Most IC manufacturers use CMP modeling to detect potential hotspots as part of their DFM flow. However, building physics-based or compact models for FCVD and eHARP CMP processes has proven challenging, since these processes include several deposition and annealing steps to fill up trenches.

Experiments show that using machine learning and neural networks for oxide deposition profile modeling for these and other CMP processes is a promising and exciting use of this technology.

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