Name
Abstract | ||
|---|---|---|
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 |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/ICNN.1996.549215 | Neural Networks, 1996., IEEE International Conference |
Keywords | Field | DocType |
adaptive control,feedback,neural nets,process control,sputter etching,anisotropy,complex fabrication processes,critical process responses,etch rate,predictive model,reactive ion etching,real-time model-based feedback control,selectivity,semiconductor manufacturers,uniformity,semiconductor manufacturing,real time,etching,prediction model,real time control,fabrication,manufacturing industries,feedback control,neural networks,circuits,neural network | Etching,Electronic engineering,Reactive-ion etching,Process control,Adaptive control,Electronic circuit,Artificial neural network,Materials science,Fabrication,Semiconductor | Conference |
Volume | ISBN | Citations |
4 | 0-7803-3210-5 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kim, T. | 1 | 0 | 0.34 |
| Stokes, D. | 2 | 0 | 0.34 |
| G. S. May | 3 | 6 | 1.91 |