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http://dx.doi.org/10.5302/J.ICROS.2007.13.4.315

Genetic Control of Learning and Prediction: Application to Modeling of Plasma Etch Process Data  

Uh, Hyung-Soo (세종대학교 전자공학과)
Gwak, Kwan-Woong (세종대학교 기계공학과)
Kim, Byung-Whan (세종대학교 전자공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.13, no.4, 2007 , pp. 315-319 More about this Journal
Abstract
A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.
Keywords
backpropagation neural network; genetic algorithm; plasma etching; model;
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