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TestDNA-E: Wafer Defect Signature for Pattern Recognition by Ensemble Learning
Wafer failure pattern recognition can be used for root cause analysis, which is very important for yield learning. Recently, TestDNA was proposed to improve diagnosis resolution with data collected from wafer test. Previous studies on wafer failure pattern recognition using machine learning achieve...
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| Published in: | IEEE transactions on semiconductor manufacturing 2022-05, Vol.35 (2), p.372-374 |
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| Main Authors: | , , , , , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c206t-8ad0938147a0e9f46da0cdfbc5b792a682a4a5e167776b8c64435cdd4e53e8d33 |
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| cites | cdi_FETCH-LOGICAL-c206t-8ad0938147a0e9f46da0cdfbc5b792a682a4a5e167776b8c64435cdd4e53e8d33 |
| container_end_page | 374 |
| container_issue | 2 |
| container_start_page | 372 |
| container_title | IEEE transactions on semiconductor manufacturing |
| container_volume | 35 |
| creator | Li, Katherine Shu-Min Chen, Leon Li-Yang Cheng, Ken Chau-Cheung Liao, Peter Yi-Yu Wang, Sying-Jyan Huang, Andrew Yi-Ann Chou, Leon Tsai, Nova Cheng-Yen Lee, Chen-Shiun |
| description | Wafer failure pattern recognition can be used for root cause analysis, which is very important for yield learning. Recently, TestDNA was proposed to improve diagnosis resolution with data collected from wafer test. Previous studies on wafer failure pattern recognition using machine learning achieve good classification results. In this letter, we propose to enhance the classification accuracy with the help of spatial information and ensemble learning algorithms. Experimental results indicate that the proposed method can further improve the accuracy by 8.9%. |
| doi_str_mv | 10.1109/TSM.2022.3145855 |
| format | article |
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Recently, TestDNA was proposed to improve diagnosis resolution with data collected from wafer test. Previous studies on wafer failure pattern recognition using machine learning achieve good classification results. In this letter, we propose to enhance the classification accuracy with the help of spatial information and ensemble learning algorithms. Experimental results indicate that the proposed method can further improve the accuracy by 8.9%.</description><identifier>ISSN: 0894-6507</identifier><identifier>EISSN: 1558-2345</identifier><identifier>DOI: 10.1109/TSM.2022.3145855</identifier><identifier>CODEN: ITSMED</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Classification ; Decision trees ; Ensemble learning ; Failure analysis ; Feature extraction ; Machine learning ; Machine learning algorithms ; Pattern recognition ; Prediction algorithms ; Root cause analysis ; Spatial data ; Sports ; test probe ; Transforms ; wafer defect diagnosis ; wafer defect map ; wafer defect pattern ; Wafer test ; yield learning</subject><ispartof>IEEE transactions on semiconductor manufacturing, 2022-05, Vol.35 (2), p.372-374</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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| fulltext | fulltext |
| identifier | ISSN: 0894-6507 |
| ispartof | IEEE transactions on semiconductor manufacturing, 2022-05, Vol.35 (2), p.372-374 |
| issn | 0894-6507 1558-2345 |
| language | eng |
| recordid | cdi_ieee_primary_9690948 |
| source | IEEE Xplore (Online service) |
| subjects | Algorithms Classification Decision trees Ensemble learning Failure analysis Feature extraction Machine learning Machine learning algorithms Pattern recognition Prediction algorithms Root cause analysis Spatial data Sports test probe Transforms wafer defect diagnosis wafer defect map wafer defect pattern Wafer test yield learning |
| title | TestDNA-E: Wafer Defect Signature for Pattern Recognition by Ensemble Learning |
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