Nonlinear Forward Selection Component Analysis for Optical Emission Spectroscopy Wavelength Selection

Luca Puggini, Séan McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Semiconductor manufacturers are increasingly reliant on optical emission spectroscopy (OES) to source information on plasma characteristics and process change. However, OES data is characterized by high dimension and by highly correlated variables. This makes it difficult to interpret process behaviour using OES measurements. It is therefore desirable to obtain more compact representations of the data using dimensionality reduction techniques such as Forward Selection Component Analysis (FSCA). In this paper we investigate non-linear extensions of FSCA based on polynomial expansions and Extreme Learning Machines and show, through a combination of simulated examples and OES recordings from a semiconductor plasma etch process, that they can yield more compact representations that classical FSCA.

Original languageEnglish
Title of host publication2016 27th Irish Signals and Systems Conference, ISSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509034093
DOIs
Publication statusPublished - 04 Aug 2016
Event27th Irish Signals and Systems Conference, ISSC 2016 - Londonderry, United Kingdom
Duration: 21 Jun 201622 Jun 2016

Conference

Conference27th Irish Signals and Systems Conference, ISSC 2016
CountryUnited Kingdom
CityLondonderry
Period21/06/201622/06/2016

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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    Puggini, L., & McLoone, S. (2016). Nonlinear Forward Selection Component Analysis for Optical Emission Spectroscopy Wavelength Selection. In 2016 27th Irish Signals and Systems Conference, ISSC 2016 [7528446] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSC.2016.7528446