1 July 2003 Neural network-based run-to-run controller using exposure and resist thickness adjustment
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Abstract
This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.
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Shane Geary, Shane Geary, Ronan Barry, Ronan Barry, } "Neural network-based run-to-run controller using exposure and resist thickness adjustment", Proc. SPIE 5044, Advanced Process Control and Automation, (1 July 2003); doi: 10.1117/12.485298; https://doi.org/10.1117/12.485298
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