An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data. (January 2018)

• Record Type: Journal Article
• Authors: Puggini, Luca; McLoone, Seán
• Is Part Of: Engineering applications of artificial intelligence. Volume 67(2018:Jan.)
• Page Start: 126
• Page End: 135
• Publication Date: 2018-01
• Subjects: Semiconductors -- Fault detection -- Dimensionality reduction -- OES spectrum -- Isolation Forest -- Forward Selection Components Analysis; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.09.021
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.