Machine Learning (ML) based technologies are actively being adopted in the computational lithography domain. ML-based methods have the potential to enhance the accuracy of predictive models, speed up the run-times of the mask optimization processes and produce consistent results compared with the other numerical methods. In this paper, we present the result of an ML-based ILT application to an advanced DRAM contact layer for both core and periphery region. In our ML ILT method, golden mask layouts are generated by ProteusTM ILT tool for the sampled target layouts to obtain reliable training inputs, which are then used to train a custom-designed Convolutional Neural Network (CNN). The trained CNN is plugged-in to the conventional ILT flow as an initial mask provider and the entire
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