Authors:
Beibei Ma
;
Seán McLoone
and
John Ringwood
Affiliation:
National University of Ireland Maynooth, Ireland
Keyword(s):
Semiconductor manufacturing, plasma etching, metal etching, optical emission spectroscopy (OES), principal component analysis (PCA), batch processing.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Change Detection
;
Computer Vision, Visualization and Computer Graphics
;
Data Engineering
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper explores the application of principal component analysis (PCA) to the monitoring of within-lot and between-lot plasma variations that occur in a plasma etching chamber used in semiconductor manufacturing, as observed through Optical Emission Spectroscopy (OES) analysis of the chamber exhaust. Using PCA, patterns that are difficult to identify in the 2048-dimension OES data are condensed into a small number of principle components (PCs). It is shown, with the aid of experimental data, that by simply tracking changes in the directions of these PCs both inter-lot and intra-lot patterns can be identified.