A Machine Learning Approach to CMP Modeling
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. In this whitepaper from Siemens Digital Industries Software, you will learn about:
- Building a CMP model
- Defining Neural Network configurations
- Using applications of Neural Networks to profile modeling for CMP
- Modeling FCVD and EHARP surface profiles with Neural Networks