Effect of initial-learning dataset on etching profile optimization using machine learning in plasma etching

Abstract

Machine learning (ML) was applied to optimize the etching profile for a line and space pattern sample in plasma etching. To investigate the effect of different initial-learning datasets on the optimization of the etching profile, high-, medium-, and low-quality datasets were prepared. The high-quality dataset was composed of etching results relatively close to a target etching profile. The low-quality dataset was composed of etching results relatively far from the target etching profile. The medium-quality dataset was intermediate between the high- and low-quality datasets. For the ML, the kernel ridge regression method was used. After six learning cycles, better etching results were obtained from the medium- and low-quality datasets than from the whole initial-learning dataset. However, the etching results from the high-quality dataset did not exceed those from the whole initial-learning dataset. These results indicate that an initial-learning dataset that has etching results far from the target profile can be useful for optimizing etching profiles.