使用U形共振器对基于法诺共振的等离子体折射率传感器进行机器学习分析
Shiva Khani1, Pejman Rezaei2, Mohammad Rahmanimanesh2
1Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. shiva.khani@semnan.ac.ir.
Scientific reports
|July 4, 2025
概括
本研究介绍了一种基于法诺共振 (FR) 的等离子体传感器,用于精确测量折射率 (RI). 机器学习集成大大减少了模拟时间和资源,以提高RI传感性能.
科学领域:
- 塑制剂是一种塑制剂.
- 纳米光子学 纳米光子学
- 光学传感传感器是什么?
背景情况:
- 等离子传感器提供紧的足迹和高灵敏度的折射率 (RI) 测量.
- 在等离子纳米结构中的法诺共振 (FR) 提供了敏感RI检测的基础.
研究的目的:
- 设计和分析一款基于法诺共振的新型折射率传感器.
- 用等离子体纳米结构来研究传感器的性能.
- 整合机器学习进行预测分析和资源优化.
主要方法:
- 使用金属绝缘体金属波导与U形和反转U形共振器.
- 使用有限差异时间域 (FDTD) 方法进行传输频谱分析.
- 应用了极端随机树回归模型来进行传导率预测.
主要成果:
- 获得了571.4nm/RIU的高灵敏度和第一个FR的14987 RIU-1的优点数字.
- 极端随机树模型在预测传输值方面表现出高精度 (Adj-R2S接近1).
- 通过机器学习集成,减少了90%的模拟时间和资源.
结论:
- 设计的等离子体传感器在RI传感方面表现出色.
- 机器学习集成增强了预测能力和计算效率.
- 该传感器有可能用于先进的RI传感应用.
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