Prediction of Material Removal Rate for Chemical Mechanical Planarization using Bayesian Additive Regression Trees
DOI:
https://doi.org/10.23055/ijietap.2026.33.5.11203Abstract
Chemical Mechanical Planarization (CMP) is a critical surface processing technique in semiconductor manufacturing, where the Material Removal Rate (MRR) plays a vital role in determining process quality. Traditional physics-driven MRR models based on Preston's Equation have difficulty capturing the complex nonlinear relationships and high-order interactions present in CMP data. This paper applies Bayesian Additive Regression Trees (BART) to CMP MRR prediction. BART uses a Bayesian sum-of-trees framework to model nonlinear effects and interactions while providing posterior uncertainty estimates and variable-importance information. Quantitative comparisons on the validation set show that BART achieves the best predictive performance, outperforming the strongest baseline model by 11% in terms of RMSE. Model reliability and interpretation are examined through residual diagnostics, MCMC convergence checks, hyperparameter-sensitivity analysis, omnibus covariate testing, and variable-importance analysis. These results indicate that BART is effective for CMP MRR prediction and can help identify process variables that warrant monitoring and diagnostic attention.
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