Prediction of Radio Frequency Impedance Matching in Plasma Equipment Using Neural Network
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 3973)
Optimizing a plasma impedance match process requires construction of prediction model. In this study, generalized regression neural network (GRNN) combined with genetic algorithm (GA) was used to build a match prediction model. A real-time match monitor system was used to collect steady match positions according to a statistical experimental design. GA-GRNN models were compared to GRNN and statistical regression models. Compared to GRNN models, GA-GRNN models demonstrated improved predictions of about 81% and 77% for the impedance and phase positions, respectively. With respect to statistical regression models, GA-GRNN models yielded an improvement of about 80% and 78%, respectively. Moreover, for either model type, the improvements for the training errors were more than about 90% for both positions.
KeywordsImpedance Match Generalize Regression Neural Network Match Network Pattern Layer Phase Position
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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