https://scholars.lib.ntu.edu.tw/handle/123456789/581144
標題: | Equipment anomaly detection for semiconductor manufacturing by exploiting unsupervised learning from sensory data? | 作者: | Chen C.-Y Chang S.-C Liao D.-Y. SHI-CHUNG CHANG |
關鍵字: | Electrostatic devices; Electrostatic discharge; Learning systems; Metadata; Personnel training; Quality control; Semiconductor device manufacture; Unsupervised learning; Current practices; Hypothesis tests; Operations management; Parallel learning; Procedure control; Semiconductor equipment; Semiconductor manufacturing; Spectral transformations; Anomaly detection | 公開日期: | 2020 | 卷: | 20 | 期: | 19 | 起(迄)頁: | 1-26 | 來源出版物: | Sensors (Switzerland) | 摘要: | In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment maintenance but also indicates potential line yield problems. Prompt AD based on available equipment sensory data (ESD) facilitates proactive yield and operations management. However, ESD items are highly diversified and drastically scale up along with the increased use of sensors. Even veteran engineers lack knowledge about ESD items for automated AD. This paper presents a novel Spectral and Time Autoencoder Learning for Anomaly Detection (STALAD) framework. The design consists of four innovations: (1) identification of cycle series and spectral transformation (CSST) from ESD, (2) unsupervised learning from CSST of ESD by exploiting Stacked AutoEncoders, (3) hypothesis test for AD based on the difference between the learned normal data and the tested sample data, (4) dynamic procedure control enabling periodic and parallel learning and testing. Applications to ESD of an HDP-CVD tool demonstrate that STALAD learns normality without engineers’ prior knowledge, is tolerant to some abnormal data in training input, performs correct AD, and is efficient and adaptive for fab applications. Complementary to the current practice of using control wafer monitoring for AD, STALAD may facilitate early detection of equipment anomaly and assessment of impacts to process quality. ? 2020 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092041831&doi=10.3390%2fs20195650&partnerID=40&md5=8e4207a5f9caceeacb659bc1c5251799 https://scholars.lib.ntu.edu.tw/handle/123456789/581144 |
ISSN: | 14248220 | DOI: | 10.3390/s20195650 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。