Using various machine learning methods to model post-ECD surface profiles and comparing the results enables you to determine which architectures and models provide the best combination of running time and accuracy.
Accurate modeling of post-ECD surface topography variation is crucial for correct CMP simulation. Siemens and the American University of Armenia collaborated to investigate and evaluate the use of machine learning (ML) modeling techniques to predict these complicated topography variations.
Using various ML methods to model post-ECD surface profiles and comparing the results enabled them to determine which architectures and models provided the best combination of running time and accuracy.
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