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基于模拟的机器学习方法,用于高速纠正连贯扫描干涉测量中的依赖斜率的误差.
Optics express
|November 29, 2023
概括
一种新的机器学习方法使用深度神经网络来纠正连贯扫描干扰计 (CSI) 测量中的依赖斜率的错误. 这种方法显著加快了复杂的工程表面的纠错过程.
科学领域:
- 计量学 计量学 计量学
- 光学工程是指光学工程.
- 机器学习应用 机器学习应用
背景情况:
- 在测量复杂工程表面的连贯扫描干扰测量 (CSI) 中,依赖斜率的误差是一个重大挑战.
- 现有的校正方法,比如3D表面转移函数的相位逆转,由于CSI仪器非移动不变性,因此在计算上是密集的.
研究的目的:
- 引入一种新的,高效的机器学习方法来纠正CSI测量中的依赖斜率的错误.
- 显著减少CSI中纠正错误所需的计算时间.
主要方法:
- 用经过验证的基于物理的虚拟CSI方法模拟的表面测量训练了一个深度神经网络.
- 网络直接学习错误特征以执行依赖斜率的错误校正.
主要成果:
- 经过训练的深度神经网络在0.1秒内纠正了1024x1024的表面高度图.
- 取得的准确性与传统的相位逆转方法相似.
- 新方法在相同的计算条件下比以前的方法快两倍.
结论:
- 机器学习,特别是深度神经网络,为CSI中依赖斜率的错误纠正提供了高效的解决方案.
- 这种方法大大提高了具有复杂几何形状的功能工程表面的测量效率.
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