Modeling SiC surface roughness using neural network and atomic force microscopy

Abstract

A prediction model for surface roughness was constructed using a neural network and atomic force microscopy. The silicon carbide etch process was characterized by a 2 5 full factorial experiment. The experimental ranges of process parameters were 600-900 W source power, 50-150 W bias power, 4 -16 mTorr pressure, 0-80% O 2 percentage, and 6 -12 cm gap. The model factors were optimized by means of a genetic algorithm. The optimized model had a root mean-squared error of 0.11 nm. From the model, various plots were predicted while being supported by actual measurements. The dc bias induced by each process parameter was correlated to the surface roughness. Increasing the bias power increased the surface roughness. In contrast, the surface roughness decreased as the dc bias was larger than about 600 V. The surface roughness was strongly correlated to the source power-induced dc bias only at low bias powers. The pressure effect was clear only as the dc bias was maintained at 480 V. For the variations in the O 2 percentage, the dc bias role was insignificant.

An inverse problem solution for undetermined electrostatic force microscopy setups using neural networks

“…Although the GICM and ANNs have been used before to analyze and improve the resolution and system stability [23][24][25][26], they have never been combined to quantitatively measure and predict unknown magnitudes in SPM.…”

Prediction of Surface Roughness Using X-Ray Photoelectron Spectroscopy and Neural Networks

“…In the context of plasma etching, surface roughness has been predicted only in terms of conventional process parameters for process characterization. 18 Prediction of surface roughness with XPS is first examined in this study.…”