## 4 Conclusions  
This paper proposes an EDS yield prediction and interpretation framework with different types of variables amassed from the wafer fabrication procedure. To test and validate the framework, real fabrication data from a semiconductor manufacturer covering general investigation fields of yield analysis is used. In this case study, the model using the RF algorithm is selected as the best performing prediction model, following the evaluation process. The results provide experimental evidence of improved prediction scores, and the XAI-based analysis specifically provides insight into the relationship between wafer fabrication conditions and EDS yield. The analysis results identify the key features, the explanation of low or high yield wafers, and the relationship between features and yield values. Furthermore, the granular SHAP values enhance the understanding of the influence on yield by the principal features.  
The findings of this study have several important implications to improve the manufacturing yields of various products with diverse fabrication data. The attempt to use different kinds of data as input variables is important not only to increase the prediction performance but also to have knowledge from every corner related to wafer fabrication. This method is expandable to cover a broad database of complex manufacturing environments. Moreover, XAI assisting scrutiny would bring progress on yield enhancement and manufacturing management.  
The limitations of this study are explained here: Despite the inclusion of various manufacturing factors, numerous factors possibly related to yield are not part of this investigation. Some factors are mentioned at the end of the previous section, datasets with missing values and the other factors need to set the way to handle the data, including the condition of the fabrication line, the condition of source material, and information on preventive maintenance or excursions of equipment, among others. Another limitation is that the analyzed yield lowering factors, such as specific units of equipment, were not experimentally implemented in real chip production.  
In future work, the various factors need to be explored and included to build more expanded models. Practical experiments in semiconductor manufacturing are also suggested to validate the yield prediction model and the impact of the features, and the possibility of yield enhancement in the field can be tested. Another objective of further research would be the development of an integrated yield enhancement system containing automated yield analysis on the basis of this study. The system can help domain engineers in the field, including non-expert machine learning users, make data-driven decisions.  
The supporting files can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13042660/s1](https://www.mdpi.com/article/10.3390/app13042660/s1)]([https://www.mdpi.com/article/10.3390/app13042660/s1](https://www.mdpi.com/article/10.3390/app13042660/s1)), Figure S1: SHAP value plots for the top parameters of individual dataset models, Example code.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and Y.R.; visualization, Y.L.; supervision, Y.R.; project administration, Y.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank Enago (www.enago.co.kr accessed on 31 October 2022) for the English language review. Y.L. is indebted to [PERSON] at Samsung Electronics, who provided great advice during the paper revision process. The authors would also like to acknowledge Samsung Electronics for providing the real-world database. Finally, the authors would like to express gratitude to the reviewers and editors for their very helpful suggestions and insightful comments in reviewing the manuscripts.