Production solutions for high-throughput/precision XPS metrology using unsupervised machine learning

A metrology module for monitoring fabrication process of an integrated circuit (IC), the metrology module comprising:

• an input module receiving signal indicative of emissions from the IC;
• a principal component analysis (PCA) module receiving the signal and calculating therefrom variance corresponding to each principal component;
• a PCA number selector selecting a number of N principal components according to the calculated variance;
• a filtering module selecting from the signal data points corresponding to the N principal components;
• an intensity module calculating emission intensity from the output of the filter module;
• a conversion module converting the emission intensity into material property value;
• and
• a display module plotting the material property value.

A non-transitory machine readable medium having stored thereon executable program code which, when executed, causes a machine to perform a process for determining anomalies in a property of a fabricated layer of a sample, the process comprising:

• collecting photoelectron emissions from the sample and generating from the emissions a dataset corresponding to photoelectron count per unit time versus kinetic energy of emitted photoelectrons;
• performing principal component analysis on the dataset to thereby obtain principal component values from the dataset;
• obtaining a number N of principal components;
• removing from the dataset all values not belonging to the N principal components to thereby obtain filtered dataset;
• and analyzing the filtered dataset to determine presence of process excursion.