Novel Structure of a Deep Neural Network-Based Material Removal Rate Prediction Model for Multizone Chemical Mechanical Planarization Process Control

Multizone pressure chemical mechanical planarization (CMP), which is regarded as irreplaceable in semiconductor manufacturing, is highly dependent on a process control strategy. However, predictive models with strict rules for advanced CMP multizone pressure process control have rarely been examined, even with the development of machine learning applications in semiconductor fabrication. Establishing a precise data-driven model that takes the fabrication conditions into consideration is urgent f