相关实验视频

Updated: Sep 13, 2025

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Published on: June 7, 2019

用深度学习驱动的光谱探索金属-绝缘体-金属-等离子金属元表面

Wei Tao, Liang Shen, Yuyang Wu

|July 30, 2025

概括

深度学习通过整合实验数据和模拟来加快金属-绝缘体-金属 (MIM) 超表面的分析. 这种框架可以为先进的光子应用程序快速表征和反向设计.

科学领域:

塑学和纳米光子学
材料科学材料科学材料科学
机器学习应用机器学习应用

背景情况:

金属-绝缘体-金属 (MIM) 超表面由于合的等离子和腔模式而显示复杂的光谱反应.
传统的表征方法对于复杂的参数空间和不同的实验条件是低效的.

研究的目的:

开发一个深度学习框架,用于分析MIM超表面光谱行为.
整合实验制造,模拟和数据驱动分析,以实现高效的表征和反向设计.

主要方法:

使用电子束光刻法制造具有不同几何形状和周期性的梯度参数MIM元表面.
在反射/传输配置下的光学特征.
应用深度学习模型 (CNN,LSTM,GRU,变压器) 用于光谱分类和回归.

主要成果:

24个结构类别的稳健分类,准确度高 (>99.2%的LSTM).
对反向设计的高效光谱回归的演示.
识别LSTM模型在分类和CNN在时间效率方面的优越性能.

结论:

建立了一个数据物理集成的范式,用于快速MIM地表表表征.
该方法将复杂的光学响应与深度学习相结合,用于光谱探索.
该框架促进了无标签生物传感和可调节的光子系统的应用.

更多相关视频

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

Published on: July 21, 2018

相关概念视频

Biasing of Metal-Semiconductor Junctions

Biasing of Metal-Semiconductor Junctions

Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...