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Clustering Ensemble for Identifying Defective Wafer Bin Map in Semiconductor Manufacturing

机译：用于识别半导体制造中缺陷晶圆仓图的聚类集成

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摘要

Wafer bin map (WBM) represents specific defect pattern that provides information for diagnosing root causes of low yield in semiconductor manufacturing. In practice, most semiconductor engineers use subjective and time-consuming eyeball analysis to assess WBM patterns. Given shrinking feature sizes and increasing wafer sizes, various types of WBMs occur; thus, relying on human vision to judge defect patterns is complex, inconsistent, and unreliable. In this study, a clustering ensemble approach is proposed to bridge the gap, facilitating WBM pattern extraction and assisting engineer to recognize systematic defect patterns efficiently. The clustering ensemble approach not only generates diverse clusters in data space, but also integrates them in label space. First, the mountain function is used to transform data by using pattern density. Subsequently, k-means and particle swarm optimization (PSO) clustering algorithms are used to generate diversity partitions and various label results. Finally, the adaptive response theory (ART) neural network is used to attain consensus partitions and integration. An experiment was conducted to evaluate the effectiveness of proposed WBMs clustering ensemble approach. Several criterions in terms of sum of squared error, precision, recall, and F-measure were used for evaluating clustering results. The numerical results showed that the proposed approach outperforms the other individual clustering algorithm.
机译：晶圆仓图（WBM）表示特定的缺陷图案，该图案提供了用于诊断半导体制造中低良率的根本原因的信息。实际上，大多数半导体工程师使用主观且费时的眼球分析来评估WBM模式。给定缩小的特征尺寸和增加的晶圆尺寸，会出现各种类型的WBM；因此，依靠人类的视觉来判断缺陷模式是复杂，不一致和不可靠的。在这项研究中，提出了一种聚类集成方法来弥合差距，促进WBM模式提取，并协助工程师有效地识别系统缺陷模式。聚类集成方法不仅可以在数据空间中生成各种聚类，还可以将它们集成到标签空间中。首先，山函数用于通过使用图案密度来转换数据。随后，使用k均值和粒子群优化（PSO）聚类算法生成分集分区和各种标记结果。最后，使用自适应响应理论（ART）神经网络实现共识划分和整合。进行了一项实验，以评估所提出的WBM聚类集成方法的有效性。在平方误差总和，精度，召回率和F度量方面，使用了多个标准来评估聚类结果。数值结果表明，所提方法优于其他个体聚类算法。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第12期|707358.1-707358.11|共11页
  • 作者

    Hsu Chia-Yu;

  • 作者单位

    Yuan Ze Univ, Innovat Ctr Big Data & Digital Convergence, Taoyuan 32003, Taiwan.;

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