A deep learning-based approach to material removal rate prediction in polishing

Abstract Prediction of material removal rate (MRR) during chemical mechanical polishing is critical for product quality control. Complexity involved in polishing makes it challenging to accurately predict MRR based on physical models. A data-driven technique based on Deep Belief Network (DBN) is investigated to reveal the relationship between MRR and polishing operation parameters such as pressure and rotational speeds of the wafer and pad. The effect of network structure and learning rate on the accuracy of predicted MRR is studied using particle swarm optimization algorithm. With an optimized network structure, the performance of DBN is experimentally verified, under varying operation conditions.

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