基于CTM-IYOLOv10网络的晶圆缺陷检测技术
Pengcheng Ji1, Zhenzhi He1, Weiwei Yang2
1School of Mechanical and Electrical Engineering, Jiangsu Normal University, Xuzhou 221116, China.
Journal of imaging
|November 26, 2025
概括
这项研究引入了改进的YOLOv10网络 (CTM-IYOLOv10) 用于半导体晶圆缺陷检测. 新方法提高了准确性和效率,大大改善了智能制造工艺.
科学领域:
- 半导体制造业 半导体制造业
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 半导体设备的缩放增加了晶圆模具的复杂性,需要先进的缺陷检测.
- 传统的检查方法效率低下,容易出现错误,特别是在小缺陷时.
研究的目的:
- 开发一个高效和准确的晶圆缺陷检测框架,用于智能制造.
- 改进现有的深度学习模型,用于检测半导体晶片上的小和不规则的缺陷.
主要方法:
- 集群模板匹配 (CTM) 与改进的YOLOv10网络 (CTM-IYOLOv10) 的集成.
- 实施了修改后的GhostConv模块和增强的BiFPN,以改善特征表示和小物体检测.
- 应用数据增强策略来增强模型的稳定性和概括性.
主要成果:
- CTM-IYOLOv10实现了98.1%的检测准确度,超过了YOLOv5和YOLOv8.
- 与基线YOLOv10.10相比,推断时间减少了23.2%,模型大小减少了52.3%.
- 证明增强了模板细分效率,并减轻了冗余匹配.
结论:
- CTM-IYOLOv10框架在晶圆缺陷检查的准确性,速度和模型大小方面提供了显著的改进.
- 拟议的架构为半导体制造业的实时缺陷检测提供了实用和有效的解决方案.
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