Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328)
DOI: 10.1109/cca.1999.807760
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Real-time plasma etch control using in-situ sensors and neural networks

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“…The authors report an 83% improvement in etch depth results compared to a purely timed etch. A model-based feedback controller is reported in [68] and [100], controlling etch rate, which is measured using laser inferometry and a profilometer. Manipulated variables are pressure, RF power, and gas flow, and a linear LQG/LTR controller is compared to a nonlinear adaptive controller based on a neural network model.…”
Section: ) Control Of Etch Variablesmentioning
confidence: 99%
“…The authors report an 83% improvement in etch depth results compared to a purely timed etch. A model-based feedback controller is reported in [68] and [100], controlling etch rate, which is measured using laser inferometry and a profilometer. Manipulated variables are pressure, RF power, and gas flow, and a linear LQG/LTR controller is compared to a nonlinear adaptive controller based on a neural network model.…”
Section: ) Control Of Etch Variablesmentioning
confidence: 99%