{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:36:33Z","timestamp":1781886993339,"version":"3.54.5"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07113-0","type":"journal-article","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T11:45:42Z","timestamp":1742730342000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced wafer map defect pattern classification through stacking ensemble method and data augmentation integration"],"prefix":"10.1007","volume":"81","author":[{"given":"Chiung-Jung","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Han","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun-Yuan","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"issue":"5\u20136","key":"7113_CR1","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1016\/j.engappai.2012.11.009","volume":"26","author":"CW Liu","year":"2013","unstructured":"Liu CW, Chien CF (2013) An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing. Eng Appl Artif Intel 26(5\u20136):1479\u20131486. https:\/\/doi.org\/10.1016\/j.engappai.2012.11.009","journal-title":"Eng Appl Artif Intel"},{"issue":"3","key":"7113_CR2","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10696-012-9161-4","volume":"25","author":"CF Chien","year":"2013","unstructured":"Chien CF, Hsu CY, Chen PN (2013) Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flex Serv Manuf J 25(3):367\u2013388. https:\/\/doi.org\/10.1007\/s10696-012-9161-4","journal-title":"Flex Serv Manuf J"},{"issue":"1","key":"7113_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tsm.2014.2364237","volume":"28","author":"MJ Wu","year":"2015","unstructured":"Wu MJ, Jang JSR, Chen JL (2015) Wafer map failure pattern recognition and similarity ranking for large-scale data sets. IEEE Trans Semicond Manuf 28(1):1\u201312. https:\/\/doi.org\/10.1109\/tsm.2014.2364237","journal-title":"IEEE Trans Semicond Manuf"},{"issue":"2","key":"7113_CR4","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/66.382273","volume":"8","author":"SP Cunningham","year":"1995","unstructured":"Cunningham SP, Spanos CJ, Voros K (1995) Semiconductor yield improvement: results and best practices. IEEE Trans Semicond Manuf 8(2):103\u2013109. https:\/\/doi.org\/10.1109\/66.382273","journal-title":"IEEE Trans Semicond Manuf"},{"issue":"1","key":"7113_CR5","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.eswa.2006.04.014","volume":"33","author":"CF Chien","year":"2007","unstructured":"Chien CF, Wang WC, Cheng JC (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33(1):192\u2013198. https:\/\/doi.org\/10.1016\/j.eswa.2006.04.014","journal-title":"Expert Syst Appl"},{"issue":"3\u20134","key":"7113_CR6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.mee.2004.12.003","volume":"77","author":"NG Shankar","year":"2005","unstructured":"Shankar NG, Zhong ZW (2005) Defect detection on semiconductor wafer surfaces. Microelectron Eng 77(3\u20134):337\u2013346. https:\/\/doi.org\/10.1016\/j.mee.2004.12.003","journal-title":"Microelectron Eng"},{"key":"7113_CR7","doi-asserted-by":"publisher","first-page":"116572","DOI":"10.1109\/access.2021.3106171","volume":"9","author":"U Batool","year":"2021","unstructured":"Batool U, Shapiai MI, Tahir M, Ismail ZH, Zakaria NJ, Elfakharany A (2021) A systematic review of deep learning for silicon wafer defect recognition. IEEE Access 9:116572\u2013116593. https:\/\/doi.org\/10.1109\/access.2021.3106171","journal-title":"IEEE Access"},{"key":"7113_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2022.3178498","volume":"71","author":"Y Wei","year":"2022","unstructured":"Wei Y, Wang H (2022) Mixed-type wafer defect pattern recognition framework based on multifaceted dynamic convolution. IEEE Trans Instrum Meas 71:1\u201311. https:\/\/doi.org\/10.1109\/tim.2022.3178498","journal-title":"IEEE Trans Instrum Meas"},{"issue":"7","key":"7113_CR9","doi-asserted-by":"publisher","first-page":"836","DOI":"10.3390\/mi15070836","volume":"15","author":"G Deng","year":"2024","unstructured":"Deng G, Wang H (2024) Efficient mixed-type wafer defect pattern recognition based on light-weight neural network. Micromachines 15(7):836. https:\/\/doi.org\/10.3390\/mi15070836","journal-title":"Micromachines"},{"key":"7113_CR10","doi-asserted-by":"publisher","first-page":"170650","DOI":"10.1109\/access.2020.3024603","volume":"8","author":"S Kang","year":"2020","unstructured":"Kang S (2020) Rotation-invariant wafer map pattern classification with convolutional neural networks. IEEE Access 8:170650\u2013170658. https:\/\/doi.org\/10.1109\/access.2020.3024603","journal-title":"IEEE Access"},{"key":"7113_CR11","doi-asserted-by":"publisher","unstructured":"Tziolas T et al (2022) Wafer map defect pattern recognition using imbalanced datasets. In: 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Corfu, Greece, pp 1\u20138, https:\/\/doi.org\/10.1109\/IISA56318.2022.9904402","DOI":"10.1109\/IISA56318.2022.9904402"},{"issue":"3","key":"7113_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/tsm.2020.2994357","volume":"33","author":"M Saqlain","year":"2020","unstructured":"Saqlain M, Abbas Q, Lee JY (2020) A deep convolutional neural network for wafer defect identification on an imbalanced dataset in semiconductor manufacturing processes. IEEE Trans Semicond Manuf 33(3):436\u2013444. https:\/\/doi.org\/10.1109\/tsm.2020.2994357","journal-title":"IEEE Trans Semicond Manuf"},{"key":"7113_CR13","doi-asserted-by":"publisher","first-page":"103809","DOI":"10.1016\/j.compind.2022.103809","volume":"145","author":"R Alqudah","year":"2023","unstructured":"Alqudah R, Al-Mousa AA, Abu Hashyeh Y, Alzaibaq OZ (2023) A systemic comparison between using augmented data and synthetic data as means of enhancing wafermap defect classification. Computers in Industry 145:103809. https:\/\/doi.org\/10.1016\/j.compind.2022.103809","journal-title":"Computers in Industry"},{"issue":"3","key":"7113_CR14","doi-asserted-by":"publisher","first-page":"2351","DOI":"10.1007\/s10462-021-10066-4","volume":"55","author":"NE Khalifa","year":"2022","unstructured":"Khalifa NE, Loey M, Mirjalili S (2022) A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif Intell Rev 55(3):2351\u20132377. https:\/\/doi.org\/10.1007\/s10462-021-10066-4","journal-title":"Artif Intell Rev"},{"issue":"1","key":"7113_CR15","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J Big Data"},{"issue":"5786","key":"7113_CR16","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science"},{"key":"7113_CR17","doi-asserted-by":"publisher","unstructured":"Shawon A, Faruk MO, Habib MB and Khan AM (2019) Silicon wafer map defect classification using deep convolutional neural network with data augmentation. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China, 2019, pp 1995\u20131999, https:\/\/doi.org\/10.1109\/ICCC47050.2019.9064029","DOI":"10.1109\/ICCC47050.2019.9064029"},{"issue":"1","key":"7113_CR18","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1002\/qre.2983","volume":"38","author":"FK Wang","year":"2022","unstructured":"Wang FK, Chou JH, Amogne ZE (2022) A deep convolutional neural network with residual blocks for wafer map defect pattern recognition. Qual Reliab Eng Int 38(1):343\u2013357. https:\/\/doi.org\/10.1002\/qre.2983","journal-title":"Qual Reliab Eng Int"},{"issue":"3","key":"7113_CR19","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1049\/cit2.12126","volume":"8","author":"N Yu","year":"2022","unstructured":"Yu N, Chen H, Xu Q, Hasan MM, Sie O (2022) Wafer map defect patterns classification based on a lightweight network and data augmentation. CAAI Trans Intel Technol 8(3):1029\u20131042. https:\/\/doi.org\/10.1049\/cit2.12126","journal-title":"CAAI Trans Intel Technol"},{"issue":"4","key":"7113_CR20","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1109\/TSM.2020.3013004","volume":"33","author":"TH Tsai","year":"2020","unstructured":"Tsai TH, Lee YC (2020) A light-weight neural network for wafer map classification based on data augmentation. IEEE Trans Semicond Manuf 33(4):663\u2013672. https:\/\/doi.org\/10.1109\/TSM.2020.3013004","journal-title":"IEEE Trans Semicond Manuf"},{"key":"7113_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2024.3395316","volume":"73","author":"Y Zhang","year":"2024","unstructured":"Zhang Y et al (2024) Class imbalance wafer defect pattern recognition based on shared-database decentralized federated learning framework. IEEE Trans Instrum Meas 73:1\u201317. https:\/\/doi.org\/10.1109\/tim.2024.3395316","journal-title":"IEEE Trans Instrum Meas"},{"key":"7113_CR22","doi-asserted-by":"publisher","unstructured":"Mengying F, Qin W and van der Waal B (2016) Wafer defect patterns recognition based on OPTICS and multi-label classification. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi\u2019an, China, pp 912-915, https:\/\/doi.org\/10.1109\/IMCEC.2016.7867343","DOI":"10.1109\/IMCEC.2016.7867343"},{"issue":"2","key":"7113_CR23","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/tsm.2019.2904306","volume":"32","author":"M Saqlain","year":"2019","unstructured":"Saqlain M, Jargalsaikhan B, Lee JY (2019) A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. IEEE Trans Semicond Manuf 32(2):171\u2013182. https:\/\/doi.org\/10.1109\/tsm.2019.2904306","journal-title":"IEEE Trans Semicond Manuf"},{"key":"7113_CR24","doi-asserted-by":"publisher","first-page":"103450","DOI":"10.1016\/j.compind.2021.103450","volume":"129","author":"H Kang","year":"2021","unstructured":"Kang H, Kang S (2021) A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification. Comput Ind 129:103450. https:\/\/doi.org\/10.1016\/j.compind.2021.103450","journal-title":"Comput Ind"},{"key":"7113_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2024.3374295","volume":"73","author":"Z Yi","year":"2024","unstructured":"Yi Z et al (2024) Large-margin extreme learning machines with hybrid features for wafer map defect recognition. IEEE Trans Instrum Meas 73:1\u201310. https:\/\/doi.org\/10.1109\/tim.2024.3374295","journal-title":"IEEE Trans Instrum Meas"},{"issue":"7553","key":"7113_CR26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"7113_CR27","doi-asserted-by":"publisher","unstructured":"Ishida T, Nitta I, Fukuda D and Kanazawa Y (2019) Deep learning-based wafer-map failure pattern recognition framework. In: 20th International Symposium on Quality Electronic Design (ISQED), 6\u20137 March 2019, pp 291\u2013297, https:\/\/doi.org\/10.1109\/ISQED.2019.8697407","DOI":"10.1109\/ISQED.2019.8697407"},{"issue":"4","key":"7113_CR28","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.3390\/s23041926","volume":"23","author":"E Shin","year":"2023","unstructured":"Shin E, Yoo CD (2023) Efficient convolutional neural networks for semiconductor wafer bin map classification. Sensors 23(4):1926. https:\/\/doi.org\/10.3390\/s23041926","journal-title":"Sensors"},{"issue":"1","key":"7113_CR29","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s10845-023-02231-z","volume":"36","author":"S Chen","year":"2023","unstructured":"Chen S, Huang Z, Wang T, Hou X, Ma J (2023) Wafer map defect recognition based on multi-scale feature fusion and attention spatial pyramid pooling. J Intel Manuf 36(1):271\u2013284. https:\/\/doi.org\/10.1007\/s10845-023-02231-z","journal-title":"J Intel Manuf"},{"key":"7113_CR30","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble methods: foundations and algorithms","author":"ZH Zhou","year":"2012","unstructured":"Zhou ZH (2012) Ensemble methods: foundations and algorithms. Chapman & Hall\/CRC, Boca Raton"},{"key":"7113_CR31","doi-asserted-by":"publisher","DOI":"10.1201\/b17320","volume-title":"Data classification: algorithms and applications","author":"CC Aggarwal","year":"2014","unstructured":"Aggarwal CC (2014) Data classification: algorithms and applications. Chapman & Hall\/CRC, Boca Raton"},{"key":"7113_CR32","doi-asserted-by":"publisher","first-page":"179454","DOI":"10.1109\/ACCESS.2019.2958703","volume":"7","author":"X Fei","year":"2019","unstructured":"Fei X, Zhang Q, Ling Q (2019) Vehicle exhaust concentration estimation based on an improved stacking model. IEEE Access 7:179454\u2013179463. https:\/\/doi.org\/10.1109\/ACCESS.2019.2958703","journal-title":"IEEE Access"},{"issue":"9","key":"7113_CR33","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang ZH (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352\u20132449. https:\/\/doi.org\/10.1162\/neco_a_00990","journal-title":"Neural Comput"},{"key":"7113_CR34","unstructured":"Simonyan K and Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, CoRR, vol abs\/1409.1556"},{"key":"7113_CR35","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S and Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7113_CR36","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L and Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp 2261\u20132269, https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"issue":"3","key":"7113_CR37","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vision"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07113-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07113-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07113-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T11:45:45Z","timestamp":1742730345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07113-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":37,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["7113"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07113-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"22 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"643"}}