End point prediction in wet etching, cleaning, and rinsing of microstructures in semiconductor manufacturing

Etching, cleaning, and rinsing of micro- and nano-scale features are important industrial processes in semiconductor manufacturing. This study focused on developing an adaptable process simulator that employs user-input criteria drawn from literature and processing conditions to predict end point times for wet chemical processing. Two industrially relevant geometric systems were investigated, a rectangular trench and a cylindrical via, to expand the function of the tool. The effect of varying process parameters, including reactant concentration in the bulk fluid and the mass transfer coefficient, on the end point time was investigated and results indicate that better reactant availability reduces the end point time. Features with stacked layers forming feature sidewalls were studied to provide results on undercut, a critical wet chemical processing challenge. The location of the interface of stacked layers influences the clean up time as well as the onset of undercut. The process simulator developed can be used as a predictive tool for in-house recipe development to minimize invasive experiments and is an adaptable foundation for automated process control.