## 3 Results and Discussion
### Discussion and Limitation  
The proposed framework to predict EDS yield with extensive wafer fabrication data is demonstrated with real-world data. The dataset consists of diverse data applied for practical yield analysis in the field. The 10 different machine learning models are optimized and compared, to select the best performing prediction model for the data. The chosen RF model shows improved prediction scores, which are 0.520 for MAE and 0.648 for RMSE. We employ the XAI method, i.e., SHAP, to explain the model and present the relationship between the key features and the yield. Thus, this study raises the possibility of practical yield prediction with expandable fabrication data and interpretation through SHAP analysis.  
The cautious application of feature analysis is necessary because these relationships, inferred from the SHAP value scatterplot over feature values, do not guarantee causality. Therefore, the counteraction on the fabrication process should be considered carefully with domain knowledge and proper experiments to establish possible causation. Knowledge derived based on the XAI analysis is possibly considered a controlling factor in the wafer fabrication processes, although the physical or chemical background of this phenomenon needs to be discussed and examined through further research. The framework goes beyond simply predicting the yield and listing important features, that is XAI informs how each feature is reflected in the yield prediction and how the yield of each wafer is predicted based on the correlation in the model. Therefore, the XAI method increases the transparency of the model to improve usability.  
The limitations of this study must be acknowledged. For new trial operating conditions, modification and retraining of the model are necessary. Some datasets from wafer fabrication are not included in the analysis because of their characteristics. As a typical example, metrology and inspection data have a high missing rate, making the utilization of these data challenging in this framework. Nevertheless, there is research that proposes a method for identifying the key steps in fabrication processes using missing value imputation [22] and other studies that focus on advanced imputation mechanisms, such as virtual metrology (VM) [45; 46]. Moreover, there is various information related to quality control, e.g., line condition, equipment maintenance [17; 47], source material change, and engineers' notes. Without wafer information, these datasets necessitate a complicated conversion in order to serve as the input dataset for this modeling. To counteract the expansion of input variables in future studies, the principal component analysis can improve dimension reduction efficiency during preprocessing [13; 19; 48]. Another limitation is that the yield-improving action based on the analysis results needs to consider various aspects of manufacturing, such as production efficiency, equipment maintenance costs, and serial changes to other features. For example, some time span parameters affect the yield, as shown in Figure 5; however, the restriction on the features would affect prior and later steps and require advanced scheduling [49; 50]. In addition, deep learning models are not studied in this paper due to the limited number of data points but are necessarily included in future research [12; 51]. Furthermore, a study of the SHAP method with non-tree-based models is required, considering other candidate models such as KNN and SVR [42; 52].