An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data

Luca Puggini, Sean McLoone

Research output: Contribution to journalArticle

18 Citations (Scopus)
1081 Downloads (Pure)

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 methodolgy is illustrated with the aid of simulated and industrial plasma etch case studies.
Original languageEnglish
Pages (from-to)126-135
Number of pages10
JournalEngineering Applications of Artificial Intelligence
Volume67
Early online date17 Oct 2017
DOIs
Publication statusPublished - Jan 2018

Keywords

  • Semiconductor
  • fault detection
  • Dimensionality Reduction
  • OES spectrum
  • isolation forest
  • forward selection component analysis

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