Low open-area endpoint detection using a PCA-based T super(2) statistic and Q statistic on optical emission spectroscopy measurements

Examines an approach for automatically identifying endpoint (the completion in etch of a thin film) during plasma etching of low open area wafers. Because many end-pointing techniques use a few manually selected wavelengths or simply time the etch, the resulting endpoint detection determination may only be valid for a very short number of runs before process drift and noise render them ineffective. Only recently have researchers begun to examine methods to automatically select and weight spectral channels for estimation and diagnosis of process behavior. This paper will explore the use of principal component analysis (PCA)-based T super(2) formulation to filter out noisy spectral channels and characterize spectral variation of optical emission spectroscopy (OES) correlated with endpoint. This approach is applied and demonstrated for patterned contact and via etching using digital semiconductor's CMOS6 (0.35- mu m) production process