Nonlinear Forward Selection Component Analysis for optical emission spectroscopy wavelength selection
| Autor: | Luca Puggini, Seán McLoone |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Physics
0209 industrial biotechnology Polynomial Dimensionality reduction 02 engineering and technology Nonlinear system Wavelength 020901 industrial engineering & automation Component analysis Dimension (vector space) Optical recording Principal component analysis 0202 electrical engineering electronic engineering information engineering Electronic engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | 2016 27th Irish Signals and Systems Conference (ISSC). |
| DOI: | 10.1109/issc.2016.7528446 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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