A Novel Approach to Reducing Testing Costs and Minimizing Defect Escapes Using Dynamic Neighborhood Range and Shapley Values
Ni, Tianming Rui, Wangsheng Zhuo, Cheng Li, Yu Wen, Xiaoqing Nie, Mu
Ni, Tianming
Rui, Wangsheng
Zhuo, Cheng
Li, Yu
Wen, Xiaoqing
Nie, Mu
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Computational Biology
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Journal article
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English
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Abstract
Wafer acceptance testing (WAT) is a process that is used to assess the quality and reliability of manufactured wafers. This technique for the early detection and screening of chips allows for improvements in their reliability and performance during the manufacture of semiconductor devices. The automatic test equipment (ATE) used for processing millions of wafers is susceptible to a number of issues, including the absence of data values, the presence of redundant parameters, and categorical imbalance. These issues increase the cost of data processing and impede an investigation into the relationship between WAT and feature diagnostics. In this study, we propose a method with a low test escape rate based on a multi-objective optimization algorithm to reduce the cost of testing and minimize the number of defective dice that go undetected. The proposed method retains outliers, dynamically selects the range of the neighborhood to reduce the cost of testing, and uses Shapley values to analyze a WAT dataset to determine the importance of features of the data. The multi-objective optimization algorithm ranks features by their importance and applies an adaptive method to eliminate features with a low overall correlation, thereby reducing the risk that defective dice are undetected.
Citation
T. Ni, W. Rui, C. Zhuo, Y. Li, X. Wen, M. Nie, "A Novel Approach to Reducing Testing Costs and Minimizing Defect Escapes Using Dynamic Neighborhood Range and Shapley Values," ACM Transactions on Design Automation of Electronic Systems, vol. 31, no. 4, pp. 1-20, 2026, https://doi.org/10.1145/3716826.
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ACM Transactions on Design Automation of Electronic Systems
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Keywords
46 Information and Computing Sciences, 4605 Data Management and Data Science
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Association for Computing Machinery