{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:15:46Z","timestamp":1790846146739,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"STMicroelectronics company and MadeIn4 European project","award":["826589"],"award-info":[{"award-number":["826589"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Amid the ongoing emphasis on reducing manufacturing costs and enhancing productivity, one of the crucial objectives when manufacturing is to maintain process tools in optimal operating conditions. With advancements in sensing technologies, large amounts of data are collected during manufacturing processes, and the challenge today is to utilize these massive data efficiently. Some of these data are used for fault detection and classification (FDC) to evaluate the general condition of production machinery. The distinctive characteristics of semiconductor manufacturing, such as interdependent parameters, fluctuating behaviors over time, and frequently changing operating conditions, pose a major challenge in identifying defective wafers during the manufacturing process. To address this challenge, a multivariate fault detection method based on a 1D ResNet algorithm is introduced in this study. The aim is to identify anomalous wafers by analyzing the raw time-series data collected from multiple sensors throughout the semiconductor manufacturing process. To achieve this objective, a set of features is chosen from specified tools in the process chain to characterize the status of the wafers. Tests on the available data confirm that the gradient vanishing problem faced by very deep networks starts to occur with the plain 1D Convolutional Neural Network (CNN)-based method when the size of the network is deeper than 11 layers. To address this, a 1D Residual Network (ResNet)-based method is used. The experimental results show that the proposed method works more effectively and accurately compared to techniques using a plain 1D CNN and can thus be used for detecting abnormal wafers in the semiconductor manufacturing industry.<\/jats:p>","DOI":"10.3390\/s23229099","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T05:04:56Z","timestamp":1699592696000},"page":"9099","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5740-9884","authenticated-orcid":false,"given":"Philip","family":"Tchatchoua","sequence":"first","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, University of Toulon, 13007 Marseille, France"},{"name":"STMicroelectronics, 13106 Rousset, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Graton","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, University of Toulon, 13007 Marseille, France"},{"name":"Ecole Centrale de Marseille (Centrale M\u00e9diterran\u00e9e), 13013 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustapha","family":"Ouladsine","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, University of Toulon, 13007 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Fran\u00e7ois","family":"Christaud","sequence":"additional","affiliation":[{"name":"STMicroelectronics, 13106 Rousset, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/MIS.2018.043741325","article-title":"From Raw Data to Smart Manufacturing: AI and Semantic Web of Things for Industry 4.0","volume":"33","author":"Patel","year":"2018","journal-title":"IEEE Intell. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1016\/j.jprocont.2012.06.009","article-title":"A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process","volume":"22","author":"Yin","year":"2012","journal-title":"J. Process Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","article-title":"A review of process fault detection and diagnosis: Part I: Quantitative model-based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jco.2018.08.003","article-title":"The curse of dimensionality for numerical integration on general domains","volume":"50","author":"Hinrichs","year":"2019","journal-title":"J. Complex."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"Karlsson","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","first-page":"176","article-title":"Classification with class imbalance problem","volume":"5","author":"Ali","year":"2013","journal-title":"Int. J. Adv. Soft Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Thieullen, A., Ouladsine, M., and Pinaton, J. (2013, January 14\u201316). Application of PCA for efficient multivariate FDC of semiconductor manufacturing equipment. Proceedings of the ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference, Saratoga Springs, NY, USA.","DOI":"10.1109\/ASMC.2013.6552755"},{"key":"ref_8","first-page":"1017","article-title":"ICA with reconstruction cost for efficient overcomplete feature learning","volume":"24","author":"Le","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1109\/TII.2013.2251891","article-title":"Least-squares fault detection and diagnosis for networked sensing systems using a direct state estimation approach","volume":"9","author":"He","year":"2013","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5711","DOI":"10.1016\/j.eswa.2010.10.062","article-title":"Spline regression based feature extraction for semiconductor process fault detection using support vector machine","volume":"38","author":"Park","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, Q.P., and Wang, J. (2008, January 11\u201313). Principal Component based K-Nearest Neighbor Rule for Semiconductor Process Fault Detection. Proceedings of the 2008 American Control Conference, Seattle, WA, USA.","DOI":"10.1109\/ACC.2008.4586721"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Goldstein, M., and Uchida, S. (2016). A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152173"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/TSM.2016.2628865","article-title":"A deep learning model for robust wafer fault monitoring with sensor measurement noise","volume":"30","author":"Lee","year":"2017","journal-title":"IEEE Trans. Semi. Manuf."},{"key":"ref_15","first-page":"1748","article-title":"Detection and Classification of Transmission Line Faults Based on Unsupervised Feature Learning and Convolutional Sparse Autoencoder","volume":"9","author":"Chen","year":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/TASE.2022.3141186","article-title":"A deep convolutional autoencoder-based approach for anomaly detection with industrial, non-images, 2-dimensional data: A semiconductor manufacturing case study","volume":"19","author":"Maggipinto","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tedesco, S., Susto, G.A., Gentner, N., Kyek, A., and Yang, Y. (2021, January 12\u201315). A scalable deep learning-based approach for anomaly detection in semiconductor manufacturing. Proceedings of the 2021 Winter Simulation Conference (WSC), Phoenix, AZ, USA.","DOI":"10.1109\/WSC52266.2021.9715336"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TSM.2022.3216032","article-title":"Anomaly Detection in Batch Manufacturing Processes Using Localized Reconstruction Errors From 1-D Convolutional AutoEncoders","volume":"36","author":"Gorman","year":"2022","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_19","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., and Shroff, G. (2016, January 19\u201324). LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. Proceedings of the ICML 2016, New York, NY, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hosseinpour, F., Ahmed, I., Baraldi, P., Behzad, M., Zio, E., and Lewitschnig, H. (September, January 28). An unsupervised method for anomaly detection in multiystage production systems based on LSTM autoencoders. Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland.","DOI":"10.3850\/978-981-18-5183-4_R22-19-604"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TSM.2017.2676245","article-title":"A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes","volume":"30","author":"Lee","year":"2017","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s10845-020-01591-0","article-title":"Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing","volume":"32","author":"Hsu","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TSM.2022.3164904","article-title":"Redefining monitoring rules for intelligent fault detection and classification via CNN transfer learning for smart manufacturing","volume":"35","author":"Chien","year":"2022","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_24","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3980","DOI":"10.1109\/TPAMI.2020.2990339","article-title":"Norm-preservation: Why residual networks can become extremely deep?","volume":"43","author":"Zaeemzadeh","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qian, L., Pan, Q., Lv, Y., and Zhao, X. (2022). Fault detection of bearing by resnet classifier with model-based data augmentation. Machines, 10.","DOI":"10.3390\/machines10070521"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tchatchoua, P., Graton, G., Ouladsine, M., Muller, J., Traor\u00e9, A., and Juge, M. (2022, January 13\u201315). 1D ResNet for Fault Detection and Classification on Sensor Data in Semiconductor Manufacturing. Proceedings of the 2022 International Conference on Control, Automation and Diagnosis (ICCAD), Lisbon, Portugal.","DOI":"10.1109\/ICCAD55197.2022.9853997"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: What it does and how to do it","volume":"78","author":"Werbos","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_30","first-page":"89","article-title":"Long Short Term Memory Networks for Anomaly Detection in Time Series","volume":"2015","author":"Malhotra","year":"2015","journal-title":"Eur. Symp. Artif. Neural Netw."},{"key":"ref_31","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lect. Notes"},{"key":"ref_32","unstructured":"Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., and Ng, A.Y. (July, January 28). On optimization methods for deep learning. Proceedings of the ICML 2011, Bellevue, WA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tchatchoua, P., Graton, G., Ouladsine, M., and Juge, M. (2021, January 23\u201327). A comparative evaluation of deep learning anomaly detection techniques on semiconductor multivariate time series data. Proceedings of the 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France.","DOI":"10.1109\/CASE49439.2021.9551541"},{"key":"ref_34","unstructured":"Browne, M., and Ghidary, S. (2003). AI 2003: Advances in Artificial Intelligence, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2917521","DOI":"10.1109\/TSM.2019.2917521","article-title":"Fault detection and diagnosis using self-attentive convolutional neural networks for variable length sensor data in semiconductor manufacturing","volume":"32","author":"Kim","year":"2019","journal-title":"IEEE Trans. Semi. Manuf."},{"key":"ref_36","unstructured":"Lin, Z., Feng, M., Santos, C.N.D., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017, January 24\u201326). A structured self-attentive sentence embedding. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France."},{"key":"ref_37","unstructured":"Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015, January 7\u201312). Training very deep networks. Proceedings of the 29th Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_40","unstructured":"Remya, K., and Sajith, V. (2023, January 19\u201321). Machine Learning Approach for Mixed type Wafer Defect Pattern Recognition by ResNet Architecture. Proceedings of the 2023 International Conference on Control, Communication and Computing (ICCC), Thiruvananthapuram, India."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fu, H., Zhou, Z., Zeng, Z., Sang, T., Zhu, Y., and Zheng, X. (2022, January 15\u201318). Surface Defect Detection Based on ResNet Classification Network with GAN Optimized. Proceedings of the 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld\/UIC\/ScalCom\/DigitalTwin\/PriComp\/Meta), Haikou, China.","DOI":"10.1109\/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00225"},{"key":"ref_42","unstructured":"Labach, A., Salehinejad, H., and Valaee, S. (2019). Survey of dropout methods for deep neural networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jeni, L., Cohn, J., and De la Torre, F. (2013, January 2\u20135). Facing imbalanced data recommendations for the use of performance metrics. Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland.","DOI":"10.1109\/ACII.2013.47"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9099\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:21:04Z","timestamp":1760131264000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"references-count":44,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229099"],"URL":"https:\/\/doi.org\/10.3390\/s23229099","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]}}}