Using a neural network, a profile roughness of plasma etching is characterized. The etching was conducted in a CHF3/CF4 inductively coupled plasma. The etch process was characterized by a 2 3 full factorial experiment. The process parameters that were varied in the design include radio frequency source and bias powers, and gas ratio. Relationships between the parameters and profile roughness were captured by training neural network with eight experiments plus one center experiment. Model appropriateness was tested with six experiments not pertaining to the training data. Model prediction capability was optimized by means of a genetic algorithm (GA). Compared to a conventional model, GA-optimized model demonstrated a drastic improvement of about 54% in predicting profile roughness. From the optimized model, several plots were generated to examine parameter effects on the profile roughness. Increasing the source power (or bias power) under high bias power (or source power) increased the profile roughness. More significant effect of the bias power was revealed. The profile roughness decreased with increasing the gas ratio was strongly correlated to the dc bias. The little variation in the profile roughness was ascribed to chamber plasma condition maintained at relatively low dc bias. (C) 2003 Elsevier B.V. All rights reserved.