Multi-source adaptive thresholding adaboost with application to virtual metrology
Gespeichert in:
| Autor*in: |
Xie, Yifan [verfasserIn] Wang, Tianhui [verfasserIn] Jeong, Myong Kee [verfasserIn] Lee, Gyeong Taek [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2024 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of production research - London [u.a.] : Taylor & Francis, 1996, 62(2024), 17, Seite 6344-6359 |
|---|---|
| Übergeordnetes Werk: |
volume:62 ; year:2024 ; number:17 ; pages:6344-6359 |
| Links: |
|---|
| DOI / URN: |
10.1080/00207543.2024.2314151 |
|---|
| Katalog-ID: |
1901936430 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | 1901936430 | ||
| 003 | DE-627 | ||
| 005 | 20240909112104.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240905s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1080/00207543.2024.2314151 |2 doi | |
| 035 | |a (DE-627)1901936430 | ||
| 035 | |a (DE-599)KXP1901936430 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Xie, Yifan |e verfasserin |0 (DE-588)133979621X |0 (DE-627)189934926X |4 aut | |
| 245 | 1 | 0 | |a Multi-source adaptive thresholding adaboost with application to virtual metrology |c Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 650 | 4 | |a Boosting |7 (dpeaa)DE-206 | |
| 650 | 4 | |a ensemble learning |7 (dpeaa)DE-206 | |
| 650 | 4 | |a multi-source data fusion |7 (dpeaa)DE-206 | |
| 650 | 4 | |a robust |7 (dpeaa)DE-206 | |
| 650 | 4 | |a semiconductor manufacturing process |7 (dpeaa)DE-206 | |
| 650 | 4 | |a virtual metrology |7 (dpeaa)DE-206 | |
| 700 | 1 | |a Wang, Tianhui |e verfasserin |0 (DE-588)1339796430 |0 (DE-627)1899349715 |4 aut | |
| 700 | 1 | |a Jeong, Myong Kee |e verfasserin |0 (DE-588)1268442011 |0 (DE-627)1816970867 |4 aut | |
| 700 | 1 | |a Lee, Gyeong Taek |e verfasserin |0 (DE-588)1341398382 |0 (DE-627)1902151348 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of production research |d London [u.a.] : Taylor & Francis, 1996 |g 62(2024), 17, Seite 6344-6359 |h Online-Ressource |w (DE-627)301516731 |w (DE-600)1485085-0 |w (DE-576)094115516 |x 1366-588X |7 nnns |
| 773 | 1 | 8 | |g volume:62 |g year:2024 |g number:17 |g pages:6344-6359 |
| 856 | 4 | 0 | |u https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 |x Verlag |z lizenzpflichtig |
| 856 | 4 | 0 | |u https://doi.org/10.1080/00207543.2024.2314151 |x Resolving-System |z lizenzpflichtig |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ILN_26 | ||
| 912 | |a ISIL_DE-206 | ||
| 912 | |a SYSFLAG_1 | ||
| 912 | |a GBV_KXP | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_647 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2093 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2119 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2232 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2472 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2548 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4246 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 62 |j 2024 |e 17 |h 6344-6359 | ||
| 980 | |2 26 |1 01 |x 0206 |b 4576540033 |y x1z |z 05-09-24 | ||
| 982 | |2 26 |1 00 |x DE-206 |b To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. | ||
| author_variant |
y x yx t w tw m k j mk mkj g t l gt gtl |
|---|---|
| matchkey_str |
article:1366588X:2024----::utsucaatvtrsodnaaoswtapiain |
| hierarchy_sort_str |
2024 |
| publishDate |
2024 |
| allfields |
10.1080/00207543.2024.2314151 doi (DE-627)1901936430 (DE-599)KXP1901936430 DE-627 ger DE-627 rda eng Xie, Yifan verfasserin (DE-588)133979621X (DE-627)189934926X aut Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 ensemble learning (dpeaa)DE-206 multi-source data fusion (dpeaa)DE-206 robust (dpeaa)DE-206 semiconductor manufacturing process (dpeaa)DE-206 virtual metrology (dpeaa)DE-206 Wang, Tianhui verfasserin (DE-588)1339796430 (DE-627)1899349715 aut Jeong, Myong Kee verfasserin (DE-588)1268442011 (DE-627)1816970867 aut Lee, Gyeong Taek verfasserin (DE-588)1341398382 (DE-627)1902151348 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 17, Seite 6344-6359 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:17 pages:6344-6359 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2024.2314151 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 17 6344-6359 26 01 0206 4576540033 x1z 05-09-24 26 00 DE-206 To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. |
| spelling |
10.1080/00207543.2024.2314151 doi (DE-627)1901936430 (DE-599)KXP1901936430 DE-627 ger DE-627 rda eng Xie, Yifan verfasserin (DE-588)133979621X (DE-627)189934926X aut Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 ensemble learning (dpeaa)DE-206 multi-source data fusion (dpeaa)DE-206 robust (dpeaa)DE-206 semiconductor manufacturing process (dpeaa)DE-206 virtual metrology (dpeaa)DE-206 Wang, Tianhui verfasserin (DE-588)1339796430 (DE-627)1899349715 aut Jeong, Myong Kee verfasserin (DE-588)1268442011 (DE-627)1816970867 aut Lee, Gyeong Taek verfasserin (DE-588)1341398382 (DE-627)1902151348 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 17, Seite 6344-6359 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:17 pages:6344-6359 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2024.2314151 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 17 6344-6359 26 01 0206 4576540033 x1z 05-09-24 26 00 DE-206 To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. |
| allfields_unstemmed |
10.1080/00207543.2024.2314151 doi (DE-627)1901936430 (DE-599)KXP1901936430 DE-627 ger DE-627 rda eng Xie, Yifan verfasserin (DE-588)133979621X (DE-627)189934926X aut Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 ensemble learning (dpeaa)DE-206 multi-source data fusion (dpeaa)DE-206 robust (dpeaa)DE-206 semiconductor manufacturing process (dpeaa)DE-206 virtual metrology (dpeaa)DE-206 Wang, Tianhui verfasserin (DE-588)1339796430 (DE-627)1899349715 aut Jeong, Myong Kee verfasserin (DE-588)1268442011 (DE-627)1816970867 aut Lee, Gyeong Taek verfasserin (DE-588)1341398382 (DE-627)1902151348 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 17, Seite 6344-6359 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:17 pages:6344-6359 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2024.2314151 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 17 6344-6359 26 01 0206 4576540033 x1z 05-09-24 26 00 DE-206 To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. |
| allfieldsGer |
10.1080/00207543.2024.2314151 doi (DE-627)1901936430 (DE-599)KXP1901936430 DE-627 ger DE-627 rda eng Xie, Yifan verfasserin (DE-588)133979621X (DE-627)189934926X aut Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 ensemble learning (dpeaa)DE-206 multi-source data fusion (dpeaa)DE-206 robust (dpeaa)DE-206 semiconductor manufacturing process (dpeaa)DE-206 virtual metrology (dpeaa)DE-206 Wang, Tianhui verfasserin (DE-588)1339796430 (DE-627)1899349715 aut Jeong, Myong Kee verfasserin (DE-588)1268442011 (DE-627)1816970867 aut Lee, Gyeong Taek verfasserin (DE-588)1341398382 (DE-627)1902151348 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 17, Seite 6344-6359 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:17 pages:6344-6359 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2024.2314151 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 17 6344-6359 26 01 0206 4576540033 x1z 05-09-24 26 00 DE-206 To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. |
| allfieldsSound |
10.1080/00207543.2024.2314151 doi (DE-627)1901936430 (DE-599)KXP1901936430 DE-627 ger DE-627 rda eng Xie, Yifan verfasserin (DE-588)133979621X (DE-627)189934926X aut Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 ensemble learning (dpeaa)DE-206 multi-source data fusion (dpeaa)DE-206 robust (dpeaa)DE-206 semiconductor manufacturing process (dpeaa)DE-206 virtual metrology (dpeaa)DE-206 Wang, Tianhui verfasserin (DE-588)1339796430 (DE-627)1899349715 aut Jeong, Myong Kee verfasserin (DE-588)1268442011 (DE-627)1816970867 aut Lee, Gyeong Taek verfasserin (DE-588)1341398382 (DE-627)1902151348 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 17, Seite 6344-6359 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:17 pages:6344-6359 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2024.2314151 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 17 6344-6359 26 01 0206 4576540033 x1z 05-09-24 26 00 DE-206 To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. |
| language |
English |
| source |
Enthalten in International journal of production research 62(2024), 17, Seite 6344-6359 volume:62 year:2024 number:17 pages:6344-6359 |
| sourceStr |
Enthalten in International journal of production research 62(2024), 17, Seite 6344-6359 volume:62 year:2024 number:17 pages:6344-6359 |
| format_phy_str_mv |
Article |
| building |
26:1 |
| institution |
findex.gbv.de |
| selectbib_iln_str_mv |
26@1z |
| topic_facet |
Boosting ensemble learning multi-source data fusion robust semiconductor manufacturing process virtual metrology |
| sw_local_iln_str_mv |
26: DE-206: |
| isfreeaccess_bool |
false |
| container_title |
International journal of production research |
| authorswithroles_txt_mv |
Xie, Yifan @@aut@@ Wang, Tianhui @@aut@@ Jeong, Myong Kee @@aut@@ Lee, Gyeong Taek @@aut@@ |
| publishDateDaySort_date |
2024-01-01T00:00:00Z |
| hierarchy_top_id |
301516731 |
| id |
1901936430 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1901936430</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240909112104.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240905s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00207543.2024.2314151</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1901936430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1901936430</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xie, Yifan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)133979621X</subfield><subfield code="0">(DE-627)189934926X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-source adaptive thresholding adaboost with application to virtual metrology</subfield><subfield code="c">Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ensemble learning</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-source data fusion</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">robust</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semiconductor manufacturing process</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">virtual metrology</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Tianhui</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1339796430</subfield><subfield code="0">(DE-627)1899349715</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jeong, Myong Kee</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1268442011</subfield><subfield code="0">(DE-627)1816970867</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Gyeong Taek</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1341398382</subfield><subfield code="0">(DE-627)1902151348</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of production research</subfield><subfield code="d">London [u.a.] : Taylor & Francis, 1996</subfield><subfield code="g">62(2024), 17, Seite 6344-6359</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)301516731</subfield><subfield code="w">(DE-600)1485085-0</subfield><subfield code="w">(DE-576)094115516</subfield><subfield code="x">1366-588X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:17</subfield><subfield code="g">pages:6344-6359</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151</subfield><subfield code="x">Verlag</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/00207543.2024.2314151</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_647</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">62</subfield><subfield code="j">2024</subfield><subfield code="e">17</subfield><subfield code="h">6344-6359</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4576540033</subfield><subfield code="y">x1z</subfield><subfield code="z">05-09-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks.</subfield></datafield></record></collection>
|
| author |
Xie, Yifan |
| spellingShingle |
Xie, Yifan misc Boosting misc ensemble learning misc multi-source data fusion misc robust misc semiconductor manufacturing process misc virtual metrology Multi-source adaptive thresholding adaboost with application to virtual metrology |
| authorStr |
Xie, Yifan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)301516731 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
KXP GVK SWB |
| remote_str |
true |
| last_changed_iln_str_mv |
26@05-09-24 |
| illustrated |
Not Illustrated |
| issn |
1366-588X |
| topic_title |
26 00 DE-206 To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee Boosting (dpeaa)DE-206 ensemble learning (dpeaa)DE-206 multi-source data fusion (dpeaa)DE-206 robust (dpeaa)DE-206 semiconductor manufacturing process (dpeaa)DE-206 virtual metrology (dpeaa)DE-206 |
| topic |
misc Boosting misc ensemble learning misc multi-source data fusion misc robust misc semiconductor manufacturing process misc virtual metrology |
| topic_unstemmed |
misc Boosting misc ensemble learning misc multi-source data fusion misc robust misc semiconductor manufacturing process misc virtual metrology |
| topic_browse |
misc Boosting misc ensemble learning misc multi-source data fusion misc robust misc semiconductor manufacturing process misc virtual metrology |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
International journal of production research |
| hierarchy_parent_id |
301516731 |
| hierarchy_top_title |
International journal of production research |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 |
| normlinkwithrole_str_mv |
(DE-588)133979621X@@aut@@ (DE-588)1339796430@@aut@@ (DE-588)1268442011@@aut@@ (DE-588)1341398382@@aut@@ |
| title |
Multi-source adaptive thresholding adaboost with application to virtual metrology |
| ctrlnum |
(DE-627)1901936430 (DE-599)KXP1901936430 |
| title_full |
Multi-source adaptive thresholding adaboost with application to virtual metrology Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee |
| author_sort |
Xie, Yifan |
| journal |
International journal of production research |
| journalStr |
International journal of production research |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2024 |
| contenttype_str_mv |
txt |
| container_start_page |
6344 |
| author_browse |
Xie, Yifan Wang, Tianhui Jeong, Myong Kee Lee, Gyeong Taek |
| selectkey |
26:x |
| container_volume |
62 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Xie, Yifan |
| doi_str_mv |
10.1080/00207543.2024.2314151 |
| normlink |
133979621X 189934926X 1339796430 1899349715 1268442011 1816970867 1341398382 1902151348 |
| normlink_prefix_str_mv |
(DE-588)133979621X (DE-627)189934926X (DE-588)1339796430 (DE-627)1899349715 (DE-588)1268442011 (DE-627)1816970867 (DE-588)1341398382 (DE-627)1902151348 |
| author2-role |
verfasserin |
| title_sort |
multi-source adaptive thresholding adaboost with application to virtual metrology |
| title_auth |
Multi-source adaptive thresholding adaboost with application to virtual metrology |
| collection_details |
GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 |
| container_issue |
17 |
| title_short |
Multi-source adaptive thresholding adaboost with application to virtual metrology |
| url |
https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151 https://doi.org/10.1080/00207543.2024.2314151 |
| ausleihindikator_str_mv |
26 |
| rolewithnormlink_str_mv |
@@aut@@(DE-588)133979621X @@aut@@(DE-588)1339796430 @@aut@@(DE-588)1268442011 @@aut@@(DE-588)1341398382 |
| remote_bool |
true |
| author2 |
Wang, Tianhui Jeong, Myong Kee Lee, Gyeong Taek |
| author2Str |
Wang, Tianhui Jeong, Myong Kee Lee, Gyeong Taek |
| ppnlink |
301516731 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1080/00207543.2024.2314151 |
| up_date |
2024-09-10T04:29:53.223Z |
| _version_ |
1809781980623011840 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1901936430</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240909112104.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240905s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00207543.2024.2314151</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1901936430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1901936430</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xie, Yifan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)133979621X</subfield><subfield code="0">(DE-627)189934926X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-source adaptive thresholding adaboost with application to virtual metrology</subfield><subfield code="c">Yifan Xie, Tianhui Wang, Myong K. Jeong and Gyeong Taek Lee</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ensemble learning</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-source data fusion</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">robust</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semiconductor manufacturing process</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">virtual metrology</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Tianhui</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1339796430</subfield><subfield code="0">(DE-627)1899349715</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jeong, Myong Kee</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1268442011</subfield><subfield code="0">(DE-627)1816970867</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Gyeong Taek</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1341398382</subfield><subfield code="0">(DE-627)1902151348</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of production research</subfield><subfield code="d">London [u.a.] : Taylor & Francis, 1996</subfield><subfield code="g">62(2024), 17, Seite 6344-6359</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)301516731</subfield><subfield code="w">(DE-600)1485085-0</subfield><subfield code="w">(DE-576)094115516</subfield><subfield code="x">1366-588X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:17</subfield><subfield code="g">pages:6344-6359</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2314151</subfield><subfield code="x">Verlag</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/00207543.2024.2314151</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_647</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">62</subfield><subfield code="j">2024</subfield><subfield code="e">17</subfield><subfield code="h">6344-6359</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4576540033</subfield><subfield code="y">x1z</subfield><subfield code="z">05-09-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks.</subfield></datafield></record></collection>
|
| score |
7.400403 |