[1] Gómez-Sirvent, J. L. et al. Defect classification on semiconductor wafers using fisher vector and visual vocabularies coding. Measurement 202, 111872 (2022).
[2] Harada, M., Minekawa, Y. & Nakamae, K. Defect detection techniques robust to process variation in semiconductor inspection. Measurement Science and Technology 30, 035402 (2019).
[3] Bhonsle, R. et al. Inspection, characterization and classification of defects for improved CMP of III-V materials. ECS Journal of Solid State Science and Technology 4, P5073-P5077 (2015).
[4] Zipfel, J. et al. Anomaly detection for industrial quality assurance: a comparative evaluation of unsupervised deep learning models. Computers & Industrial Engineering 177, 109045 (2023).
[5] Chandola, V., Banerjee, A. & Kumar, V. Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41, 15 (2009).
[6] Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21-27 (1967).
[7] Carratù, M. et al. A novel methodology for unsupervised anomaly detection in industrial electrical systems. IEEE Transactions on Instrumentation and Measurement 72, 3532812 (2023).
[8] Krizhevsky, A. , Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, NV, USA: Curran Associates Inc. , 2012, 1097-1105.
[9] He, K. M. et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016, 770-778.
[10] Yang, J. G. et al. Recent advances in deep-learning-enhanced photoacoustic imaging. Advanced Photonics Nexus 2, 054001 (2023).
[11] Park, J. et al. Clinical translation of photoacoustic imaging. Nature Reviews Bioengineering (2024) http://dx. doi.org/10.1038/s44222-024-00240-y.
[12] Misra, S. et al. Deep learning‐based multimodal fusion network for segmentation and classification of breast cancers using B‐mode and elastography ultrasound images. Bioengineering & Translational Medicine 8, e10480 (2023).
[13] Yoon, C. et al. Collaborative multi-modal deep learning and radiomic features for classification of strokes within 6h. Expert Systems with Applications 228, 120473 (2023).
[14] Jeong, H. et al. Robust ensemble of two different multimodal approaches to segment 3D ischemic stroke segmentation using brain tumor representation among multiple center datasets. Journal of Imaging Informatics in Medicine 37, 2375-2389 (2024).
[15] Park, E. et al. Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning. Nature Communications 15, 10892 (2024).
[16] Kim, S. et al. Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse‐view sampling scheme. Medical Physics 49, 6253-6277 (2022).
[17] Misra, S. et al. Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, 222-232 (2022).
[18] Choi, S. et al. Deep learning enhances multiparametric dynamic volumetric photoacoustic computed tomography in vivo (DL‐PACT). Advanced Science 10, 2202089 (2023).
[19] Wang, M., Zhou, D. H. & Chen, M. Y. Hybrid variable monitoring mixture model for anomaly detection in industrial processes. IEEE Transactions on Cybernetics 54, 319-331 (2024).
[20] Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504-507 (2006).
[21] Bergmann, P. et al. Improving unsupervised defect segmentation by applying structural similarity to autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Funchal, Portugal: VISIGRAPP, 2019, 372-380.
[22] Sakurada, M. & Yairi, T. Anomaly detection using autoencoders with nonlinear dimensionality reduction. Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis. Gold Coast, Australia: ACM, 2014, 4-11.
[23] Masci, J. et al. Stacked convolutional auto-encoders for hierarchical feature extraction. Proceedings of the 21st International Conference on Artificial Neural Networks and Machine Learning. Espoo, Finland: Springer, 2011, 52-59.
[24] Zhang, H. B. et al. Unsupervised deep anomaly detection for medical images using an improved adversarial autoencoder. Journal of Digital Imaging 35, 153-161 (2022).
[25] Zhang, C. K. , Wang, Y. M. & Tan, W. M. MTHM: self-supervised multitask anomaly detection with hard example mining. IEEE Transactions on Instrumentation and Measurement 72, 3518613 (2023).
[26] Luo, J. X. et al. SMD anomaly detection: a self-supervised texture–structure anomaly detection framework. IEEE Transactions on Instrumentation and Measurement 71, 5017611 (2022).
[27] Cheng, X. et al. Deep self-representation learning framework for hyperspectral anomaly detection. IEEE Transactions on Instrumentation and Measurement 73, 5002016 (2024).
[28] Goodfellow, I. et al. Generative adversarial nets. Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014, 2672-2680.
[29] Kim, J. et al. Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy. Scientific Reports 12, 16238 (2022).
[30] Kim, G. et al. Integrated deep learning framework for accelerated optical coherence tomography angiography. Scientific Reports 12, 1289 (2022).
[31] Kim, J. et al. Deep learning acceleration of multiscale superresolution localization photoacoustic imaging. Light: Science & Applications 11, 131 (2022).
[32] Niu, M. H. et al. An adaptive pyramid graph and variation residual-based anomaly detection network for rail surface defects. IEEE Transactions on Instrumentation and Measurement 70, 5020013 (2021).
[33] Schlegl, T. et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. Proceedings of the 25th International Conference on Information Processing in Medical Imaging. Boone, NC, USA: Springer, 2017, 146-157.
[34] Lee, S. et al. Emergency triage of brain computed tomography via anomaly detection with a deep generative model. Nature Communications 13, 4251 (2022).
[35] Akcay, S. , Atapour-Abarghouei, A. & Breckon, T. P. GANomaly: semi-supervised anomaly detection via adversarial training. Proceedings of the 14th Asian Conference on Computer Vision. Perth, Australia: Springer, 2019, 622-637.
[36] Ruff, L. et al. Deep one-class classification. Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden: PMLR, 2018, 4393-4402.
[37] Misra, S. et al. A voting-based ensemble feature network for semiconductor wafer defect classification. Scientific Reports 12, 16254 (2022).
[38] Imoto, K. et al. A CNN-based transfer learning method for defect classification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 32, 455-459 (2019). doi: 10.1109/TSM.2019.2941752
[39] Chen, Z. Q. et al. DMVSVDD: multi-view data novelty detection with deep autoencoding support vector data description. Expert Systems with Applications 240, 122443 (2024).
[40] Dong, X. H., Taylor, C. J. & Cootes, T. F. Defect classification and detection using a multitask deep one-class CNN. IEEE Transactions on Automation Science and Engineering 19, 1719-1730 (2022). doi: 10.1109/TASE.2021.3109353
[41] Liu, B. et al. Adaboost-based SVDD for anomaly detection with dictionary learning. Expert Systems with Applications 238, 121770 (2024).
[42] Zhou, Y. et al. VAE-based deep SVDD for anomaly detection. Neurocomputing 453, 131-140 (2021).
[43] Yi, J. H. & Yoon, S. Patch SVDD: patch-level SVDD for anomaly detection and segmentation. Proceedings of the 15th Asian Conference on Computer Vision. Kyoto, Japan: Springer, 2020, 375-390.
[44] Roth, K. et al. Towards total recall in industrial anomaly detection. Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, LA, USA: IEEE, 2022, 14298-14308.
[45] Yu, J. B. & Liu, J. T. Two-dimensional principal component analysis-based convolutional autoencoder for wafer map defect detection. IEEE Transactions on Industrial Electronics 68, 8789-8797 (2021).
[46] Kang, H. & Kang, S. A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification. Computers in Industry 129, 103450 (2021).
[47] Cheon, S. et al. Convolutional neural network for wafer surface defect classification and the detection of unknown defect class. IEEE Transactions on Semiconductor Manufacturing 32, 163-170 (2019).
[48] Wen, G. J. et al. A novel method based on deep convolutional neural networks for wafer semiconductor surface defect inspection. IEEE Transactions on Instrumentation and Measurement 69, 9668-9680 (2020).
[49] Kim, E. S. et al. An oversampling method for wafer map defect pattern classification considering small and imbalanced data. Computers & Industrial Engineering 162, 107767 (2021).
[50] Tao, X. et al. Deep learning for unsupervised anomaly localization in industrial images: A survey. IEEE Transactions on Instrumentation and Measurement 71, 5018021 (2022).
[51] Gao, Y. P. et al. A multilevel information fusion-based deep learning method for vision-based defect recognition. IEEE Transactions on Instrumentation and Measurement 69, 3980-3991 (2020).
[52] Yang, L. M., Zhou, F. Q. & Wang, L. A scratch detection method based on deep learning and image segmentation. IEEE Transactions on Instrumentation and Measurement 71, 5015012 (2022).
[53] Tao, X. et al. ViTALnet: anomaly on industrial textured surfaces with hybrid transformer. IEEE Transactions on Instrumentation and Measurement 72, 5009013 (2023).
[54] Shang, H. B. et al. Defect-aware transformer network for intelligent visual surface defect detection. Advanced Engineering Informatics 55, 101882 (2023).
[55] Vaswani, A. et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA, USA: Curran Associates Inc. , 2017, 6000-6010.
[56] Gao, Y. H. , Zhou, M. & Metaxas, D. N. UTNet: a hybrid transformer architecture for medical image segmentation. Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France: Springer, 2021, 61-71.
[57] Wang, S. N. et al. Linformer: self-attention with linear complexity. Print at https://arxiv.org/abs/2006.04768 (2020).
[58] Bello, I. et al. Attention augmented convolutional networks. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE, 2019, 3285-3294.
[59] Shaw, P. , Uszkoreit, J. & Vaswani, A. Self-attention with relative position representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). New Orleans, LA, USA: ACL, 2018, 464-468.
[60] Caruana, R. Multitask learning. Machine Learning 28, 41-75 (1997).
[61] Lin, T. Y. et al. Focal loss for dense object detection. Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017, 2999-3007.
[62] Dureuil, V. et al. Wafer bevel shape inducing high defect density in shallow trench isolation process. Proceedings of 2010 IEEE/SEMI Advanced Semiconductor Manufacturing Conference (ASMC). San Francisco, CA, USA: IEEE, 2010, 213-216.
[63] Huang, Y. B., Qiu, C. Y. & Yuan, K. Surface defect saliency of magnetic tile. The Visual Computer 36, 85-96 (2020). doi: 10.1007/s00371-018-1588-5
[64] Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5, 180161 (2018). doi: 10.1038/sdata.2018.161
[65] Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2579-2605 (2008).