玻璃绝缘终端的玻璃掉落检测通过粗细的机器学习框架
Weibo Li1, Bingxun Zeng1, Weibin Li1
1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Micromachines
|January 28, 2026
概括
本研究引入了一种机器学习框架,用于检测玻璃绝缘终端 (GIT) 中的玻璃掉落. 该方法提高了微电子系统中缺陷检测的准确性和速度.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 玻璃绝缘终端 (GIT) 对于高可靠性的微电子系统至关重要.
- 在GIT中的玻璃掉落可能会损害微电子元件和设备的可靠性.
- 由于光反射,不规则的缺陷和有限的数据,自动缺陷检查很困难.
研究的目的:
- 开发一种自动化检查方法,用于检测GIT中的玻璃掉落.
- 为了应对工业环境中自动检测缺陷的挑战.
- 改进微电子元件的可靠性评估.
主要方法:
- 一个粗细的机器学习框架被提议用于玻璃掉落检测.
- 一个适应性部门分区方案利用了GITs的圆环几何.
- 渐变增强决策树 (GBDT) 和部门邻居 (SN) 特性被用于分类.
主要成果:
- 拟议的方法在真实工业GIT图像上实现了高性能.
- 实现了96.85%的欧盟 (IoU) 上的平均交叉点和0.984.1的F1得分.
- 显示了低像素级错误报警率 (0.55%) 和实际检查速度 (32.18秒/图像).
结论:
- 开发的机器学习框架有效地检测了GIT中的玻璃掉落.
- 这种方法比现有的检查方法有了显著的改进.
- 这有助于提高微电子制造业的可靠性和质量控制.
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