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机器学习辅助的光管理和电磁场调制大面积的等离子同轴圆柱柱/环纳米阵列模式

Anyang Wang¹, Yingjie Hang¹, Jiacheng Wang¹

¹Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003-9303, United States.

The journal of physical chemistry. C, Nanomaterials and interfaces

|February 20, 2025

概括

有限差异时间域 (FDTD) 模拟优化金纳米阵列的光学特性. 机器学习指导几何参数调整以获得增强的电场和质量因素.

科学领域:

塑学和纳米光子学
计算电磁学计算机电磁学
材料科学材料科学材料科学

背景情况:

制造六角同轴圆柱形金柱/环纳米阵列在实验上具有挑战性.
为所需的光学特性优化几何参数需要广泛,昂贵的实验.

研究的目的:

研究几何参数对金纳米阵列光学特性的影响.
利用模拟和机器学习来指导特定光学应用的纳米阵列的设计.

主要方法:

使用有限差异时间域 (FDTD) 模拟来建模光学响应.
分析了局部表面等离子体共振 (LSPR) 和电荷分布.
应用机器学习用于参数分析和可视化.

主要成果:

像等离子体共振带,电场增强和Q因子这样的光学特性是可调的.
通过管理LSPR和电荷分布,可以抑制辐射减缓,并集中电场.
纳米阵列高度和间隙宽度是优化Q因子和电场增强的关键参数.

结论:

与机器学习相结合的FDTD模拟为设计纳米阵列提供了理论框架.
这种方法有助于为特定的光学功能量身定制几何参数.
提供了优化等离子体纳米结构以提高光学性能的见解.

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