Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment
Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly duri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/8/944 |
| _version_ | 1797537594849886208 |
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| author | Hyoeun Park Jeong Eun Choi Dohyun Kim Sang Jeen Hong |
| author_facet | Hyoeun Park Jeong Eun Choi Dohyun Kim Sang Jeen Hong |
| author_sort | Hyoeun Park |
| collection | DOAJ |
| description | Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed. |
| first_indexed | 2024-03-10T12:17:25Z |
| format | Article |
| id | doaj.art-7fa64c2c6fe24d36b4b86a458e3eb4be |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:17:25Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-7fa64c2c6fe24d36b4b86a458e3eb4be2023-11-21T15:44:05ZengMDPI AGElectronics2079-92922021-04-0110894410.3390/electronics10080944Artificial Immune System for Fault Detection and Classification of Semiconductor EquipmentHyoeun Park0Jeong Eun Choi1Dohyun Kim2Sang Jeen Hong3Department of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, KoreaDepartment of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, KoreaDepartment of Industrial and Management Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, KoreaDepartment of Electronics Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Gyeonggi-do, KoreaSemiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.https://www.mdpi.com/2079-9292/10/8/944artificial immune systemfault detectiondata miningdecision makingsemiconductor equipment |
| spellingShingle | Hyoeun Park Jeong Eun Choi Dohyun Kim Sang Jeen Hong Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment Electronics artificial immune system fault detection data mining decision making semiconductor equipment |
| title | Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment |
| title_full | Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment |
| title_fullStr | Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment |
| title_full_unstemmed | Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment |
| title_short | Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment |
| title_sort | artificial immune system for fault detection and classification of semiconductor equipment |
| topic | artificial immune system fault detection data mining decision making semiconductor equipment |
| url | https://www.mdpi.com/2079-9292/10/8/944 |
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