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• W4321374432 abstract "Enormous amounts of data are generated and analyzed in the latest semiconductor industry. Established yield prediction studies have dealt with one type of data or a dataset from one procedure. However, semiconductor device fabrication comprises hundreds of processes, and various factors affect device yields. This challenge is addressed in this study by using an expandable input data-based framework to include divergent factors in the prediction and by adapting explainable artificial intelligence (XAI), which utilizes model interpretation to modify fabrication conditions. After preprocessing the data, the procedure of optimizing and comparing several machine learning models is followed to select the best performing model for the dataset, which is a random forest (RF) regression with a root mean square error (RMSE) value of 0.648. The prediction results enhance production management, and the explanations of the model deepen the understanding of yield-related factors with Shapley additive explanation (SHAP) values. This work provides evidence with an empirical case study of device production data. The framework improves prediction accuracy, and the relationships between yield and features are illustrated with the SHAP value. The proposed approach can potentially analyze expandable fields of fabrication conditions to interpret multifaceted semiconductor manufacturing." @default.
• W4321374432 created "2023-02-21" @default.
• W4321374432 creator A5008458781 @default.
• W4321374432 creator A5029769355 @default.
• W4321374432 date "2023-02-18" @default.
• W4321374432 modified "2023-09-26" @default.
• W4321374432 title "An Expandable Yield Prediction Framework Using Explainable Artificial Intelligence for Semiconductor Manufacturing" @default.
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• W4321374432 doi "https://doi.org/10.3390/app13042660" @default.
• W4321374432 hasPublicationYear "2023" @default.
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