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Atomic Force Microscopy

Atomic Force Microscopy

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...

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Updated: May 31, 2025

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

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Published on: July 5, 2016

基于机器学习配置的可变形镜面的自由形表面测量动态干涉计.

Xu Chang¹, Yao Hu², Jintao Wang¹

¹Institute of Mechanics and Acoustics Metrology, National Institute of Metrology, Beijing 100029, China.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

本研究介绍了一种使用机器学习配置的可变形镜 (DM) 来实时测量光学自由形表面的动态干扰计方法. 这种方法提高了动态表面计量学的效率和准确性.

关键词:

动态干扰测量是一种动态干扰测量.表面自由形状的表面.机器学习是机器学习.

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Last Updated: May 31, 2025

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Published on: July 5, 2016

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科学领域:

光学工程是指光学工程.
计量学计量学计量学
机器学习应用机器学习应用

背景情况:

光学自由形表面提供了高度的设计自由,但在制造和组装的动态测量方面存在挑战.
在自由形表面制造过程中实时反对于指导后续操作至关重要.

研究的目的:

为自由形表面开发动态干扰度测量方法.
为应对自由形式表面制造和组装中实时计量学的挑战.

主要方法:

开发了一种采用同轴结构和极化干扰的动态干扰度系统.
一个机器学习配置的可变形镜 (DM) 用于暂时相调节.
该系统包含DM的暂时监控,以减轻由于表面变化而导致的准确性损失.

主要成果:

拟议的方法允许自由形表面的短暂测量,满足动态要求.
基于机器学习的DM配置比传统的代方法更有效.
实验验证证了动态干扰计方法的可行性和有效性.

结论:

开发的动态干扰测量方法为测量动态自由形表面提供了有效的解决方案.
这项研究为在先进的光学制造中应用动态干扰度提供了基础.
机器学习用于DM配置的使用显著提高了测量效率和适应性.