Modeling Electrochemical Deposition with Machine Learning for Chemical Mechanical Simulation

This technical paper reviews the importance of accurate modeling of postelectrochemical deposition surface topography variation for optimum chemical mechanical polishing simulations. Siemens EDA and the American University of Armenia collaborated to evaluate the use of machine learning (ML) modeling techniques to predict these complicated topography variations. Using various ML methods to model postECD surface profiles enabled them to determine which models provided the best combination of running time and accuracy.