Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Defect clusters on the wafer map can provide important clue to identify the process failures so that it is important to accurately classify the defect patterns into corresponding pattern types. In this research, we present an image-based wafer map defect pattern classification method. The presented method consists of two main steps: without any specific preprocessing, high-level features are extracted from convolutional neural network and then the extracted features are fed to combination of error-correcting output codes and support vector machines for wafer map defect pattern classification. To the best of our knowledge, no prior work has applied the presented method for wafer map defect pattern classification. Experimental results tested on 20,000 wafer maps show the superiority of presented method and the overall classification accuracy is up to 98.43%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

CNN:

Convolutional neural networks

ECOC:

Error-correcting output codes

SVM:

Support vector machines

CNN-SVM:

SVM classification based on CNN features

OPTICS:

Ordering point to identify the cluster structure

CART:

Classification and regression trees

NB:

Naive Bayes

kNN:

k-nearest neighbors

ReLU:

Rectified linear unit

LDA:

Linear discriminant analysis

LOGISTIC:

Logistic regression

CNN-ECOC-X:

Use CNN features for ECOC classification where X is used as binary classifiers

ANOVA:

Analysis of variance

SVE:

Soft voting ensemble

References

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A., & Hamed, H. F. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018(1), 97. https://doi.org/10.1186/s13640-018-0332-4.

    Article  Google Scholar 

  • Al-Shargie, F., Tang, T. B., Badruddin, N., & Kiguchi, M. (2018). Towards multilevel mental stress assessment using SVM with ECOC: An EEG approach. Medical & Biological Engineering & Computing, 56(1), 125–136. https://doi.org/10.1007/s11517-017-1733-8.

    Article  Google Scholar 

  • Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). Optics: ordering points to identify the clustering structure. ACM Sigmod Record, ACM, 28, 49–60. https://doi.org/10.1145/304182.304187.

    Article  Google Scholar 

  • Ali Bagheri, M., Montazer, G.A., & Escalera, S. (2012). Error correcting output codes for multiclass classification: application to two image vision problems. In The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012) (pp. 508–513). IEEE. https://doi.org/10.1109/AISP.2012.6313800.

  • Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapnik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2(Dec), 125–137.

    Google Scholar 

  • Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth International Group,. https://doi.org/10.1201/9781315139470.

    Article  Google Scholar 

  • Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, 1–27.

    Article  Google Scholar 

  • Chang, C. W., Chao, T. M., Horng, J. T., Lu, C. F., & Yeh, R. H. (2012). Development pattern recognition model for the classification of circuit probe wafer maps on semiconductors. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2(12), 2089–2097. https://doi.org/10.1109/TCPMT.2012.2215327.

    Article  Google Scholar 

  • Chien, C. F., Chang, K. H., & Wang, W. C. (2014). An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing. Journal of Intelligent Manufacturing, 25(5), 961–972.

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018.

    Article  Google Scholar 

  • Dietterich, T. G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263–286.

    Article  Google Scholar 

  • Ding, C., & Tao, D. (2018). Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 1002–1014. https://doi.org/10.1109/TPAMI.2017.2700390.

    Article  Google Scholar 

  • Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014). Decaf: A deep convolutional activation feature for generic visual recognition. In International Conference on Machine Learning (pp. 647–655).

  • Dorj, U. O., Lee, K. K., Choi, J. Y., & Lee, M. (2018). The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications,. https://doi.org/10.1007/s11042-018-5714-1.

    Article  Google Scholar 

  • Fan, M., Wang, Q., & van der Waal, B. (2016). Wafer defect patterns recognition based on optics and multi-label classification. In IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 912–915). IEEE. https://doi.org/10.1109/IMCEC.2016.7867343.

  • García-Pedrajas, N., & Ortiz-Boyer, D. (2011). An empirical study of binary classifier fusion methods for multiclass classification. Information Fusion, 12(2), 111–130. https://doi.org/10.1016/j.inffus.2010.06.010.

    Article  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2006). Digital Image Processing (3rd ed.). Upper Saddle River, NJ: Prentice-Hall Inc.

    Google Scholar 

  • Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design (Vol. 20). Boston: PWS Pub.

    Google Scholar 

  • Hansen, M. H., Nair, V. N., & Friedman, D. J. (1997). Monitoring wafer map data from integrated circuit fabrication processes for spatially clustered defects. Technometrics, 39(3), 241–253. https://doi.org/10.2307/1271129.

    Article  Google Scholar 

  • Hastie, T., & Tibshirani, R. (1998). Classification by pairwise coupling. In Advances in Neural Information Processing Systems (pp. 507–513). https://doi.org/10.1214/aos/1028144844

  • Huang, F.J., & LeCun, Y. (2006). Large-scale learning with svm and convolutional nets for generic object categorization. In Proceeding of Computer Vision and Pattern Recognition Conference (CVPR’06). https://doi.org/10.1109/CVPR.2006.164

  • Jin, C. H., Na, H. J., Piao, M., Gouchol, P., & Ho, R. K. (2019). A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map. In IEEE Transactions on Semiconductor Manufacturing.

  • Kim, M., Lee, M., An, M., & Lee, H. (2019). Effective automatic defect classification process based on cnn with stacking ensemble model for tft-lcd panel. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01502-y

  • Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. https://doi.org/10.3115/v1/D14-1181.

  • Kong, E.B., & Dietterich, T.G. (1995). Error-correcting output coding corrects bias and variance. In Machine Learning Proceedings (pp. 313–321). Elsevier. https://doi.org/10.1016/B978-1-55860-377-6.50046-3.

  • Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097–1105). https://doi.org/10.1145/3065386.

  • Kyeong, K., & Kim, H. (2018). Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks. In IEEE Transactions on Semiconductor Manufacturing. https://doi.org/10.1109/TSM.2018.2841416.

  • Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019). Automated defect inspection of LED chip using deep convolutional neural network. Journal of Intelligent Manufacturing, 30(6), 2525–2534.

    Article  Google Scholar 

  • Liu, Y. (2006). Using svm and error-correcting codes for multiclass dialog act classification in meeting corpus. In Ninth International Conference on Spoken Language Processing.

  • Lorena, A. C., De Carvalho, A. C., & Gama, J. M. (2008). A review on the combination of binary classifiers in multiclass problems. Artificial Intelligence Review, 30(1–4), 19. https://doi.org/10.1007/s10462-009-9114-9.

    Article  Google Scholar 

  • Matlab. (2018). https://www.mathworks.com/products/matlab.html.

  • Nakazawa, T., & Kulkarni, D. V. (2018). Wafer map defect pattern classification and image retrieval using convolutional neural network. IEEE Transactions on Semiconductor Manufacturing, 31(2), 309–314. https://doi.org/10.1109/TSM.2018.2795466.

    Article  Google Scholar 

  • Nilsson, N. J. (1965). Learning machines. New York: McGrawHill.

    Google Scholar 

  • Niu, X. X., & Suen, C. Y. (2012). A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition, 45(4), 1318–1325. https://doi.org/10.1016/j.patcog.2011.09.021.

    Article  Google Scholar 

  • Ooi, M. P. L., Sok, H. K., Kuang, Y. C., Demidenko, S., & Chan, C. (2013). Defect cluster recognition system for fabricated semiconductor wafers. Engineering Applications of Artificial Intelligence, 26(3), 1029–1043. https://doi.org/10.1016/j.engappai.2012.03.016.

    Article  Google Scholar 

  • Van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music recommendation. In Advances in Neural Information Processing Systems (pp. 2643–2651).

  • Othman, K. M., & Rad, A. B. (2019). An indoor room classification system for social robots via integration of CNN and ECOC. Applied Sciences, 9(3), 470. https://doi.org/10.3390/app9030470.

    Article  Google Scholar 

  • Piao, M., Jin, C. H., Lee, J. Y., & Byun, J. Y. (2018). Decision tree ensemble based wafer map failure pattern recognition based on radon transform based features. IEEE Transactions on Semiconductor Manufacturing, 31(2), 250–257. https://doi.org/10.1109/TSM.2018.2806931.

    Article  Google Scholar 

  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251.

    Article  Google Scholar 

  • Quinlan, J. R. (1993). C4. 5: Programs for machine learning. Amsterdam: Elsevier.

    Google Scholar 

  • Rakhlin, A., Shvets, A., Iglovikov, V., & Kalinin, A. A. (2018). Deep convolutional neural networks for breast cancer histology image analysis. In International Conference Image Analysis and Recognition (pp. 737–744). Springer. https://doi.org/10.1007/978-3-319-93000-8_83.

  • Saqlain, M., Jargalsaikhan, B., & LEE, J. Y. (2019). A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. In IEEE Transactions on Semiconductor Manufacturing. https://doi.org/10.1109/TSM.2019.2904306.

  • Tang, Y. (2013). Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239

  • Wang, C. H. (2008). Recognition of semiconductor defect patterns using spatial filtering and spectral clustering. Expert Systems with Applications, 34(3), 1914–1923. https://doi.org/10.1016/j.eswa.2007.02.014.

    Article  Google Scholar 

  • Wang, C. H., Kuo, W., & Bensmail, H. (2006). Detection and classification of defect patterns on semiconductor wafers. IIE Transactions, 38(12), 1059–1068. https://doi.org/10.1080/07408170600733236.

    Article  Google Scholar 

  • Wu, M. J., Jang, J. S. R., & Chen, J. L. (2015). Wafer map failure pattern recognition and similarity ranking for large-scale data sets. IEEE Transactions on Semiconductor Manufacturing, 28(1), 1–12. https://doi.org/10.1109/TSM.2014.2364237.

    Article  Google Scholar 

  • Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A. (2018). The microsoft 2017 conversational speech recognition system. In International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5934–5938). IEEE. https://doi.org/10.1109/ICASSP.2018.8461870.

  • Xue, D. X., Zhang, R., Feng, H., & Wang, Y. L. (2016). Cnn-svm for microvascular morphological type recognition with data augmentation. Journal of Medical and Biological Engineering, 36(6), 755–764. https://doi.org/10.1007/s40846-016-0182-4.

    Article  Google Scholar 

  • Yu, J., & Lu, X. (2016). Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis. IEEE Transactions on Semiconductor Manufacturing, 29(1), 33–43. https://doi.org/10.1109/TSM.2015.2497264.

    Article  Google Scholar 

  • Yuan, T., Bae, S. J., & Park, J. I. (2010). Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering. The International Journal of Advanced Manufacturing Technology, 51(5), 671–683. https://doi.org/10.1007/s00170-010-2647-x.

    Article  Google Scholar 

  • Zheng, G., Qian, Z., Yang, Q., Wei, C., Xie, L., Zhu, Y., et al. (2008). The combination approach of SVM and ECOC for powerful identification and classification of transcription factor. BMC Bioinformatics, 9(1), 282. https://doi.org/10.1186/1471-2105-9-282.

    Article  Google Scholar 

Download references

Acknowledgements

Most of this work was done when the first author was at BISTel. This work was supported by the World Class 300 Project (R&D) (S2641209, “Development of next generation intelligent Smart manufacturing solution based on AI & Big data to improve manufacturing yield and productivity”) of the MOTIE, MSS(Korea) and supported by the National Natural Science Foundation of China (Grant Nos. 61702324 and 61911540482) in People’s Republic of China.

Author information

Authors and Affiliations

  1. ENN Research Institute of Digital Technology, Beijing, China

    Cheng Hao Jin

  2. BISTel, Seoul, 06754, South Korea

    Hyun-Jin Kim

  3. School of Medicine, Nankai University, Tianjin, China

    Yongjun Piao

  4. College of Information Engineering, Shanghai Maritime University, Shanghai, China

    Meijing Li

  5. Department of Computer Science, Chungbuk National University, Cheongju, 28644, South Korea

    Minghao Piao

Authors
  1. Cheng Hao Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyun-Jin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongjun Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meijing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Minghao Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minghao Piao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, C.H., Kim, HJ., Piao, Y. et al. Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes. J Intell Manuf 31, 1861–1875 (2020). https://doi.org/10.1007/s10845-020-01540-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01540-x

Keywords

Search

Navigation