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相关实验视频

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基于深度学习方法改进激光直接写作覆盖精度

Guohan Gao1, Jiong Wang1,2, Xin Liu1

  • 1Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.

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概括
此摘要是机器生成的。

使用卷积神经网络 (CNN) 的深度学习方法显著减少了激光直接写作中的叠加对齐错误. 这种方法通过准确预测坐标计算错误来提高微纳米制造的精度.

关键词:
卷积神经网络深度学习激光直接写作覆盖精度

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科学领域:

  • 光学和光学
  • 机器学习
  • 纳米技术

背景情况:

  • 在微纳米制造的激光直接写作系统中,重叠对齐的精度至关重要.
  • 调整错误源于光学偏差,机械漂移和信托标记的缺陷.
  • 视觉系统对信托标记坐标的解释对剩余对齐错误有很大影响.

研究的目的:

  • 开发基于深度学习的方法来提高叠加对齐的精度.
  • 创建一个卷积神经网络 (CNN) 模型,用于预测信托标记中的坐标计算错误.
  • 将CNN与传统的前神经网络 (FNN) 的性能进行比较.

主要方法:

  • 创建了66000个计算机模拟的错误标记以模仿现实世界的信托标记缺陷.
  • 开发和比较了14个神经网络架构,包括8个CNN变体和6个FNN配置.
  • 使用生成的数据集训练和验证模型以评估预测的准确性.

主要成果:

  • 一个简单的CNN架构实现了0.0011 (培训) 和0.0016 (验证) 的平均平方误差 (MSE).
  • 与FNN结构相比,CNN的错误减少了90%.
  • 实验结果显示,在X/Y坐标下,CNN的预测误差低于100nm,其性能优于FNN.

结论:

  • 深度学习,特别是CNN提供了一种强大的方法来提高激光直接写作中的叠加对齐精度.
  • 美国广播公司在本地特征提取和翻译不变性方面的能力是其卓越表现的关键.
  • 这种方法为微纳米光学设备制造的精度增强奠定了新的范例.