Y. L. Yang, P. Tsao, C. W. Lin, Ross Lee, Olivia Ni, T. T. Chen, Y. Ting, C. Lai, Jason Yeh, Arnold Yang, Wayne Huang, Peng Chen, Charly Tsai, Ryan Yang, Y. S. Huang, B. Hsu, M. Z. Lee, T. Lee, Michael Huang, Coming Chen, L. Chu, H. Kao, N. S. Tsai
{"title":"Performing Machine Learning Based Outlier Detection for Automotive Grade Products","authors":"Y. L. Yang, P. Tsao, C. W. Lin, Ross Lee, Olivia Ni, T. T. Chen, Y. Ting, C. Lai, Jason Yeh, Arnold Yang, Wayne Huang, Peng Chen, Charly Tsai, Ryan Yang, Y. S. Huang, B. Hsu, M. Z. Lee, T. Lee, Michael Huang, Coming Chen, L. Chu, H. Kao, N. S. Tsai","doi":"10.1109/IRPS48203.2023.10118207","DOIUrl":null,"url":null,"abstract":"Near-zero defective parts per million (DPPM) and returned material authorization (RMA) from customers is the goal pursued by many companies. In this paper, a machine learning based method is proposed to detect outliers in the final test stage using the XGBoost algorithm and the Mahalanobis distance. We captured the weak integrated circuits (ICs) that passed the final test but failed in the system level test (SLT) or the verification of quality engineering aging (QEA). Compared to the random sampling, the experiments showed we could recognize 2x~3x weak IC ratio in the SLT and >10x in the QEA to achieve automotive grade DPPM.","PeriodicalId":159030,"journal":{"name":"2023 IEEE International Reliability Physics Symposium (IRPS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Reliability Physics Symposium (IRPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRPS48203.2023.10118207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Near-zero defective parts per million (DPPM) and returned material authorization (RMA) from customers is the goal pursued by many companies. In this paper, a machine learning based method is proposed to detect outliers in the final test stage using the XGBoost algorithm and the Mahalanobis distance. We captured the weak integrated circuits (ICs) that passed the final test but failed in the system level test (SLT) or the verification of quality engineering aging (QEA). Compared to the random sampling, the experiments showed we could recognize 2x~3x weak IC ratio in the SLT and >10x in the QEA to achieve automotive grade DPPM.
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