Neural network characterization of plasma-induced charging damage on thick oxide-based metal-oxide-semiconductor device

Charging damage can critically degrade oxide reliability. Antenna-structured metal-oxide-semiconductor field-effect transistors were fabricated to examine the effect of process parameters on charging damage. Charging damage to threshold voltage ( V th ) was investigated experimentally as well as by constructing a neural network model. For a systematic modeling, charging damage process was characterized by means of a face-centered Box-Wilson experiment. The prediction performance of neural network model was optimized by applying genetic algorithm. A radio frequency source power was identified as the most influential factor. This could be more ascertained by the insignificant impact of bias power or gas ratio. Using the model, implications of plasma nonuniformity and polymer deposition were examined under various plasma conditions.