Max separation clustering for feature extraction from Optical Emission Spectroscopy Data

Beibei Flynn, Sean McLoone

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This paper proposes max separation clustering (MSC), a new non-hierarchical clustering method used for feature extraction from optical emission spectroscopy (OES) data for plasma etch process control applications. OES data is high dimensional and inherently highly redundant with the result that it is difficult if not impossible to recognize useful features and key variables by direct visualization. MSC is developed for clustering variables with distinctive patterns and providing effective pattern representation by a small number of representative variables. The relationship between signal-to-noise ratio (SNR) and clustering performance is highlighted, leading to a requirement that low SNR signals be removed before applying MSC. Experimental results on industrial OES data show that MSC with low SNR signal removal produces effective summarization of the dominant patterns in the data.
Original languageEnglish
Pages (from-to)480-488
Number of pages8
JournalIEEE Transactions on Semiconductor Manufacturing
Issue number4
Publication statusPublished - Nov 2011

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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