Defect Prediction and Optimization in Semiconductor Manufacturing Using Explainable AutoML

Min Yin

doi:10.70393/616a6e73.333533

OPEN ACCESS |Research Article ||3 December 2025

Defect Prediction and Optimization in Semiconductor Manufacturing Using Explainable AutoML

Min Yin

University of California-Berkeley

gmiayinc@gmail.com

University of California-Berkeley, 94720, USA.

* Corresponding Author¹: Min Yin, E-Mail: gmiayinc@gmail.com

Publication

Accepted 2025 December 3 ; Published 2025 December 3

Academic Journal of Natural Science, 2025, 2(4), 3078-5170.

Abstract

The semiconductor manufacturing industry often faces the severe challenges of data scarcity and imbalance. While the semiconductor industry has conducted extensive research on leveraging machine learning to improve yield, defect prediction remains largely unexplored, especially with small datasets. This research proposes a framework called xAutoML, which automatically selects the optimal model and hyperparameters for defect prediction to enhance the interpretability of the results. Furthermore, it addresses the critical issue of data imbalance, a common problem in defect prediction tasks, by employing techniques such as focus loss and oversampling. We use publicly available datasets to demonstrate how xAutoML effectively adapts to data constraints and deeply analyzes key features influencing defect occurrence. Results show that the proposed method outperforms traditional methods in terms of prediction accuracy and the provision of actionable and interpretable insights. Its application in real-time defect monitoring and process optimization in semiconductor manufacturing helps bridge the gap between advanced machine learning techniques and practical industry applications.

Keywords

Explainable AutoML , XAutoML , Semiconductor Manufacturing , Defect Prediction , Data Imbalance , Machine Learning , Model Interpretability , Process Optimization .

Metadata

DOI:

10.70393/616a6e73.333533

ARK:

ark:/40704/AJNS.v2n4a01

Pages: 1-10

References: 29

Disciplines: Computer Science

Subjects: Artificial Intelligence

Cite This Article

APA Style

Yin, M. (2025). Defect prediction and optimization in semiconductor manufacturing using explainable automl. Academic Journal of Natural Science, 2(4), 1-10. https://doi.org/10.70393/616a6e73.333533

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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