Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data
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, result...
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| Published in: | Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 1889 |
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| Language: | English |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Choi, Jeong Eun Seol, Da Hoon Hong, Sang Jeen Kim, Chan Young |
| AuthorAffiliation | Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, Republic of Korea |
| AuthorAffiliation_xml | – name: Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, Republic of Korea |
| Author_xml | – sequence: 1 givenname: Jeong Eun orcidid: 0000-0002-5233-7364 surname: Choi fullname: Choi, Jeong Eun – sequence: 2 givenname: Da Hoon surname: Seol fullname: Seol, Da Hoon – sequence: 3 givenname: Chan Young surname: Kim fullname: Kim, Chan Young – sequence: 4 givenname: Sang Jeen orcidid: 0000-0002-6576-690X surname: Hong fullname: Hong, Sang Jeen |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36850488$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TSM.2011.2175394 10.1109/TSM.2022.3161512 10.3390/rs13194011 10.1016/j.neunet.2020.10.004 10.1109/TSM.2019.2916374 10.1007/s10845-021-01806-y 10.1109/5.149445 10.3390/risks9030049 10.1063/1.328060 10.1016/j.ifacol.2016.07.102 10.3390/s21113880 10.1016/j.measen.2021.100046 10.1109/TSM.2021.3138918 10.1109/TASE.2020.2983061 10.3390/ma14113005 10.1109/TIE.2005.851663 10.1016/j.applthermaleng.2019.113933 10.1109/ICCV.2015.123 10.1016/j.neucom.2019.06.043 10.1016/j.compind.2012.10.002 10.3390/pr8091123 10.1109/TSM.2006.883595 10.1016/S1474-6670(17)43143-0 10.1016/j.eswa.2019.05.006 10.1109/66.857948 10.3390/s100605703 10.3390/electronics10080944 10.1109/JEDS.2018.2868465 10.1109/TSM.2021.3079211 10.1088/2058-6272/ac24f4 10.1016/j.jprocont.2012.01.012 10.1109/TSM.2016.2602226 10.1016/j.eswa.2009.05.053 10.1016/j.measurement.2019.107377 10.1016/j.engappai.2017.09.021 10.1149/1.1623772 |
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| Keywords | generative adversarial networks machine learning plasma etch optical emission spectroscopy fault detection |
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| StartPage | 1889 |
| SubjectTerms | Algorithms Control equipment Etching equipment fault detection generative adversarial networks Machine learning optical emission spectroscopy plasma etch Plasma etching Process controls Sensors Silicon wafers Statistical methods |
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| Title | Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data |
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