Effect of pre-training to build a regression model using shallow neural network for semiconductor plasma etch process equipment

Ohyung Kwon, Nayeon Lee, Kangil Kim

Abstract: Plasma etch process is one of manufacturing steps to fabricate semiconductor chips and the difficulty of etch process has become harder and harder because the target specifications of chips have become harsher. To overcome this circumstance, monitoring plasma parameters in real-time has been requested. In this study, regression models to predict a plasma density from the intensities of optical wavelength obtained from plasma etch process chamber using shallow neural network were presented. The estimation results of several models with or without pre-training were also analyzed. The model using variational auto-encoder showed the best performance and it can expect to be easily accepted in semiconductor industry because optical intensity measurement device was already equipped for plasma etch process chamber.