Use of neural networks to model low-temperature tungsten etch characteristics in high density SF6 plasma

Abstract

A tungsten (W) etch process in a SF6 helicon plasma has been modeled using neural networks. The process was characterized by a 24−1 fractional factorial experimental design. The design factors that were varied include source power, bias power, chuck holder temperature, and SF6 flow rate. The responses modeled include etch rate, selectivity, anisotropy, and nonuniformity. With optical emission spectroscopy, spectra of radical F intensity were collected to investigate the etch mechanisms. High prediction accuracy was achieved in the etch models. The root mean-square prediction errors were 249 Å/min, and 0.41, 0.16 and 0.83 for the etch rate, selectivity, anisotropy, and uniformity models, respectively. While exerting little impact on the selectivity, the temperature greatly affected the etch rate and anisotropy. In particular, the etch nonuniformity was improved at low temperature. Both the selectivity and nonuniformity were predominantly determined by the bias power. The anisotropy was inversely related to the F intensity. Consequently, W etching at temperatures ranging from −50 to −40 °C offers advantages in anisotropy and uniformity, without much sacrifice of the selectivity.