基于优化DBSCAN聚类算法的晶圆图预处理研究
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作者单位:

1.江苏大学机械工程学院,桂林电子科技大学电子工程与自动化学院;2.桂林电子科技大学;3.江苏大学

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on Wafer Map Preprocessing Based on Optimized DBSCAN Clustering Algorithm
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Affiliation:

1.School of Mechanical Engineering, Jiangsu University;2.School of Electronic Engineering and Automation, Guilin University of Electronic Technology

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    摘要:

    晶圆图是由半导体生产过程中的对晶圆进行可测试性检测而得到的,通过对晶圆图进行分类可以为生产过程中出现的问题提供依据,从而解决问题,降低生产成本。对晶圆图进行分类之前,最重要的就是特征提取,晶圆图除了本身有一定的空间图案以外,还存在着很多的噪声,影响着特征提取的过程。传统的DBSCAN算法用于滤波,需要人为确定两个参数,最小邻域Eps和最小点数MinPts,参数的选择直接影响了聚类的准确性。因此,本文提出了一种基于优化DBSCAN聚类算法的滤波方式,自动确定DBSCAN的参数,可以解决传统的手动设定参数的弊端。该算法基于参数自动寻优策略,选取DBSCAN聚类后的簇内密度参数、簇间密度参数的综合指标来评定最优参数。实验结果表明,该算法能自动并合理的选择较好的参数,具有很好的聚类效果,对后续的特征提取及分类也有很大的帮助。

    Abstract:

    The wafer map is obtained by testing each die in the wafer during semiconductor production for defects and marking the defective die. The classification of the wafer map can provide a basis for problems that occur in the production process, thereby solving the problems and reducing the cost. Before classifying the wafer map, the most important thing is feature extraction. In addition to a certain spatial pattern, the wafer map also has a lot of noise, which affects the process of feature extraction. When the traditional DBSCAN algorithm is used for filtering, it is necessary to manually determine the value of Eps and MinPts parameters, and the selection of the parameters directly affects the accuracy of the clustering. Therefore, this paper proposes a filtering method based on the optimized DBSCAN clustering algorithm to automatically determine the parameters of DBSCAN, which can solve the traditional drawbacks of manually parameters setting. This method selects a comprehensive index of cluster intra-cluster density and inter-cluster density to evaluate the optimal parameters. The experimental results show that the algorithm proposed in this paper can automatically and reasonably select better parameters and has a good clustering effect, which is also very helpful for subsequent feature extraction and classification.

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历史
  • 收稿日期:2020-06-11
  • 最后修改日期:2021-07-09
  • 录用日期:2020-09-25
  • 在线发布日期: 2020-11-01
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