## 1 Introduction
Recently, machine learning techniques have attracted much attention as they enable researchers to handle large-scale data and automate analysis. In numerous prediction studies in diversified fields including information technology, geographical science, industrial manufacturing, and energy production, established regression models for each research purpose are used through comparison and selection among various machine learning and deep learning models [7; 8; 9; 10; 11; 12]. In our study, we adopted the procedure of choosing the most fitting model for the prediction. Specifically, the machine learning-based method has been applied in various studies in the semiconductor industry [2; 13; 14; 15; 16; 17; 18]. In our view, yield research mainly focuses on analysis with one kind of input variable. Various types of independent variables exist; however, the most used variables are one type or from a dataset obtained in one test procedure. One study focuses on non-normal distribution test parameters to estimate the yield using the principal component analysis [19]. Considering the delays of gates and paths on a chip, researchers predict parametric yield using statistical timing analysis [20]. Machine learning-based root cause detection approaches are developed to target a specific circuit test yield [21]. The existing approaches for metrology data usage for yield classification focus on the imputation of missing data and counteraction of the imbalanced classes [22]. There are studies on back-end FT yield regression and classification modeling based on front-end WAT data [1; 23]. Some studies preprocess the categorical input data using one-hot encoder or other encoders to utilize non-numerical data as input data for machine learning models, and the categorical and numerical data are recorded in the same step [1; 24]. In previous research, big data analysis for low yield validates its detection efficiency with simulation, especially for the development of new devices [25]. There is a study to modify the gradient boosting algorithm model to analyze multi-step data from semiconductor manufacturing [26]. Regarding XAI, a few studies have applied this method for semiconductor manufacturing. As discussed in the literature, XAI has been studied to apply artificial intelligence with more transparency while maintaining high performance [27]. Additionally, the explainability makes the continuous improvement of models possible and approves AI-baseddecisions. The SHAP value method is adopted to improve process quality in the real production of semiconductor devices [28]. SHAP analysis is used to rank the features that affect the electrical test scores of the device [24]. To the best of our knowledge, few studies cover machine learning-based modeling using various kinds of wafer fabrication data to predict EDS yield and explain each wafer and feature with the XAI method. This study uses different types of data from the whole wafer fabrication stage as input data to examine yield impact. Multiple machine learning models are trained to compare prediction performance for the wafer yield and the top model is explained on the basis of SHAP values. Brief explanations of the utilized machine learning algorithms and the SHAP value method are mentioned in the proposed framework part of this manuscript. Less attention has been focused on the analysis of multiple kinds of data in the front-end production stage to predict EDS yield. Therefore, this paper aims to predict wafer yield based on combined fabrication data to include possible causes in investigating as many fields as possible and speeding up the feedback. The framework is expanded by interpreting the model to determine the yield-lowering factors and by explaining the relationship between factors and yields. The XAI technique-based SHAP decomposes individual attribution of target-affecting factors [29; 30].