https://scholars.lib.ntu.edu.tw/handle/123456789/607280
標題: | Detecting and Scoring Equipment Faults in Real Time during Semiconductor Test Processes | 作者: | Wu Y.-H Huang J.-Y Yao Y.-C Tien Y.-J Yu C.-J SHENG-DE WANG |
關鍵字: | anomaly detection;collective anomaly;equipment faults;robust statistics;semiconductor test processes;Cost effectiveness;Costs;Fault detection;Cost effective;Level of use;Live streaming;Operational efficiencies;Real time;Semiconductor tests;Industrial internet of things (IIoT) | 公開日期: | 2021 | 卷: | 38 | 期: | 4 | 起(迄)頁: | 119-126 | 來源出版物: | IEEE Design and Test | 摘要: | This article proposes a robust calculation method for the identification of anomalies in IC manufacturing test data while eliminating the need of large storage of raw measurements. Having the capability to process live streaming data is now the fundamental requisite for the successful realization of industrial Internet of Things (IIoT). The presented framework supports the in-stream analysis of data by the unsupervised incremental binning (UIB) technique as shown in one of the algorithms. UIB groups individual values into a small but sufficient maximum number of bins, such as default maximum 100 bins, with each bin recording observed sum and count. Upon a newly received value, UIB creates a new bin with sum value and count 1. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096389959&doi=10.1109%2fMDAT.2020.3036591&partnerID=40&md5=fbdbbe95c4d3b681c12798bdedeed0f0 https://scholars.lib.ntu.edu.tw/handle/123456789/607280 |
ISSN: | 21682356 | DOI: | 10.1109/MDAT.2020.3036591 |
顯示於: | 電機工程學系 |
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