Defect Pattern Recognition (DPR) of wafer maps is crucial to find the root cause of the issue and further improving the . Testing Open Defects in Memristor-Based Memories. (2021). Alberti, M., Seuret, M., Ingold, R., & Liwicki, M. (2017, December 17). Momentum Contrast for Unsupervised Visual Representation Learning. Preil, M. E. (2016). The defect recognition and classification methods are introduced and analyzed for discussion on their respective advantages, limitations, and scalability. Discriminative feature learning and cluster-based defect label reconstruction for reducing uncertainty in wafer bin map label. In this paper, we also investigated whether the training time can be reduced by using the transfer learning technology on pretrained deep learning neural networks. SVM is a well-known supervised classifier that performs by separating classes using hyper-planes, which are called support vectors. A systematic literature review (SLR) has been conducted to determine how the semiconductor industry is leveraged by deep learning research advancements for wafer defects recognition and analysis. The F-measure is a trade-off between these two, and its value is high when the predicted results and the actual results are close to each other. A comprehensive survey of clustering algorithms. Kaempf, U.; Ulrich, K. The binomial test: A simple tool to identify process problems. https://doi.org/10.1016/j.engappai.2012.11.009, Luo, Y., Yin, L., Bai, W., & Mao, K. (2020). Metallization: mainly to perform the connections of metals. The future challenges and trends of wafer map detection research are also presented. Conceptualization, analysis, validation, writing, J.-C.C. In this paper, a multi-level semantic method based on residual adversarial learning with Wasserstein divergence is proposed to realize sample augmentation and automatic classification . https://doi.org/10.1109/tsm.2020.2994357, Saqlain, M., Jargalsaikhan, B., & Lee, J. Y. In Proceedings of the Desig, Automation & Test in Europe Conference & Exhibition, Dresden, Germany, 2428 March 2014; pp. Inspection and Classification of Semiconductor Wafer Surface Defects Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis. A., Liu, S., Hochbaum, D. S., & Ding, Y. Madison, WI. Enhanced Deep Convolutional Neural Network for Identifying and Taha, K., Salah, K., & Yoo, P. D. (2018). Defect cluster recognition system for fabricated semiconductor wafers. Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN. (2020). IEEE Transactions on Semiconductor Manufacturing, 13(3), 366373. Byun, Y., & Baek, J. G.(2020). Bayesian optimisation for constrained problems. Author to whom correspondence should be addressed. Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition. You do not need to apply random augmentations to validation or test data. The definition of precision, recall and F-measure is [, The AUC (area under the roc curve) measure is the area under the ROC curve [, That shows the degree of reparability within the same class. (2020). (2019). An appraisal of incremental learning methods. Degree Program of Digital Space and Product Design, Kainan University, Taoyuan 33587, Taiwan, Department of Electrial Engineering, Chang Gung University, Taoyuan 33302, Taiwan, Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan, Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan. Semi-supervised classification of wafer map based on ladder network. https://doi.org/10.1109/tit.1962.1057692, Hu, H., He, C., & Li, P. (2021). https://doi.org/10.1109/TSM.2014.2364237. Neural Optimizer Search with Reinforcement Learning. 537541). These metrics are defined for a binary classification. Automatic Defect Detection from SEM Images of Wafers using Component Tree. GAMM-Mitteilungen. GAN-based statistical modeling with adaptive schemes for surface defect [6] A Deep Learning Model for Identication of Defect Patterns in Semiconductor Wafer Map, Yang, Yuan-Fu, ASMC 2019 [7] Deep-Structured Machine Learning Model for the Recognition of Mixed-Defect Patterns in Semiconductor Fabrication Processes, Ghalia Tello, Omar Y. Al-Jarrah, Paul D. Yoo , Yousof Al-Hammadi, Sami Muhaidat, and In 2019 IEEE 5th International Conference on Computer and Communications (ICCC). Semi-supervised wafer map pattern recognition using domain-specific data augmentation and contrastive learning. Saqlain, M.; Jargalsaikhan, B.; Lee, J.Y. [4] T., Bex. (2015). https://doi.org/10.1109/ieem44572.2019.8978568, Shi, X., Yan, Y., Zhou, T., Yu, X., Li, C., Chen, S., & Zhao, Y. 4, pp. Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets. IEEE Transactions on Semiconductor Manufacturing 28, no. Available online: Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Semiconductor Manufacturing, 28(1), 112. Web browsers do not support MATLAB commands. ; Tsai, M.Y. This example uses only labeled images. [. Remove these fields from the structure using the rmfield function. IEEE Transactions on Semiconductor Manufacturing, 33(4), 635643. Qualitative and quantitative analysis of multi-pattern wafer bin maps. Define the convolutional neural network architecture. Specify the input size for the network. (2019). CiDaS 2019. Our search criteria are only limited to semiconductor process domain. Wafer defect inspection is being conducted passively by experts. IEEE Transactions on Information Theory, 8(2), 179187. Journal of Micro/nanolithography, MEMS, and MOEMS, 19(02), 1. https://doi.org/10.1117/1.jmm.19.2.024801. https://doi.org/10.1016/j.cie.2020.106358, Hu, M. (1962). Hidden wafer scratch defects projection for diagnosis and quality enhancement. Semiconductor engineers are able to use the defect patterns on the wafers to locate problems in the process, which would then become clues in helping improve the yield. https://doi.org/10.1109/EnT47717.2019.9030550. https://doi.org/10.1109/tsm.2019.2902657, Chien, C.-F., Hsu, S.-C., & Chen, Y.-J. 13). Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. In Proceedings of 34th International Conference on Machine Learning (pp. In Proceedings of the 1993 IEEE International Workshop on Memory Testing, San Jose, CA, USA, 910 August 1993; pp. In A. Rocha, L. Steels, & J. van den Herik (Eds. https://doi.org/10.1109/cvprw.2014.79, Wang, Y., & Ni, D. (2019). https://doi.org/10.1613/jair.1.12125, Ooi, M.P.-L., Sok, H. K., Kuang, Y. C., Demidenko, S., & Chan, C. (2013). (2009). 974979). Entropy, 22(11), 1190. https://doi.org/10.3390/e22111190, Maksim, K., Kirill, B., Eduard, Z., Nikita, G., Aleksandr, B., Arina, L., Vladislav, S., Daniil, M., & Nikolay, K. (2019). 8488. Deep learning-based detection, classification, and localization of defects in semiconductor processes. Inspection and classification of semiconductor wafer surface defects using CNN deep learning networks. Classification of wafer maps defect based on deep learning methods with small amount of data. Puggini, L.; Doyle, J.; McLoone, S. Fault Detection using Random Forest Similarity Distance. Classification of defect clusters on semiconductor wafers via the hough transformation. In Proceedings of the Advanced Semiconductor Manufacturing Conference and Workshop, Boston, MA, USA, 2325 September 1998; Volume 4, pp. A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map. You cannot create an augmented image datastore from data in a structure, but you can create the datastore from data in a table. A Voting Ensemble Classifier for Wafer Map Defect Patterns Identification in Semiconductor Manufacturing. The pretrained faster-R-CNN models used in this experiments were trained on two different datasets: COCO and KITTI. IEEE Transactions on Instrumentation and Measurement, 69(12), 96689680. IEEE Transactions on Semiconductor Manufacturing, 31(2), 309314. (2018). Chien, J.-C.; Wu, M.-T.; Lee, J.-D. PDF Deep-Learning Based Classication Models for Wafer Defective Pattern Deep Learning Toolbox This example shows how to classify eight types of manufacturing defects on wafer maps using a simple convolutional neural network (CNN). (rep.). (2016). This measurement works the best when the number of classes is few. The data set consists of 811,457 wafer maps images, including 172,950 labeled images. CVPR 2022 Open Access Repository Diffusion and ion implantation: to use physical phenomena of heat diffusion to alter the semiconductors electrical conductivity, then ionize the surface substance, then control the electrical current magnitude to control the concentrations of ions. Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. future research directions and describes possible research applications. J Intell Manuf (2022). 2). 15: 5340. In this paper, two ways to use the deep learning convolution neural networks to classify semiconductor defect images were presented. https://doi.org/10.1109/tsm.2014.2364237, Xu, D., & Tian, Y. In D. Macdonald & S. A. Kalogirou (Eds. https://arxiv.org/abs/2105.13245, Wang, C.-H. (2009). Weld Defect Monitoring Based on Two-Stage Convolutional - Springer A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. https://doi.org/10.1109/tsm.2018.2841416, Lee, H., & Kim, H. (2020). https://doi.org/10.1109/tsm.2015.2497264, Yu, N., Xu, Q., & Wang, H. (2019a). https://doi.org/10.1109/IJCNN.2012.6252800, Saqlain, M., Abbas, Q., & Lee, J. Y. https://doi.org/10.1109/TII.2015.2481719, Article You can use the gradCAM (Deep Learning Toolbox) function to identify parts of the image that most influenced the network prediction. In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 912915. A Defect Detection Model for Imbalanced Wafer Image Data Using CAE and Comparisons will be made with other machine-learning-based classifiers presented in the literatures: SVM [. Inspection and Classification of Semiconductor Wafer Surface Defects Using CNN Deep Learning Networks. In Proc. Kaggle SM-811K Wafer Map. Dong, H.; Chen, N.; Wang, K. Wafer yield prediction using derived spatial variables. https://doi.org/10.1007/s40745-015-0040-1, Yu, J. Zenodo. Wafer bin map inspection based on DenseNet | SpringerLink Jang, J., Seo, M., & Kim, C. O. Decision tree ensemble-based wafer map failure pattern recognition based on radon transform-based features. See further details. Multiple requests from the same IP address are counted as one view. The higher the AUC value implies that the classifier is better at predicting each label class or distinguishing between different defect classes. IEEE Transactions on Semiconductor Manufacturing, 34(1), 916. This function is attached to the example as a supporting file. https://doi.org/10.1109/dac18072.2020.9218580, Alawieh, M. B., Wang, F., & Li, X. https://doi.org/10.1016/j.asoc.2008.10.002, Hwang, J., & Kim, H. (2020). Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Deep learning-based detection, classification, and localization of https://doi.org/10.1109/66.857947, Cheon, S., Lee, H., Kim, C. O., & Lee, S. H. (2019). MathWorks is the leading developer of mathematical computing software for engineers and scientists.