In-line XPS metrology using unsupervised machine learning in high volume manufacturing

Abstract

To further enhance performance of in-line XPS metrology we will demonstrate the benefit of an unsupervised machine learning approach to increase precision of critical metal gate film thickness measurements and quantification of doping concentration within source-drain junctions. Unsupervised ML efficiently separates process information from inherent noise in the XPS spectra to enable a noise-filtering that improves result precision. The observed precision improvements were utilized to increase wafer through-put by reducing the acquisition time while preserving precision, accuracy, and sensitivity when supporting high volume manufacturing.