کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4942573 1437411 2018 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data
چکیده انگلیسی
The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low, and is an active area of research in semiconductor manufacturing, particularly in the context of using Optical Emission Spectroscopy (OES) data. The high dimension and correlated nature of OES data can limit the performance achievable with anomaly detection systems. In this paper we present a dimensionality reducing variable selection and isolation forest based anomaly detection and diagnosis methodology that addresses these issues. In particular, it takes account of isolated variables that can be overlooked when using conventional approaches such as PCA, and provides greater interpretability than afforded by PCA. The proposed methodology is illustrated with the aid of simulated and industrial plasma etch case studies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 67, January 2018, Pages 126-135
نویسندگان
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