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基于半导体制造工艺的机器学习数据预处理研究.

Ha-Je Park1, Yun-Su Koo2, Hee-Yeong Yang1

  • 1Department of Software Convergence Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea.

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概括
此摘要是机器生成的。

这项研究通过使用机器学习来预测产品缺陷来优化半导体制造. 最好的模型结合了支持向量机 (SVM) 与ADASYN过量采样和MaxAbs缩放,以提高产量和降低成本.

关键词:
在SECOM的数据集中,几何平均值的几何平均值机器学习是机器学习.过量采样过量采样半导体制造工艺 半导体制造工艺

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科学领域:

  • 半导体制造业 半导体制造业
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 半导体制造从各种传感器中产生多样化,不平衡的数据.
  • 数据预处理对于准确的产量预测和成本降低至关重要.
  • 现有的方法经常与不平衡的数据集和漫长的处理时间作斗争.

研究的目的:

  • 评估机器学习分析和预处理技术,用于预测半导体产品质量.
  • 通过改进缺陷预测,提高产品产量并降低制造成本.
  • 为了应对数据挑战,如多样化的单位,不平衡的类和高维度.

主要方法:

  • 使用SECOM数据集进行分析.
  • 应用的预处理技术:缺失值赋值,维度减小,重新采样 (过量采样/不足采样) 和特征缩放.
  • 实施并比较了六个机器学习模型,重点是用于评估的几何平均值 (GM).
  • 通过早期列车/测试组分离来防止数据泄露,确保数据完整性.

主要成果:

  • 除KM SMOTE外的过量采样方法有效地平衡了数据集类别.
  • 支持矢量机 (SVM),自适应合成采样 (ADASYN) 和MaxAbs缩放的组合实现了最高的性能.
  • 最佳模型的准确率为85.14%,几何平均值为72.95%.

结论:

  • 拟议的预处理和机器学习方法显著改善了半导体制造中的缺陷预测.
  • 功能减少技术缩短了训练和预测时间,提高了实际应用性.
  • 这项研究证明了SVM与ADASYN和MaxAbs缩放对不平衡的半导体数据的有效性.