In-line Characterization of Nonselective SiGe Nodule Defects with Scatterometry Enabled by Machine Learning

Authored by: Dexin Kong, Robin Chao, Mary Breton, Chi-chun Liu, Gangadhara Raja Muthinti, Soon-cheon Seo, Nicolas J. Loubet, Pietro Montanini, John Gaudiello, Veeraraghavan Basker, Aron Cepler, Susan Ng-Emans, Matthew Sendelbach, Itzik Kaplan, Gilad Barak, Daniel Schmidt, Julien Frougier | SPIE 2018, February 1, 2018

As device scaling continues, defects related to the nucleation of non-epitaxial SiGe, in the form of nodules, are becoming
more problematic. These are generated during the source/drain epitaxial growth process and are difficult to control and
measure. In this work, novel methods, based on scatterometry and machine learning, are developed to accurately and
comprehensively measure the SiGe nodules. The results of scatterometry-based defect measurements are compared to topdown
scanning electron microscopy (SEM), cross-sectional SEM and transmission electron microscopy (TEM). With the
advances in the scatterometry-based defect measurement metrology, we demonstrate such fast, quantitative, and
comprehensive measurement of SiGe nodule defects can be used to improve the metrology throughput and device yield.
Keywords: Scatterometry, gate all around, nodules, machine learning