The prediction of the average material removal rate (MRR) in chemical mechanical planarization (CMP) process has been recognized to be a critical factor of virtual metrology (VM) modeling for advanced process control (APC). This paper proposes a Gaussian process regression (GPR)-based model to dynamically predict MRR in CMP process. The proposed method uses K-nearest neighbor (KNN) to search for reference MRR samples in the historical dataset. Furthermore, a GPR model is trained to fuse the information from reference samples. Finally, the proposed method employs multi-task Gaussian process (MTGP) to predict the final MRR and quantify the prediction uncertainty based on the historical and the reference MRR. Compared with other methods in the recent literature, the proposed method, named KNN-MTGP model, yields better prediction accuracy than ensemble models, and comparable accuracy with deep neural networks (NN). Besides, KNN-MTGP model is capable to demonstrate the behavior of the past MRR changing with time and provide quantified prediction uncertainties. In this paper, the feasibility and advantages of KNN-MTGP model are evaluated based on the dataset of 2016 prognostic and health management (PHM) data challenge.

化学机械平面化（CMP）过程中平均材料去除率（MRR）的预测已被认为是用于高级过程控制（APC）的虚拟计量（VM）建模的关键因素。本文提出了一种基于高斯过程回归（GPR）的模型来动态预测CMP过程中的MRR。所提出的方法使用K最近邻（KNN）在历史数据集中搜索参考MRR样本。此外，训练了GPR模型以融合参考样本中的信息。最后，该方法采用多任务高斯过程（MTGP）来预测最终MRR，并基于历史MRR和参考MRR来量化预测不确定性。与最近文献中的其他方法相比，该方法称为KNN-MTGP模型，产生比集成模型更好的预测精度，以及与深度神经网络（NN）相当的精度。此外，KNN-MTGP模型能够证明过去MRR随时间变化的行为，并提供量化的预测不确定性。本文基于2016年预后与健康管理（PHM）数据挑战的数据集，评估了KNN-MTGP模型的可行性和优势。