{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:28:07Z","timestamp":1784723287918,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The production process of a wafer in the semiconductor industry consists of several phases such as a diffusion and associated defectivity test, parametric test, electrical wafer sort test, assembly and associated defectivity tests, final test, and burn-in. Among these, the fault detection phase is critical to maintain the low number and the impact of anomalies that eventually result in a yield loss. The understanding and discovery of the causes of yield detractors is a complex procedure of root-cause analysis. Many parameters are tracked for fault detection, including pressure, voltage, power, or valve status. In the majority of the cases, a fault is due to a combination of two or more parameters, whose values apparently stay within the designed and checked control limits. In this work, we propose an ensembled anomaly detector which combines together univariate and multivariate analyses of the fault detection tracked parameters. The ensemble is based on three proposed and compared balancing strategies. The experimental phase is conducted on two real datasets that have been gathered in the semiconductor industry and made publicly available. The experimental validation, also conducted to compare our proposal with other traditional anomaly detection techniques, is promising in detecting anomalies retaining high recall with a low number of false alarms.<\/jats:p>","DOI":"10.3390\/s21165465","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T09:22:38Z","timestamp":1628846558000},"page":"5465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Ensembled Anomaly Detector for Wafer Fault Detection"],"prefix":"10.3390","volume":"21","author":[{"given":"Giuseppe","family":"Furnari","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Vattiato","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Allegra","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo Luigi Maria","family":"Milotta","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy"},{"name":"STMicroelectronics, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Orofino","sequence":"additional","affiliation":[{"name":"STMicroelectronics, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosetta","family":"Rizzo","sequence":"additional","affiliation":[{"name":"STMicroelectronics, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosaria Angela","family":"De Palo","sequence":"additional","affiliation":[{"name":"STMicroelectronics, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3865-9911","authenticated-orcid":false,"given":"Filippo","family":"Stanco","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Viagrande, L.C., Milotta, F.L.M., Giuffr\u00e8, P., Bruno, G., Vinciguerra, D., and Gallo, G. (2020, January 27\u201329). Semisupervised Classification of Anomalies Signatures in Electrical Wafer Sorting (EWS) Maps. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications\u2014Volume 5: VISAPP, Valletta, Malta.","DOI":"10.5220\/0008914402780285"},{"key":"ref_2","unstructured":"Automotive Electronic Council (2021, August 12). Guidelines for Part Average Testing. Available online: http:\/\/www.aecouncil.com\/Documents\/AEC_Q001_Rev_D.pdf."},{"key":"ref_3","unstructured":"Automotive Electronic Council (2021, August 12). Guidelines for Statistical Yield Analysis. Available online: http:\/\/www.aecouncil.com\/Documents\/AEC_Q002_Rev_B1.pdf."},{"key":"ref_4","unstructured":"Muriel, S., Garcia, P., Maire-Richard, O., Monleon, M., and Recio, M. (2001, January 23\u201324). Statistical bin analysis on wafer probe. Proceedings of the 2001 IEEE\/SEMI Advanced Semiconductor Manufacturing Conference (IEEE Cat. No. 01CH37160), Munich, Germany."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/66.121980","article-title":"Statistical bin limits-an approach to wafer disposition in IC fabrication","volume":"5","author":"Illyes","year":"1992","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly Detection: A Survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2018.05.020","article-title":"DeepNAP: Deep neural anomaly pre-detection in a semiconductor fab","volume":"457","author":"Kim","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Elsisi, M., Tran, M.Q., Mahmoud, K., Lehtonen, M., and Darwish, M.M. (2021). Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings. Sensors, 21.","DOI":"10.3390\/s21041038"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Elsisi, M., Mahmoud, K., Lehtonen, M., and Darwish, M.M. (2021). Reliable industry 4.0 based on machine learning and IOT for analyzing, monitoring, and securing smart meters. Sensors, 21.","DOI":"10.3390\/s21020487"},{"key":"ref_10","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An","year":"2015","journal-title":"Spec. Lect. IE"},{"key":"ref_11","unstructured":"Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H. (May, January 30). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","article-title":"High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning","volume":"58","author":"Erfani","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1016\/j.promfg.2017.07.353","article-title":"Anomaly detection approaches for semiconductor manufacturing","volume":"11","author":"Susto","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TKDE.2012.99","article-title":"Anomaly detection via online oversampling principal component analysis","volume":"25","author":"Lee","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Schubert, M., and Zimek, A. (2008, January 24\u201327). Angle-based outlier detection in high-dimensional data. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401946"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., and Sander, J. (2000, January 16\u201318). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA.","DOI":"10.1145\/342009.335388"},{"key":"ref_18","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TSM.2011.2175394","article-title":"Fault detection and classification in plasma etch equipment for semiconductor manufacturing e-diagnostics","volume":"25","author":"Hong","year":"2011","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0169-7439(89)80095-4","article-title":"Analysis of variance (ANOVA)","volume":"6","author":"St","year":"1989","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_23","unstructured":"Dua, D., and Graff, C. (2021, August 12). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_24","first-page":"1","article-title":"PyOD: A Python Toolbox for Scalable Outlier Detection","volume":"20","author":"Zhao","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhao, Y., Botta, N., Ionescu, C., and Hu, X. (2020, January 17\u201320). COPOD: Copula-based outlier detection. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00135"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15\u201319). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5465\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:24Z","timestamp":1760165124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,13]]},"references-count":26,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165465"],"URL":"https:\/\/doi.org\/10.3390\/s21165465","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,13]]}}}