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Stan Stokowski, Vaez-Iravani Mehdi, 1998, Wafer inspection technology challenges for ULSI manufacturing., American Institute of Physics, Vol. 449, No. 1DOI
Zhang Jiun-Ming, Lin Ruey-Ming, Wang Mao-Jiun J, 1999, The development of an automatic post-sawing inspection system using computer vision techniques., Computers in Industry, Vol. 40, No. 1, pp. 51-60DOI
Chou Paul B. , et al. , 1997, Automatic defect classification for semiconductor manufacturing., Machine Vision and Applications, Vol. 9, No. 4, pp. 201-214DOI
Sheng-Uei Guan, Pin Xie, Hong Li., 2003, A golden-block-based self-refining scheme for repetitive patterned wafer inspections., Machine Vision and Applications, Vol. 13, No. 5, pp. 314-321DOI
Liu Hongxia, et al. , 2010, Defect detection of IC wafer based on two-dimension wavelet transform., Microelectronics journal, Vol. 41, No. 2-3, pp. 171-177DOI
Chen Ssu-Han, Kang Chih-Hsiang, Perng Der-Baau, 2020, Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3., Applied Sciences, Vol. 10, No. 23DOI
Nakazawa Takeshi, Kulkarni Deepak V., 2019, Anomaly detection and segmentation for wafer defect patterns using deep convolutional encoder-decoder neural network architectures in semiconductor manufacturing., IEEE Transactions on Semiconductor Manufacturing, Vol. 32, No. 2, pp. 250-256DOI
Cheon Sejune, et al. , 2019, Convolutional neural network for wafer surface defect classification and the detection of unknown defect class., IEEE Transactions on Semiconductor Manufacturing, Vol. 32, No. 2, pp. 163-170DOI
Lin Hui, et al. , 2019, Automated defect inspection of LED chip using deep convolutional neural network., Journal of Intelligent Manufacturing, Vol. 30, No. 6, pp. 2525-2534DOI
Chen Xiaoyan, et al. , 2020, A Light-Weighted CNN Model for Wafer Structural Defect Detection., IEEE Access, Vol. 8, pp. 24006-24018DOI
Koch Gregory, Richard Zemel, Ruslan Salakhutdinov, 2015, Siamese neural networks for one-shot image recognition., ICML deep learning workshop, Vol. 2Google Search
Alex Kendall, Vijay Badrinarayanan, Roberto Cipolla, 2015, Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding., arXiv preprint arXiv:1511.02680DOI
Isobe Shuya, Arai Shuichi, 2017, Deep convolutional encoder-decoder network with model uncertainty for semantic segmentation., 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) IEEEDOI
Nair Tanya, et al. , 2020, Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation., Medical image analysis, Vol. 59DOI
Gal Yarin, Ghahramani Zoubin, 2016, Dropout as a bayesian approximation: Representing model uncertainty in deep learning., international conference on machine learning PMLRGoogle Search
Graves Alex, 2011, Practical variational inference for neural networks., Advances in neural information processing systemsGoogle Search
Srivastava Nitish, et al. , 2014, Dropout: a simple way to prevent neural networks from overfitting., The journal of machine learning research, Vol. 15, No. 1, pp. 1929-1958Google Search
Kristiadi Agustinus, Matthias Hein, Philipp Hennig, 2020, Being bayesian, even just a bit, fixes overconfidence in relu networks., International Conference on Machine Learning. PMLRGoogle Search
Shen Yichen, et al. , 2021, Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation., Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.DOI
Lu Xiankai, et al. , 2019, See more, know more: Unsupervised video object segmentation with co-attention siamese networks., Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionDOI
Varghese Ashley, et al. , 2018, ChangeNet: A deep learning architecture for visual change detection., Proceedings of the European Conference on Computer Vision (ECCV) WorkshopsDOI
Dong Hongwen, et al. , 2019, PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection., IEEE Transactions on Industrial Informatics, Vol. 16, No. 12, pp. 7448-7458DOI
Chen Jie, et al. , 2020, DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images., IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingDOI
Daudt Rodrigo Caye, Saux Bertr Le, Boulch Alexandre, 2018, Fully convolutional siamese networks for change detection., 2018 25th IEEE International Conference on Image Processing (ICIP), IEEEDOI
Paszke Adam, et al. , 2019, Pytorch: An imperative style, high-performance deep learning library., arXiv preprint arXiv:1912.01703Google Search