Data-driven Approach to Material Removal Rate Prediction in Chemical Mechanical Polishing

Abstract

Chemical mechanical polishing (CMP) has been widely used in the semiconductor sector for creating planar surfaces with the combination of chemical and mechanical forces. CMP is very complex because several chemical and mechanical phenomena (e.g., surface kinetics, contact mechanics, stress mechanics, and tribochemistry) are involved. Due to the complexity of the CMP process, it is very challenging to predict material removal rate (MRR) with sufficient accuracy. While physics-based methods have been introduced to predict MRR, little research has been reported on data-driven predictive modeling of MRR in the CMP process. This paper presents a novel decision tree-based ensemble learning algorithm that trains a predictive model of MRR on condition monitoring data. A stacking technique is used to combine three decision tree-based learning algorithms, including the random forests (RF), gradient boosting trees (GBT), and extremely randomized trees (ERT). The proposed method is demonstrated on the data collected from a wafer CMP tool that removes material from the surface of the wafer. Experimental results have shown that the decision tree-based ensemble learning algorithm can predict MRR in the CMP process with very high accuracy.