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无面具灰度激光光刻的数据驱动优化.

Adrian H A Lutey1, David Kuhness2, Seyyedhossein Mckee3

  • 1Dipartimento di Ingegneria dei Sistemi e delle Tecnologie Industriali, Università degli studi di Parma, 43124, Parma, Italy. adrian.lutey@unipr.it.

Scientific reports
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概括
此摘要是机器生成的。

一个新的数据驱动算法通过使用人工神经网络 (ANN) 来纠正地形错误来优化无面膜灰度激光光刻 (MGLL). 这大大提高了制造微光元件的精度.

关键词:
人工神经网络的人工神经网络数据驱动优化的优化.灰度激光光刻法灰度激光光刻法.微光学元件是一种微光学元件.

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 材料科学 材料科学 材料科学
  • 计算科学 计算科学

背景情况:

  • 无面具灰度激光光刻 (MGLL) 面临着地形错误的挑战.
  • 这些错误源于光电阻非线性反应,近距离效应和剂量过渡.

研究的目的:

  • 为MGLL开发一个数据驱动的优化算法.
  • 为了纠正地形错误并提高制造精度.
  • 为了使微光元件的首次正确制造成为可能.

主要方法:

  • 一个人工神经网络 (ANN) 已被开发来模拟MGLL过程.
  • 该ANN使用MGLL虚拟光罩和辐射平均值作为输入来预测表面地形.
  • 通过代预测和纠正地形错误来实现虚拟照片面膜优化.
  • 一个大约100万个数据点的综合数据集被用于ANN培训.

主要成果:

  • 一个优化的ANN架构,具有Sigmoid激活功能和单个隐藏层 (15个神经元),证明了高精度和概括性.
  • 优化算法将实验和目标表面之间的平均欧几里德距离从3.6μm减少到1.0-1.2μm,对于超薄的自由形式微光元件 (FFMO).
  • 这代表了在单个构造中制造精度的显著提高.

结论:

  • 开发的数据驱动算法有效地纠正了MGLL的地形错误.
  • 这种方法为实现高精度,首次正确制造提供了多功能和适应性的途径.
  • 该方法对精确制造微光学元件具有前景.