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Developing a neural network-based run-to-run process controller for chemical-mechanical planarization

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Abstract

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 nonuniformity (WIWNU) in an acceptable range.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chung-Hsing University, 402, Taichung, Taiwan

    Gou-Jen Wang & Cheng-Hsiong Yu

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  1. Gou-Jen Wang
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  2. Cheng-Hsiong Yu
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Correspondence to Gou-Jen Wang.

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Wang, GJ., Yu, CH. Developing a neural network-based run-to-run process controller for chemical-mechanical planarization. Int J Adv Manuf Technol 28, 899–908 (2006). https://doi.org/10.1007/s00170-004-2451-6

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  • DOI: https://doi.org/10.1007/s00170-004-2451-6

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