Modeling SiC etching in C2F6/O2 inductively coupled plasma using neural networks

Abstract

Silicon carbide (SiC) has been etched in a C2F6/O2 inductively coupled plasma and modeled using neural networks. A 25 full factorial experiment was used to characterize the relationships between input process factors and etch response. The factors that were varied include source power, bias power, pressure, O2 fraction, and gap between the chuck holder and coil antenna. Neural networks were trained on the resultant 32 experiments and then tested on 18 additional experiments to evaluate prediction accuracy. Due to little variations in etch anisotropy, etch rate was only modeled and its root-mean-squared prediction error was 23.9 nm/min. Etch rate was found to be a strong function of source power. Increasing etch rate with pressure may partly be attributed to increased ion density and ion energy. Placing the chuck holder closer to the source antenna coil increased the etch rate. At higher bias powers, increasing the O2 fraction resulted in a crossover. This crossover seems to be weakened significantly with a decrease in bias power. Although etch anisotropy did not vary consistently with source power, it improved consistently with bias power. Microtrenches were noticed for variations in each of the five factors. With increasing pressure, the anisotropy was slightly degraded while being insensitive to a variation in the gap.