## 2 Related Work  
Over the past decade, many studies have been proposed that consider the parameters and circumstances involved in the polishing process from the perspectives of the physical and chemical processes. [PERSON] et al. [11] investigated the effects of the pH, oxidizer concentration, and slurry flow rate on the MRR. They showed that a higher oxidizer concentration or a higher slurry flow rate resulted in a higher MRR when the CMP was completed. This can be explained by an adhesive removal mechanism on the molecular scale. [PERSON] and [PERSON] [12] discovered the cross-effect between the abrasion, wafer, particle size, and pad in the microcontact view, in which the sources of pressure are the pad deformation and number of abrasive particles. They also considered the relationship between the wafer surface hardness, slurry concentration, and slurry particle size. [PERSON] et al. [13] and [PERSON] et al. [14] investigated how the roughness of the pad surface affected the material removal process in CMP and discovered and explained the relationship between the MRR and the level of surface roughness of the pad. [PERSON] et al. (2015) developed an analytical MRR model to scale key process variables and replicated the surface finishing task performed by the manual operator. [PERSON] et al. (2016) investigated how the wafer material affects the MRR by conducting mechanistic experiments and showed that the removal level is determined by the wafer deformation capability rather than the material hardness of the wafer. Thus, if the wafer material has a higher deformation ability, a higher MRR is obtained. [PERSON] and [PERSON] (2017) presented an integrated MRR model of the MRR for silicon dioxide in CMP with the slurry chemical diffusion model. By comparing the results with the experimental dataset in the literature, the validity of the proposed model was supported. The last decade has witnessed great progress in machine learning because of the advances in machine learning and the presence of big data ([PERSON] et al., 2018; [PERSON] et al., 2019). The key idea behind machine learning is to learn predictive models from the data ([PERSON] et al., 2018). Once the learning process has been completed, the learned models make predictions on unseen data. Due to the growing demand for the accuracy, stability, and reliability of VM, many machine learning models, such as linear regression (LR), multivariate linear regression (MLR), multitask learning, and artificial neural networks, are commonly used in VM ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020). To cope with the problem that a model involves enormous features, feature selection is a commonly used technique. The goal is to select a subset of features from the original feature set. For example, to avoid the negative effects of many features, [PERSON] and [PERSON] (2016) used partial least squares (PLS) and variable importance in the projection to process 57,600 features, in which PLS was applied to find the fundamental relationship between features and output. Their proposed model tried to find the multidimensional direction in the feature space that explained the maximum multidimensional variance direction in the output space by removing the redundant features until the remaining number of features reached the target number. [PERSON] et al. (2018) focused on the chemical-mechanical process for polishing a color filter (CMP-CF). They proposed an adaptive network-based fuzzy inference system with sliding-level particle swarm optimization (SL-PSO) to optimize the CMP-CF parameters. In the 2016 PHM Data Challenge, [PERSON] et al. (2018) discovered two new types of features that can improve prediction accuracy. The first feature was the \"Time Neighbor Features\" since they found out that there exists a strong correlation between the MRR at time \(t\), that is, MRR(\(t\)), and MRR at \(t-1\), that is, MRR(\(t-1\)). The relationship can be extended to the previous time steps.