Zhu, Dayu;Kim, Andrew;Poutrina, Ekaterina;Urbas, Augustine;Cai, Wenshan
从红外区到可见光谱的光子上转换可以通过和频产生（SFG）发生。二阶非线性光学响应，例如SFG，可以由非线性材料产生，在这种情况下为ABC纳米层压材料。由等离子体纳米层压装置组成的超材料的优化可以使来自入射波长的SFG最大化。深度学习框架的使用消除了对传统猜测和检查方法的需要，并为等离子体几何形状创造了新的可能性。这项研究的应用包括用于国防、自动驾驶汽车和其他商业用途的低成本夜视或微光成像系统。
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.