A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires

Wu Xiaoqin; Wang Yipei

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A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires

机译：A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires

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摘要

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.

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来源
《Nanotechnology》 |2022年第20期|共9页
作者
Wu Xiaoqin; Wang Yipei;
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作者单位

Chongqing Univ;

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原文格式 PDF
正文语种英语
中图分类特种结构材料;
关键词
metal nanowires; plasmonic waveguides; complex reflection coefficient; reflectivity; reflection phase; machine learning; PLASMON MODES; WAVE; GOLD; PROPAGATION;

A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires

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