Assessment of STI dry etch process variability by means of dynamic time warping technique

Numerical data of two in situ optical acquisition systems were used in machine learning algorithm to evaluate an shallow trench isolation dry etch process in a dataset of more than 200 etched wafers processed during a year. Though overall recipe performance was according to specifications, the observed parameter fluctuations were characterized to check for correlation patterns in processing data by means of machine learning. Thus, dynamic time wrapping was used to analyze time series datasets to get characteristics of minimum distance's path between signals coming of each individual wafer. Such metrics of distances could help clustering groups of wafers that appear to be in proximity. Out of eight analyzed channels, we observed the largest distance variability was in the Si main etch for spectral reflectometer dataset, which we compared to optical emission spectroscopy channels. Based on correlation assessment of distances to the etch depth uniformity data, we showed that the selected approach is a good means to analyze time series datasets of e.g. dry etch processes for monitoring process stability.