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• W2766178794 abstract "Lithographic wafer scanners or wafer steppers are the machines used for the manufacturing of Integrated Circuits (ICs). Modeling based approaches are being used to simulate the massive wafer scanner machines. Modeling consumes much time as well as expensive to build and conduct experiments with it. So, the actual implementation set up on wafer scanners with precise positioning measures becomes a difficult process. Research works are going on using data-determined approaches in wafer manufacturing process. In this paper, a novel approach is proposed to minimize the desired IC pattern's trajectory tracking error in a wafer scanner. The data-driven procedure provides a model-free environment and the Iterative Feedback Tuning (IFT) enables exact controller tuning with the measured records. Through ANN, the trajectory tracking performance was found to be improved and is capable to apply in lithographic wafer scanners for better machine performance." @default.
• W2766178794 created "2017-11-10" @default.
• W2766178794 creator A5006044638 @default.
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• W2766178794 date "2017-08-01" @default.
• W2766178794 modified "2023-09-23" @default.
• W2766178794 title "Adaptive fault reduction of a wafer scanning system using artificial neural network" @default.
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• W2766178794 doi "https://doi.org/10.1109/spices.2017.8091306" @default.
• W2766178794 hasPublicationYear "2017" @default.
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