A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires
A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires
复制标题
基于物理的机器学习方法对金属纳米线复反射系数的建模
DOI：
10.1088/1361-6528/ac512e
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发表时间：
2022-02
期刊：
影响因子：
3.5
通讯作者：
Xiaoqin Wu;Yipei Wang
中科院分区：
文献类型：
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作者：
Xiaoqin Wu;Yipei Wang
金属纳米线是下一代等离子体器件的重要组成部分，具有高性能和紧凑的占地面积。等离子体波导的复反射系数对于估计谐振、激光或传感性能至关重要。通过结合物理导向的目标函数和约束，我们提出了一种将纳米线的特定反射问题转化为普遍回归问题的简单方法。该方法结合了基于物理建模和数据驱动建模的优点，能够有效、可靠地确定任意几何参数、工作环境和终端形状的金属纳米线的反射率和反射相位。研究结果可为建立等离子体结构的综合数据集，促进纳米光子元件和器件的理论研究和大规模设计提供有价值的参考。
Metal nanowires are attractive building blocks for next-generation plasmonic devices with high performance and compact footprint. The complex reflection coefficients of the plasmonic waveguides are crucial for estimation of the resonating, lasing, or sensing performance. By incorporating physics-guided objective functions and constraints, we propose a simple approach to convert the specific reflection problem of nanowires to a universal regression problem. Our approach is able to efficiently and reliably determine both the reflectivity and reflection phase of the metal nanowires with arbitrary geometry parameters, working environments, and terminal shapes, merging the merits of the physics-based modeling and the data-driven modeling. The results may provide valuable reference for building comprehensive datasets of plasmonic architectures, facilitating theoretical investigations and large-scale designs of nanophotonic components and devices.