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4 April 2019 Using machine learning in the physical modeling of lithographic processes
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Abstract
We show how combining machine learning with physical models can improve the overall accuracy of modeling the lithographic process for OPC applications by up to 40%. This level of model accuracy improvement is critical to meet the stringent requirements of the 5nm node and below. We demonstrate how the judicious design of the neural network can create a model capable of high accuracy and high contour quality, even when no contour data is available. This allows the neural network model to be introduced without disrupting the model calibration flow used in OPC.
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Kostas Adam, Shashidhara Ganjugunte, Clement Moyroud, Kostya Shchehlik, Michael Lam, Andrew Burbine, Germain Fenger, and Yuri Granik "Using machine learning in the physical modeling of lithographic processes", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620F (4 April 2019); doi: 10.1117/12.2519848; https://doi.org/10.1117/12.2519848
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