Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/46406
標題: Developing a neural network-based run-to-run process controller for chemical-mechanical planarization
作者: Wang, G.J.
王國禎
Yu, C.H.
關鍵字: chemical-mechanical planarization;neural network control;run-to-run;control;optimization
Project: International Journal of Advanced Manufacturing Technology
期刊/報告no:: International Journal of Advanced Manufacturing Technology, Volume 28, Issue 9, Page(s) 899-908.
摘要: 
A new neural network-based run-to-run process control system (NNRtRC) is proposed in this article. The key characteristic of this NNRtRC is that the linear and stationary process estimator and controller in the exponentially weighted moving average (EWMA) run-to-run control scheme are replaced by two multilayer feed-forward neural networks. An efficient learning algorithm inspired by the sliding mode control law is suggested for the neural network-based run-to-run controller. Computer simulations illustrate that the proposed NNRtRC performs better than the EWMA approach in terms of draft suppression and adaptation to environmental change. Experimental results show that the NNRtRC can precisely trace the desired target of material removal rate (MRR) and keep the within wafer non-uniformity (WIWNU) in an acceptable range.
URI: http://hdl.handle.net/11455/46406
ISSN: 0268-3768
DOI: 10.1007/s00170-004-2451-6
Appears in Collections:生醫工程研究所

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