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基于metasurface的图像分类使用衍射深度神经网络.

Kaiyang Cheng1, Cong Deng1, Fengyu Ye1

  • 1International School of Microelectronics, Dongguan University of Technology, Dongguan 523808, China.

Nanomaterials (Basel, Switzerland)
|November 26, 2024
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概括
此摘要是机器生成的。

这项研究介绍了一个衍射深度神经网络 (D2NN),使用全介电元面进行光子计算. 这种人工智能驱动的方法在手写数字分类中达到90%以上的准确性,使光学计算更快,更准确.

关键词:
衍射式深度神经网络是一种神经网络.图像的分类图像的分类.metasurfaces 是一个地表.

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科学领域:

  • 光子学和人工智能的人工智能
  • 基于地表光学计算的Metasurface.

背景情况:

  • 传统的光子计算在制造和数据驱动应用的灵活性方面面临限制.
  • 人工智能算法加速光子计算的发展,但需要可适应的调制方法.

研究的目的:

  • 为光学计算提出一种新的衍射深度神经网络 (D2NN) 框架.
  • 为了展示一个灵活的,全介电的光调节元面.
  • 使用光子神经网络在图像分类任务中实现高精度.

主要方法:

  • 开发了一个D2NN框架,采用三层全介电相位传输阵列.
  • 工程纳米盘元原子控制相位配置,并保持高传导率 (0.9在600nm).
  • 模拟了一个完全连接的神经网络,使用每层1024个单位的仅相位元表面.

主要成果:

  • 在使用D2NN框架对手写数字 ('0'至'5') 的分类中,获得了90%以上的准确性.
  • 通过全波模拟验证了性能.
  • 通过增加网络连接,证明了更复杂的动物图像的成功分类.

结论:

  • 拟议的D2NN框架为光子计算中的灵活光调制提供了一个可行的解决方案.
  • 这种全光学计算方法显示出在图像处理和机器视觉方面的实际应用潜力.
  • 基于metasurface的神经网络为高效和紧的光学计算提供了一条道路.