Developing a predictive model for nanoimprint lithography using artificial neural networks

Abstract Nanoimprint lithography (NIL) is a high-throughput and cost-effective technique for fabricating nanoscale features. However, the fine tuning of NIL process parameters is critical in achieving defect-free imprints. Currently, there exists no unified material and process design guidelines to deal with the complex set of process parameters. In this research, an artificial neural network (ANN) algorithm was developed to predict the imprint quality based on a set of input factors collected from experiments and literature. The prediction accuracy was tested in two stages by the use of three ANN models - General Regression Neural Network (GRNN), Back Propagation Neural Network (BPNN), and Probabilistic Neural Network (PNN). Higher prediction accuracy was observed for GRNN in both stages (1st stage = 58.22% and 2nd stage = 92.37%). A significant increase in the prediction accuracy was observed in the second stage after including additional input factors and larger data set. The average prediction accuracies for GRNN, BPNN, and PNN in the second stage were reported as 92.37%, 84.79%, and 91.19%, respectively. The prediction of the GRNN model was validated with experimental results confirming the accurate classification of all the outputs. This research lays the foundation for the development of an expert system for nanoimprint lithography.

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