深度学习辅助的非接触式缺陷识别方法使用衍射相位显微镜
Applied optics
|September 14, 2023
概括
本研究引入了一种深度学习方法,可靠地检测光学边缘模式中的缺陷,用于非破坏性测试. 该方法使用相梯度来准确地定位干扰计量计量学中的缺陷.
科学领域:
- 光学计量学 在光学计量学
- 非破坏性测试是指非破坏性测试.
- 人工智能的人工智能
背景情况:
- 光学边缘图案对于非破坏性测试至关重要.
- 检测这些模式中的缺陷是一个重大挑战.
- 目前的方法可能缺乏稳定性和精度.
研究的目的:
- 开发一种基于深度学习的方法,用于准确识别边缘模式缺陷.
- 使用相梯度信息来改善缺陷定位.
- 在各种条件下证明方法的有效性.
主要方法:
- 使用深度学习模型计算空间相导数.
- 缺陷信息归因于边缘图案的相梯度.
- 由此产生的梯度图被用于缺陷定位.
主要成果:
- 该方法在不同噪声级别的数值合成缺陷上证明了稳定性.
- 使用衍射相位显微镜成功实现了试验性缺陷识别.
- 深度学习方法有效地局部化了边缘模式中的缺陷.
结论:
- 拟议的深度学习方法为边缘模式缺陷检测提供了可靠的解决方案.
- 通过深度学习进行相梯度分析,可以提高缺陷定位的准确性.
- 该技术在实验计量学应用中显示出实际实用性.
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