基于背景减法和更快的R-CNN的晶圆表面缺陷检测
1School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
Micromachines
|May 27, 2023
概括
一种新的晶圆表面缺陷检测方法使用背景减法和更快的R-CNN来提高准确性. 这种方法通过将缺陷与背景噪声分开来加强缺陷识别,提高制造质量.
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 计算机视觉
- 图像处理 图像处理
背景情况:
- 晶圆表面缺陷由于与背景的相似性而难以检测.
- 自动缺陷检测对于智能制造至关重要.
研究的目的:
- 提出一种新的晶圆表面缺陷检测方法.
- 为了提高缺陷检测的准确性和可靠性.
主要方法:
- 改进了用于图像周期测量的光谱分析.
- 局部模板匹配用于亚结构图像重建.
- 通过图像差异化进行背景减去.
- 使用改进的更快的R-CNN网络进行检测.
主要成果:
- 提出的方法有效地重建了背景图像.
- 显著减少了背景干扰.
- 改进的Faster R-CNN实现了更高的检测准确度.
- 与原来的Faster R-CNN相比,平均平均精度 (mAP) 增加了5.2%.
结论:
- 开发的方法准确地检测晶圆表面缺陷.
- 它满足智能制造对高检测精度的要求.
- 这种方法为半导体质量控制提供了一个强大的解决方案.
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