wafer defect inspection deep learning

https://doi.org/10.1109/tsm.2008.2000269, Wu, M.-J., Jang, J.-S.R., & Chen, J.-L. (2015). In 2020a IEEE International Conference on Consumer Electronics (ICCE). https://doi.org/10.3390/app10155340, Chien, Jong-Chih, Ming-Tao Wu, and Jiann-Der Lee. Defect cluster recognition system for fabricated semiconductor wafers. Visualize the confusion matrix using the confusionchart (Deep Learning Toolbox) function. https://doi.org/10.1109/tsm.2018.2825482, Tsai, T.-H., & Lee, Y.-C. (2020a). 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. Ruthotto, L., & Haber, E. (2021). Wafer defect patterns recognition based on OPTICS and multi-label classification. methods, instructions or products referred to in the content. China Semiconductor Technology International Conference (CSTIC), 2020, 13. Use the rocmetrics function to calculate the precision, recall, and AUC for each class over a range of thresholds. IEEE Transactions on Semiconductor Manufacturing, 33(4), 578586. As different process flows can lead to different defect types, the correct identification of defect patterns is important for recognizing manufacturing problems and fixing them in good time. A Light-Weighted CNN Model for Wafer Structural Defect Detection. (2019). [3] Selvaraju, Ramprasaath R., Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. In 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Chapter III-1-A - Characterization and Diagnosis of Silicon Wafers, Ingots, and Solar Cells. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. To achieve high precision identification of wafer defects and improve the quality and production yield of wafers, this paper proposes . Finally, the validation results of the proposed CNN model will be shown. 45. Then, select one of the images to evaluate. Compact Convolutional Transformers on Edge TPUs - ResearchGate A. (2016). IEEE Transactions on Semiconductor Manufacturing, 32(2), 163170. These two images both show data with the Loc defect. In 2020 IEEE 15th International Conference on Solid-State Integrated Circuit Technology (ICSICT) (pp. https://doi.org/10.1109/ICSICT49897.2020.9278021, Ebayyeh, A. To calculate precision-recall curves, start by performing a binary classification for each defect class by comparing the probability against an arbitrary threshold. 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. Appl. 1 Citations Metrics Abstract Semiconductor wafer defects severely affect product development. This type of defect is generated randomly, and no specific clustering phenomenon is visible, as shown in, Type-B defects are systematic and repeatable from wafer to wafer. https://doi.org/10.1007/s10845-021-01755-6, Kim, S., & Oh, I. S. (2017). ; Lin, P.C. Each copy has one of these modifications: horizontal reflection, vertical reflection, or rotation by a multiple of 90 degrees. IEEE Transactions on Semiconductor Manufacturing, 33(3), 436444. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(4), 832844. The network misclassified the image on the right as an Edge-Ring defect. https://doi.org/10.1007/s40745-021-00362-9, Santos, A. M., & Canuto, A. M. P. (2012). A comprehensive survey of anomaly detection algorithms. (2009). These two images both show data with the Donut defect. 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. Create datastores that read validation and test data and resize the data to the network input size. The machines produce images, called wafer maps, that indicate which dies perform correctly (pass) and which dies do not meet performance standards (fail). https://doi.org/10.1109/access.2020.3029127, Ezzat, A. Specify a class to evaluate. Deep learning model for imbalanced multi-label surface defect 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). In Proceedings of 34th International Conference on Machine Learning (pp. https://doi.org/10.1109/66.857947, Cheon, S., Lee, H., Kim, C. O., & Lee, S. H. (2019). https://doi.org/10.1080/00207543.2011.574502. IEEE Transactions on Semiconductor Manufacturing, 32(3), 286292. International Journal of Production Research, 58(9), 28052821. The proposed method uses deep learning convolutional neural networks to identify and classify four types of surface defects: center, local, random, and scrape. https://doi.org/10.1016/j.engappai.2012.11.009, Luo, Y., Yin, L., Bai, W., & Mao, K. (2020). 29802988. https://doi.org/10.1109/isqed.2019.8697407. Their performance comparisons on wafer defect classification using confusion matrices are shown below in, In the third experiment, the parameters for the classifiers to be tested all use default values. The authors declare no conflict of interest. In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 912915. Train on a GPU if one is available. In 2020 16th IEEE International Colloquium on Signal Processing Its Applications (CSPA), 230235. The data is stored in a MAT file as an array of structures. 8488. (2018). Metallization: mainly to perform the connections of metals. Precision-recall curves plot the tradeoff between precision and recall values as you adjust the threshold for the binary classification. This example evaluates the network performance using several metrics: precision, recall, and F1 scores. To evaluate the network performance, you must consider the performance at a range of thresholds. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020b). Semi-supervised classification of wafer map based on ladder network. Wafer bin map inspection based on DenseNet | SpringerLink IEEE Transactions on Information Theory, 8(2), 179187. Find the indices of correctly classified images. This article aims to provide a comprehensive review on the advancement of machine learning and deep learning applications for wafer map defect recognition and classification. This example uses the WM-811K Wafer Defect Map data set [1] [2]. Who are the leading innovators in wafer defect inspection system for 125136). In many cases, not only the available training data is limited but may also imbalanced. future research directions and describes possible research applications. The Hessian . Wiatowski, T.; Boelcskei, H. A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction. A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map. Patterning challenges in the sub-10 nm era. SVM is a well-known supervised classifier that performs by separating classes using hyper-planes, which are called support vectors. In 20th International Symposium on Quality Electronic Design (ISQED). https://doi.org/10.1016/j.compind.2019.04.015, Yuan, T., Bae, S. J., & Park, J. I. Detection and classification of defect patterns on semiconductor wafers. This paper proposes a deep learning-based automatic linear defects diagnosis solution for polycrystalline silicon photovoltaic cells based on EL images. For the image classified as an Edge-Loc defect, the defects at the boundary at the die are most influential in the network prediction. An appraisal of incremental learning methods. This enables the safe operation of the railway network. Sci. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). An overview on data representation learning: From traditional feature learning to recent deep learning. The Journal of Finance and Data Science, 2(4), 265278. In future research, other defect types, including mixed types, should be added into the types of defects to be classified and, if possible, there should be an increase in the number of training samples used in future investigations. https://doi.org/10.1016/j.engappai.2012.03.016, Park, S., Jang, J., & Kim, C. O. Stacked convolutional sparse denoising auto-encoder for identification of defect patterns in semiconductor wafer map. ), Handbook of Silicon Based MEMS Materials and Technologies (2nd Ed., pp. Thin-film processing: the use of physical or chemical means to perform vapor deposition of crystals on thin film. The averaging pooling layer, which would then be the last pooling layer before the fully connected layer, is followed by a softmax function [, The training of a CNN network takes a long time, especially with a large dataset. Simulated analog wafer test data for pattern recognition. Simplified subspaced regression network for identification of defect patterns in semiconductor wafer maps. CoRR, abs/2105.13245. Type-A defects are evenly random with a stable mean density. Engineering Applications of Artificial Intelligence, 26(56), 14791486. Both ways achieved similar performances, but discovering which way is better requires additional investigations. Wafer Defect Classification by Deep Learning | Kaggle https://doi.org/10.1109/tie.2020.3013492, Yu, J., & Lu, X. https://doi.org/10.1109/TII.2015.2481719, Article This function is attached to the example as a supporting file. This article presents a review of the deep learning methods employed for wafer map defect recognition. The defect recognition and classification methods are introduced and analyzed for discussion on their respective advantages, limitations, and scalability. Specify a set of random augmentations to apply to the training data using an imageDataAugmenter (Deep Learning Toolbox) object. Computers & Industrial Engineering, 142, 106358. method to classify visible surface defects on semiconductor wafers. Memory-augmented convolutional neural networks with triplet loss for imbalanced wafer defect pattern classification. Gradient-weighted class activation mapping (Grad-CAM) produces a visual explanation of decisions made by the network. You can also select a web site from the following list: Select the China site (in Chinese or English) for best site performance. (2010). Learn more about Institutional subscriptions, Adly, F., Alhussein, O., Yoo, P. D., Al-Hammadi, Y., Taha, K., Muhaidat, S., Jeong, Y.-S., Lee, U., & Ismail, M. (2015a). In Advances in Neural Information Processing Systems (Vol. Enhanced Deep Convolutional Neural Network for - IEEE Xplore (2018). (2007). A wafer bin map (WBM), obtained from an electrical die sorting (EDS) test, is used to detect defect patterns in a wafer chip. Our proposed approach features an integrated reject option where the model chooses to abstain from predicting a class label when misclassification risk is high. ),ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence(pp. [. Convolutional neural network for wafer surface defect classification and the detection of unknown defect class. A Deep Learning Model for Identification of Defect Patterns in That is, it is a combination of Type-A defects and Type-B defects, as shown in, There are many different types of defects, both visible and invisible. Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks. A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification. Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. Precision is the measure of how close the predicted results are to the actual value. In this paper, we utilize the information processing advantages of quantum computing to promote the defect learning defect review (DLDR). WM-811K Wafer Map. This function is attached to the example as a supporting file. You do not need to apply random augmentations to validation or test data. In Proceedings of the 1993 IEEE International Workshop on Memory Testing, San Jose, CA, USA, 910 August 1993; pp. Deep learning-based automatic defect classification for semiconductor permission is required to reuse all or part of the article published by MDPI, including figures and tables. ArXiv E-Prints, arXiv:1502.05700. The pretrained faster-R-CNN models used in this experiments were trained on two different datasets: COCO and KITTI. Using techniques of deep learning, the defect patterns on wafers may be efficiently classified, making it possible to rapidly identify production defects . AdaBalGAN: An improved generative adversarial network with imbalanced learning for wafer defective pattern recognition. A possible reason for this could be there are far more Edge-Ring images in the training set as compared to Donut images. The size of the data set is 3.5 GB. In this paper, the state-of-the-art in surface defect inspection using deep learning is presented. The main algorithm of logistic regression is used in a binary classification algorithm to solve linearly separable problems. Wafers are thin disks of semiconducting material, typically silicon, that serve as the foundation for integrated circuits. Using a GPU requires Parallel Computing Toolbox and a CUDA enabled NVIDIA GPU. (2018). https://doi.org/10.1109/tsm.2020.2994357, Saqlain, M., Jargalsaikhan, B., & Lee, J. Y. Semi-supervised multi-label learning for classification of wafer bin maps with mixed-type defect patterns. Optical wafer defect inspection at the 10 nm technology - IOPscience In Proceedings of the 7th International Conference on Computer and Communication Technology, Allahabad, India, 2426 November 2017; pp. Practical bayesian optimization of machine learning algorithms. (2020). Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers. Building Manufacturing Deep Learning Models with Minimal and Imbalanced Accelerating the pace of engineering and science. 1996-2023 MDPI (Basel, Switzerland) unless otherwise stated. https://doi.org/10.1109/tsm.2021.3062943, Faaeq, A., Guruler, H., & Peker, M. (2018). By default, this example evaluates the first correctly classified image. (2013). Compared with the test results, the comprehensive . (2020). 13). If the defect type can be correctly identified, then by the use of the process of elimination, the engineers can localize the cause(s) of the defects in the process and thus should be able to correct them and increase the yield. Nanomanufacturing and Metrology, 1(2), 6781. Sydney. A Pitfall of Unsupervised Pre-Training. In terms of application diversity, Kulicke & Soffa Industries holds the top position, followed by Fogale Nanotech and Rambus. https://doi.org/10.1007/s10845-022-01994-1, access via Detecting and measuring defects in wafer die using GAN and YOLOv3. Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm. [, Shen, L.; Cockburn, B. Surface defect classification is one of key points in the field of steel manufacturing. A Deep Learning Model for Identification of Defect Patterns in Semiconductor Wafer Map. Wafer defect pattern recognition and analysis based on convolutional neural network. https://doi.org/10.1613/jair.1.12125, Ooi, M.P.-L., Sok, H. K., Kuang, Y. C., Demidenko, S., & Chan, C. (2013). Karafuto Co., Ltd. on Twitter: "RT @Secomindai: Silicon Wafer In this paper, two ways to use the deep learning convolution neural networks to classify semiconductor defect images were presented.
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