Data Driven Wafer Pattern Defect Pattern Recognition Method

被引：1
|
作者：
Yang Z. [1 ]
Wang J. [1 ]
Zhang J. [1 ]
Jiang X. [1 ]
机构：
[1] College of Mechanical Engineering, Donghua University, Shanghai
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2019年 / 30卷 / 02期
关键词：
Data driven; Generative adversarial network; Pattern recognition; Semiconductor manufacturing; Wafer defect;
D O I：
10.3969/j.issn.1004-132X.2019.02.015
中图分类号：
学科分类号：
摘要：
Aiming at the characteristics of wafer map data angle and dimension diversity and quantity imbalance during wafer production processes, a wafer pattern defect recognition method was proposed based on generative adversarial networks. The two-stage wafer defect data pre-processing method was proposed to obtain standard wafer defect data, where Radon transform was designed to solve the multi-angle characteristics of the wafer map, and a resampling mechanism was used to realize the scaling of various data dimensions. The proposed wafer defect classification method used a generation mechanism to balance the number of samples of each defect type based on a generative adversarial networks, which could improve the defect pattern recognition accuracy. The experimental results show that this method may greatly improve the accuracy of small class samples, and the overall recognition rate is much better than the support vector machine and Adaboost algorithm. © 2019, China Mechanical Engineering Magazine Office. All right reserved.
引用
收藏
页码：230 / 236
页数：6
相关论文
共 13 条
  • [1] Wang J., Qin W., Zhang J., Data Mining for Orders' LT Forecasting in Wafer Fabrication, China Mechanical Engineering, 27, 1, pp. 105-108, (2016)
  • [2] Hansen M.H., Nair V.J., Friedman D.J., Monitoring Wafer Map Data from Integrated Circuit Fabrication Process for Spatially Clustered Defects, Technometrics, 39, 3, pp. 241-253, (1997)
  • [3] Kaempf U., The Binomial Test: a Simple Tool to Identify Process Problems, IEEE Transactions on Semiconductor Manufacturing, 8, 2, pp. 160-166, (1995)
  • [4] Friedman D.J., Hansen M.H., Nair V.N., Et al., Model-free Estimation of Defect Clustering in Integrated Circuit Fabrication, IEEE Transactions on Semiconductor Manufacturing, 10, 3, pp. 344-359, (1997)
  • [5] Haralick R.M., Shapiros L.G., Computer and Robot Vision, pp. 28-48, (1993)
  • [6] Tan Y.S., Zhou X.M., Research on the Statistical Characteristics of the Unipixel Gray Value of Natural Images, Computer Engineering & Science, 26, 3, pp. 42-46, (2004)
  • [7] Gonzalez R.C., Woods R.E., Digital Image Processing, (2008)
  • [8] Wu M.J., Jang J.S.R., Chen J.L., Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-scale Data Sets, IEEE Transactions on Semiconductor Manufacturing, 28, 1, pp. 1-12, (2015)
  • [9] Fang X., Shi J., Convolutional Neural Networks for Wafer Defect Detection and Classification, Computer Engineering, pp. 1-5
  • [10] Goodfellow I.J., Pouget-Abadie J., Mirza M., Et al., Generative Adversarial Networks, Advances in Neural Information Processing Systems, 3, pp. 2672-2680, (2014)
12