| Title: | A multi-objective feature selection and self-paced ensemble framework for semiconductor defect detection |
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| Authors: | ID Zheng, H. (Author)
ID Gao, X. (Author)
ID Yang, X. (Author)
ID Jing, G. (Author)
ID Yang, M. (Author)
ID Liu, Y. (Author) |
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| Files: |  APEM20-4_458-474.pdf (1,50 MB)
MD5: 748D724F1D628131B3F2C195040F945E
 https://apem-journal.org/Archives/2025/Abstract-APEM20-4_458-474.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | In semiconductor manufacturing, defect detection is commonly performed using high-dimensional process data. These data often exhibit class imbalance and class overlap, which create challenges for achieving reliable classification performance. To address these issues, this study proposes a multi-objective feature selection and self-paced ensemble (MOFS-SPE) framework. The framework employs a multi-objective evolutionary algorithm based on decomposition (MOEA/D) for feature selection. In this process, the area under the precision–recall curve (AUPRC) and the R-value are used as objective functions to identify feature subsets that are highly relevant to quality outcomes. In addition, the framework integrates the self-paced ensemble (SPE) with tree-based classifiers to handle imbalanced and overlapping data. Experiments conducted on a real semiconductor manufacturing dataset (SECOM dataset) demonstrate the effectiveness of the proposed approach. Compared with using the full feature set, the selected features increase the area under the receiver operating characteristic curve (AUROC) from 0.685 to 0.770 and the AUPRC from 0.932 to 0.972. When applying the SPE framework, the specificity of the decision tree model improves from 0.048 to 0.667, thereby enhancing the reliability of identifying defective products. Overall, the proposed framework provides a useful reference for intelligent quality inspection in semiconductor production environments. |
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| Keywords: | semiconductor manufacturing, defect detection, quality inspection, class imbalance, class overlap, multi-objective feature selection, self-paced ensemble, machine learning |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 27.08.2025 |
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| Article acceptance date: | 30.12.2025 |
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| Publication date: | 31.12.2025 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 458-474 |
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| Numbering: | Vol. 20, no. 4 |
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| PID: | 20.500.12556/DKUM-96695  |
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| UDC: | 658.562 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 265921027 |
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| DOI: | 10.14743/apem2025.4.552 |
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| Publication date in DKUM: | 23.01.2026 |
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| Views: | 186 |
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| Downloads: | 4 |
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| Metadata: |    |
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| Categories: | Misc.
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