{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:03:55Z","timestamp":1783526635675,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008783","name":"National Research Council of Science and Technology","doi-asserted-by":"publisher","award":["1711121944, CRC-20-01-NFRI"],"award-info":[{"award-number":["1711121944, CRC-20-01-NFRI"]}],"id":[{"id":"10.13039\/501100008783","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, resulting in a process anomaly. Thus, fault detection in the semiconductor process is essential for success in advanced process control. Two datasets that assume faults of the mass flow controller (MFC) in equipment components were acquired using optical emission spectroscopy (OES) in the plasma etching process of a silicon trench: The abnormal process changed by the MFC is assumed to be faults, and the minority class of Case 1 is the normal class, and that of Case 2 is the abnormal class. In each case, additional minority class data were generated using GANs to compensate for the degradation of model training due to class-imbalanced data. Comparisons of five existing fault detection algorithms with the augmented datasets showed improved modeling performances. Generating a dataset for the minority group using GANs is beneficial for class imbalance problems of OES datasets in fault detection for the semiconductor plasma equipment.<\/jats:p>","DOI":"10.3390\/s23041889","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T03:01:45Z","timestamp":1675825305000},"page":"1889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5233-7364","authenticated-orcid":false,"given":"Jeong Eun","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da Hoon","family":"Seol","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6576-690X","authenticated-orcid":false,"given":"Sang Jeen","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TSM.2006.883595","article-title":"In Situ and Real-Time Monitoring of Plasma Process Chamber Component Qualities and Predictive Controlling of Wafer Yields","volume":"19","author":"Lee","year":"2006","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.compind.2012.10.002","article-title":"Feature Extraction, Condition Monitoring, and Fault Modeling in Semiconductor Manufacturing Systems","volume":"64","author":"Bleakie","year":"2013","journal-title":"Comp. Ind."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/5.149445","article-title":"Statistical Process Control in Semiconductor Manufacturing","volume":"80","author":"Spanos","year":"1992","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S1474-6670(17)43143-0","article-title":"Development and Benchmarking of Multivariate Statistical Process Control Tools for A Semiconductor Etch Process: Improving Robustness through Model Updating","volume":"30","author":"Gallagher","year":"1997","journal-title":"IFAC Proc. Vol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.jprocont.2012.01.012","article-title":"Real-Time Virtual Metrology and Control for Plasma Etch","volume":"22","author":"Lynn","year":"2012","journal-title":"J. Proc. Contr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.3390\/pr8091123","article-title":"A Review on Fault Detection and Process Diagnostics in Industrial Processes","volume":"8","author":"Park","year":"2020","journal-title":"Processes"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"G778","DOI":"10.1149\/1.1623772","article-title":"Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools","volume":"150","author":"Goodlin","year":"2003","journal-title":"J. Electrochem. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5703","DOI":"10.3390\/s100605703","article-title":"Real-Time Plasma Process Condition Sensing and Abnormal Process Detection","volume":"10","author":"Yang","year":"2010","journal-title":"Sensors"},{"key":"ref_9","first-page":"1","article-title":"Real-Time In-Situ Plasma Etch Process Monitoring for Sensor Based-Advanced Process Control","volume":"11","author":"Ahn","year":"2011","journal-title":"J. Semi. Technol. Sci."},{"key":"ref_10","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. Semi. Manufac."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3134","DOI":"10.1063\/1.328060","article-title":"Optical Emission Spectroscopy of Reactive Plasmas: A Method for Correlating Emission Intensities to Reactive Particle Density","volume":"51","author":"Coburn","year":"1980","journal-title":"J. Appl. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/66.857948","article-title":"Fault Detection of Plasma Etchers Using Optical Emission Spectra","volume":"13","author":"Yue","year":"2000","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.ifacol.2016.07.102","article-title":"Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy","volume":"49","author":"Puggini","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSM.2021.3079211","article-title":"Use of Plasma Information in Machine-Learning-Based Fault Detection and Classification for Advanced Equipment Control","volume":"34","author":"Kim","year":"2021","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kwon, J.W., Ryu, S., Park, J., Lee, H., Jang, Y., Park, S., and Kim, G.H. (2021). Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6\/O2\/Ar Capacitively Coupled Plasma. Materials, 14.","DOI":"10.3390\/ma14113005"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Park, H., Choi, J.E., Kim, D., and Hong, S.J. (2021). Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment. Electronics, 10.","DOI":"10.3390\/electronics10080944"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/TIE.2005.851663","article-title":"Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching","volume":"52","author":"Hong","year":"2005","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.engappai.2017.09.021","article-title":"An Enhanced Variable Selection and Isolation Forest Based Methodology for Anomaly Detection with OES Data","volume":"67","author":"Puggini","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12554","DOI":"10.1016\/j.eswa.2009.05.053","article-title":"A Virtual Metrology System for Semiconductor Manufacturing","volume":"36","author":"Kang","year":"2009","journal-title":"Exp. Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TSM.2019.2916374","article-title":"Denoised Residual Trace Analysis for Monitoring Semiconductor Process Faults","volume":"32","author":"Jang","year":"2019","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TSM.2016.2602226","article-title":"Performance of Machine Learning Algorithms for Class-Imbalanced Process Fault Detection Problems","volume":"29","author":"Lee","year":"2016","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/TASE.2020.2983061","article-title":"Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing","volume":"17","author":"Fan","year":"2020","journal-title":"IEEE Trans. Auto Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chang, K., Yoo, Y., and Baek, J.G. (2021). Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing. Sensors, 21.","DOI":"10.3390\/s21113880"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113933","DOI":"10.1016\/j.applthermaleng.2019.113933","article-title":"Chiller Fault Diagnosis with Field Sensors Using the Technology of Imbalanced Data","volume":"159","author":"Fan","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TSM.2022.3161512","article-title":"Machine Learning-Based Process-Level Fault Detection and Part-Level Fault Classification in Semiconductor Etch Equipment","volume":"35","author":"Kim","year":"2022","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_26","first-page":"1","article-title":"The Use of Generative Adversarial Networks to Alleviate Class Imbalance in Tabular Data: A Survey","volume":"9","author":"Khoshgoftaar","year":"2022","journal-title":"J. Big Data"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.1007\/s10845-021-01806-y","article-title":"An Imbalanced Data Learning Method for Tool Breakage Detection Based on Generative Adversarial Networks","volume":"33","author":"Sun","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2019.05.006","article-title":"Oversampling Method Using Outlier Detectable Generative Adversarial Network","volume":"133","author":"Oh","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neunet.2020.10.004","article-title":"CEGAN: Classification Enhancement Generative Adversarial Networks for Unraveling Data Imbalance Problems","volume":"133","author":"Suh","year":"2021","journal-title":"Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107377","DOI":"10.1016\/j.measurement.2019.107377","article-title":"Machinery Fault Diagnosis with Imbalanced Data Using Deep Generative Adversarial Networks","volume":"152","author":"Zhang","year":"2020","journal-title":"Measurement"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4011","DOI":"10.3390\/rs13194011","article-title":"A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)","volume":"13","author":"Pradhan","year":"2021","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3390\/risks9030049","article-title":"Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks","volume":"9","author":"Ngwenduna","year":"2021","journal-title":"Risks"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"125501","DOI":"10.1088\/2058-6272\/ac24f4","article-title":"Analysis of Optical Emission Spectroscopy Data during Silicon Etching in SF6\/O2\/Ar Plasma","volume":"23","author":"Kim","year":"2021","journal-title":"Plasma Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/TSM.2021.3138918","article-title":"Virtual Metrology for Etch Profile in Silicon Trench Etching with SF6\/O2\/Ar Plasma","volume":"35","author":"Choi","year":"2021","journal-title":"IEEE Trans. Semi. Manufac."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100046","DOI":"10.1016\/j.measen.2021.100046","article-title":"Machine Learning-Based Virtual Metrology on Film Thickness in Amorphous Carbon Layer Deposition Process","volume":"16","author":"Choi","year":"2021","journal-title":"Meas. Sens."},{"key":"ref_36","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neur. Inform. Proc. Syst."},{"key":"ref_37","first-page":"3","article-title":"Rectifier Nonlinearities Improve Neural Network Acoustic Models","volume":"30","author":"Maas","year":"2013","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification. Proc. IEEE Int. Conf. Comp. Vision, 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2019.06.043","article-title":"MFC-GAN: Class-Imbalanced Dataset Classification Using Multiple Fake Class Generative Adversarial Network","volume":"361","author":"Elyan","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1109\/JEDS.2018.2868465","article-title":"Vacuum Nano-Triode in Nothing-On-Insulator Configuration Working in Terahertz Domain","volume":"6","author":"Ravariu","year":"2018","journal-title":"IEEE J. Electron Devices Soc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:27:47Z","timestamp":1760120867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,8]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041889"],"URL":"https:\/\/doi.org\/10.3390\/s23041889","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,8]]}}}