Principal Component Analysis Based Support Vector Machine for the End Point Detection of the Metal Etch Process

Abstract

Abstract An endpoint detection using the algorithm of principal component analysis based support vector machine was developed for the plasma etching process. Because many endpoint detection techniques use a few manually selected wavelengths, noise render them ineffective and it is hard to select the important wavelengths. So the principal component algorithm with the whole wavelengths has been developed for the more effective monitoring of end point. And the support vector regression was followed for the realtime end point detection with reduced wavelengths to save the processing time. This approach was applied and demonstrated for a metal etching process of Al and 0.5% Cu on the oxide stack with inductively coupled BCl 2 /Cl 2 plasma.