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Updated: Jun 13, 2025

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纳米光子结构反向设计用于使用深度学习切换应用程序.

Ehsan Adibnia1, Majid Ghadrdan1, Mohammad Ali Mansouri-Birjandi2

  • 1Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan (USB), PO Box 9816745563, Zahedan, Iran.

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概括

深度学习加速了纳米级全光学等离子开关的设计. 这种方法有效地解决了反向设计问题,使通信系统和光子集成电路更快.

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科学领域:

  • 光子学和纳米技术的使用.
  • 计算电磁学 计算机电磁学

背景情况:

  • 传统的光学开关设计依赖于代模拟和麦克斯韦方程.
  • 纳米光子设备的反向设计问题是计算密集且耗时的.

研究的目的:

  • 提出一种基于深度神经网络 (DNN) 的方法,用于估计全光开关的光谱传输率.
  • 为了证明深度学习在解决纳米光子反向设计问题的有效性.

主要方法:

  • 开发了一个深度神经网络模型来预测全光开关的光谱传输率.
  • 在正方形共振器中使用非线性克尔效应来展示切换性能.
  • 该DNN模型经过训练,并与传统模拟方法进行了验证.

主要成果:

  • DNN模型实现了高精度,平均平方误差为前向模型的0.03左右,反向模型的0.02.
  • 与传统模拟相比,深度学习方法显著提高了计算效率.
  • 提出的方法有效地解决了纳米光子反向设计的挑战,没有经验战略.

结论:

  • 深度学习为设计全光等离子开关提供了强大而高效的工具.
  • 开发的方法有助于将先进的光学开关集成到光子集成电路中.
  • 这一进步为全光信号处理和通信系统的发展带来了巨大的潜力.