Low pressure plasma etching of silicon carbide

Abstract

A low pressure etching of silicon carbide is qualitatively characterized by using a neural network. To construct a predictive model, the etch process was characterized by means of a 25 full factorial experiment. Experimental factors that were varied include radio frequency (rf) source power, bias power, pressure, O2 fraction, and gap between the plasma source and wafer. An additional 15 experiments were conducted to test the appropriateness of the trained model. An optimized etch rate model has a root mean-squared error of 12.78 nm/min. Model response surface behaviors were certified by actual measurements. Several noticeable features at lower pressure etching include a lower etch rate, inverse relationship between the source power level and the dc bias, and a smaller etch rate variation with the source power. The effect of the bias power on the etch rate or dc bias was affected little by the pressure level. Etch mechanisms for the gap variations were quite different depending on the bias powers. Several etching aspects useful for plasma control were revealed.