Paper Title
Optimized CatBoost-Based Modeling for Accurate Semiconductor Yield Diagnosis and Tuning
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Registration ID: IJNRD_325511
Published ID: IJNRD2605694
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Keywords
CatBoost, Machine Learning, Yield Prediction, Semiconductor
Abstract
Yield diagnosis and tuning in emerging semiconductor devices remains challenging due to limited experimental data and high fabrication costs during the research stage. Traditional machine learning models such as Support Vector Machine, Random Forest, and XGBoost provide reasonable predictions but often suffer from overfitting and poor handling of complex feature interactions. This work introduces an extension by integrating the CatBoost algorithm into the existing yield prediction framework. The proposed approach utilizes process parameters and electrical characteristics to model yield behavior and identify key influencing factors. Bayesian optimization is employed to iteratively refine model parameters and improve prediction performance. The extended model demonstrates improved accuracy with lower mean absolute error and higher explained variance compared to conventional methods. CatBoost effectively handles encoded features and data imbalance, leading to more stable predictions. This approach reduces the need for extensive experimentation and enables efficient yield optimization in early-stage semiconductor research environments.
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How To Cite (APA)
KOPPISETTI VENKATA SURYA, Mr. K. PRAVARDHAN, & Dr. V.S.V. DEEPAK (May-2026). Optimized CatBoost-Based Modeling for Accurate Semiconductor Yield Diagnosis and Tuning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), g769-g774. https://ijnrd.org/papers/IJNRD2605694.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : g769-g774
Other Publication Details
Paper Reg. ID: IJNRD_325511
Published Paper Id: IJNRD2605694
Research Area: Other area not in list
Author Type: Indian Author
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605694.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605694
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