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相关概念视频

Atomic Force Microscopy01:08

Atomic Force Microscopy

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Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
3.6K

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相关实验视频

Updated: Sep 11, 2025

Control of Cell Geometry through Infrared Laser Assisted Micropatterning
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使用自适应区域选择和强化学习的自动对焦控制,应用在晶圆微图像中.

Ruoyu Wang, Tundong Liu

    Optics express
    |August 13, 2025
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    概括

    我们开发了一种强化学习 (RL) 方法,用于在晶圆微图像中个性化自动对焦控制. 这种方法通过学习特定区域的最佳焦距来提高焦点质量,提高图像清晰度.

    科学领域:

    • 半导体制造业 半导体制造业
    • 计算机视觉 计算机视觉
    • 人工智能的人工智能

    背景情况:

    • 晶圆微图像需要精确的自动对焦,但不同区域的焦距不一致构成了挑战.
    • 现有的自动对焦算法往往缺乏对各种晶圆检查领域的概括性.

    研究的目的:

    • 提出一种通用强化学习 (RL) 方法,用于在晶圆微图像中个性化自动对焦控制.
    • 为了提高聚焦质量,并消除晶圆检查中的手动调整.

    主要方法:

    • 在RL框架内开发了一个深度网络,以估计图像的焦距.
    • 利用工程师反来微调个性化模型,预测对感兴趣区域的最佳焦距.
    • 在政策网络更新中使用高斯政策梯度算法.

    主要成果:

    • 为培训和验证创建了一个晶圆图像数据集.
    • 拟议的网络在不同晶圆区域中表现出更好的泛化.
    • 与现有方法相比,在聚焦质量方面取得了大约4.0%的平均改善.

    结论:

    • 一般化的RL方法有效地解决了晶圆微图像中的不一致的焦距.

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  • 个性化的自动对焦控制提高了成像质量,减少了手动干预的需要.
  • 为推进晶圆微图像技术提供了新的见解.