Real-time feedback control of reactive ion etching using neural networks

Consistent demands on semiconductor manufacturers to produce circuits with increased density and complexity have made stringent process control an issue of growing importance in the industry. Recent work has shown that neural networks offer great promise in modeling complex fabrication processes such as reactive ion etching (RIE). Motivated by these results, this paper explores the use of neural networks for real-time, model-based feedback control of RIE. This objective is accomplished in part by constructing a predictive model for the system, which can be inverted (or approximately inverted) to achieve the desired control. The efficacy of this approach is demonstrated using experimental data from an actual RIE process to examine real-time control of critical process responses such as etch rate, uniformity, selectivity, and anisotropy