###### Abstract  
Predicting the wafer material removal rate (MRR) is an important step in semiconductor manufacturing for total quality control. This work proposes a deep learning model called a fusion network to predict the MRR, in which we consider separating features into shallow and deep features and use the characteristics of deep learning to perform a fusion of these two kinds of features. In the proposed model, the deep features go through a sequence of nonlinear transformations and the goal is to learn the complex interactions among the features to obtain the deep feature embeddings. Additionally, the proposed method is flexible and can incorporate domain knowledge into the model by encoding the knowledge as shallow features. Once the learning of deep features is completed, the proposed model uses the shallow features and the learned deep feature embeddings to obtain new features for the subsequent layers. This work performs experiments on a dataset from the 2016 Prognostics and Health Management Data Challenge. The experimental results show that the proposed model outperforms the competition winner and three ensemble learning methods. The proposed method is a single model, whereas the comparison methods are ensemble models. Besides the experimental results, we conduct extensive experiments to analyze the proposed method. c 202212211478 (202212211478) 202212211478 202212211478 202212211478 202212211478 20221  
Taking into account the complicated interactions of the chemical and mechanical processes, many relevant parameters are related to the MRR including the slurry (e.g., flow rate and cohesion), operating environment (e.g., humidity and temperature), mechanism (e.g., rotational speed and pressure), polishing pad (e.g., thickness and heat resistance level), and membrane (e.g., hardness) [7; 8; 9]. Therefore, developing a predictive model with enormous process parameters is a valuable and challenging task. The last decade has witnessed the outstanding progress of deep learning, which enables models to learn feature representations and perform classifications simultaneously [10]. This can reduce the overhead caused by manual feature extraction, which is used by traditional machine learning methods. Thus, this work proposes a deep learning model called the fusion network to predict the MRR in which data science approaches are used to analyze the data. We consider separating the features into shallow and deep features instead of using the samples with all the features as inputs. Our developed model aims to use the characteristics of deep learning to merge these two types of features. In the developed model, the deep features go through a sequence of nonlinear transformations and the goal is to learn the complex interactions among the features to obtain the deep feature embeddings. Additionally, the proposed method incorporates domain knowledge into the model by encoding the knowledge as shallow features. Once the learning of deep features is completed, the proposed model combines the shallow features and the deep feature embeddings learned to obtain new features for the subsequent layers. We conduct experiments on the dataset obtained from the 2016 Prognostic and Health Management (PHM) Data Challenge, collected by virtual metrology (VM) technology to evaluate the proposed method. In the experiments, we compare our proposed method with the winner of the competition and with three ensemble learning methods. Compared to the comparison methods that use ensemble learning to combine several base models to boost model performance, our proposed fusion network comprises only a single model. However, the proposed model can still outperform the alternatives. The contributions of this work are as follows:  
* The proposed method is a deep learning model that can incorporate domain knowledge into the model by encoding the knowledge as shallow features. Additionally, the remaining features can go through a deep neural network to learn discriminative feature embeddings.