{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:08:27Z","timestamp":1775326107032,"version":"3.50.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006579","name":"Ministry of Industry and Information Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["2024ZY01010"],"award-info":[{"award-number":["2024ZY01010"]}],"id":[{"id":"10.13039\/501100006579","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002766","name":"Beijing University of Posts and Telecommunications","doi-asserted-by":"publisher","award":["2023SYLTD04"],"award-info":[{"award-number":["2023SYLTD04"]}],"id":[{"id":"10.13039\/501100002766","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["510224074"],"award-info":[{"award-number":["510224074"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.eswa.2025.127121","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T18:24:24Z","timestamp":1742408664000},"page":"127121","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["MLR-WM-ViT: Global high-performance classification of mixed-type wafer map defect using a multi-level relay Vision Transformer"],"prefix":"10.1016","volume":"277","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9600-5345","authenticated-orcid":false,"given":"Xiangyan","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2626-5455","authenticated-orcid":false,"given":"Xuexiu","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6980-4555","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2335-6793","authenticated-orcid":false,"given":"Jian","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6081-7229","authenticated-orcid":false,"given":"Shimin","family":"Wei","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2025.127121_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107154","article-title":"Supervised contrastive learning for wafer map pattern classification","volume":"126","author":"Bae","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2025.127121_b2","article-title":"Wafer map defect recognition based on multi-scale feature fusion and attention spatial pyramid pooling","author":"Chen","year":"2023","journal-title":"Journal of Intelligent Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122795","article-title":"Mixed-type wafer defect detection based on multi-branch feature enhanced residual module","volume":"242","author":"Chen","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"230","key":"10.1016\/j.eswa.2025.127121_b4","article-title":"Wafer map defect pattern detection method based on improved attention mechanism","author":"Chen","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.127121_b5","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2025.127121_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118254","article-title":"Wafer map failure pattern recognition based on deep convolutional neural network","volume":"209","author":"Chen","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.127121_b7","series-title":"IEEE international test conference","first-page":"549","article-title":"Wafer defect pattern classification with explainable decision tree technique","author":"Cheng","year":"2022"},{"key":"10.1016\/j.eswa.2025.127121_b8","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s10845-013-0791-5","article-title":"An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing","volume":"25","author":"Chien","year":"2014","journal-title":"Journal of Intelligent Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b9","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.eswa.2025.127121_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpe.2024.109275","article-title":"A new vit-based augmentation framework for wafer map defect classification to enhance the resilience of semiconductor supply chains","volume":"273","author":"Fan","year":"2024","journal-title":"International Journal of Production Economics"},{"key":"10.1016\/j.eswa.2025.127121_b11","series-title":"Levit: a vision transformer in convnet\u2019s clothing for faster inference","author":"Graham","year":"2021"},{"key":"10.1016\/j.eswa.2025.127121_b12","series-title":"Ghostnet: More features from cheap operations","author":"Han","year":"2020"},{"key":"10.1016\/j.eswa.2025.127121_b13","series-title":"Transformer in transformer","author":"Han","year":"2021"},{"key":"10.1016\/j.eswa.2025.127121_b14","series-title":"Deep residual learning for image recognition","author":"He","year":"2015"},{"key":"10.1016\/j.eswa.2025.127121_b15","series-title":"Gaussian error linear units (gelus)","author":"Hendrycks","year":"2023"},{"key":"10.1016\/j.eswa.2025.127121_b16","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TSM.2024.3418520","article-title":"Recognition and classification of mixed defect pattern wafer map based on multi-path dcnn","volume":"37","author":"Hou","year":"2024","journal-title":"Ieee Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b17","doi-asserted-by":"crossref","DOI":"10.1155\/2015\/707358","article-title":"Clustering ensemble for identifying defective wafer bin map in semiconductor manufacturing","volume":"2015","author":"Hsu","year":"2015","journal-title":"Mathematical Problems in Engineering"},{"key":"10.1016\/j.eswa.2025.127121_b18","series-title":"Densely connected convolutional networks","author":"Huang","year":"2018"},{"key":"10.1016\/j.eswa.2025.127121_b19","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/TSM.2008.2005375","article-title":"Automatic identification of defect patterns in semiconductor wafer maps using spatial correlogram and dynamic time warping","volume":"21","author":"Jeong","year":"2008","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b20","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Laurens","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2025.127121_b21","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TSM.2022.3145855","article-title":"Testdna-e: Wafer defect signature for pattern recognition by ensemble learning","volume":"35","author":"Li","year":"2022","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b22","series-title":"25th IEEE European test symposium","article-title":"Pws: Potential wafermap scratch defect pattern recognition with machine learning techniques","author":"Li","year":"2020"},{"key":"10.1016\/j.eswa.2025.127121_b23","series-title":"Efficientvit: Memory efficient vision transformer with cascaded group attention","author":"Liu","year":"2023"},{"key":"10.1016\/j.eswa.2025.127121_b24","series-title":"Sgdr: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.eswa.2025.127121_b25","series-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2019"},{"key":"10.1016\/j.eswa.2025.127121_b26","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2023.3261924","article-title":"Composite wafer defect recognition framework based on multiview dynamic feature enhancement with class-specific classifier","volume":"72","author":"Luo","year":"2023","journal-title":"Ieee Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2025.127121_b27","series-title":"Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications","author":"Maaz","year":"2022"},{"key":"10.1016\/j.eswa.2025.127121_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122301","article-title":"Semi-supervised imbalanced classification of wafer bin map defects using a dual-head cnn","volume":"238","author":"Manivannan","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.127121_b29","series-title":"Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer","author":"Mehta","year":"2022"},{"key":"10.1016\/j.eswa.2025.127121_b30","series-title":"Separable self-attention for mobile vision transformers","author":"Mehta","year":"2022"},{"key":"10.1016\/j.eswa.2025.127121_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2022.103720","article-title":"Wafersegclassnet - a light-weight network for classification and segmentation of semiconductor wafer defects","volume":"142","author":"Nag","year":"2022","journal-title":"Computers in Industry"},{"key":"10.1016\/j.eswa.2025.127121_b32","series-title":"Swiftformer: Efficient additive attention for transformer-based real-time mobile vision applications","author":"Shaker","year":"2023"},{"key":"10.1016\/j.eswa.2025.127121_b33","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.mee.2004.12.003","article-title":"Defect detection on semiconductor wafer surfaces","volume":"77","author":"Shankar","year":"2005","journal-title":"Microelectronic Engineering"},{"key":"10.1016\/j.eswa.2025.127121_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125378","article-title":"A framework for detecting unknown defect patterns on wafer bin maps using active learning","volume":"260","author":"Shin","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.127121_b35","series-title":"Going deeper with convolutions","author":"Szegedy","year":"2014"},{"key":"10.1016\/j.eswa.2025.127121_b36","series-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2020"},{"key":"10.1016\/j.eswa.2025.127121_b37","series-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"10.1016\/j.eswa.2025.127121_b38","series-title":"2023 IEEE\/CVF conference on computer vision and pattern recognition","first-page":"7907","article-title":"Mobileone: An improved one millisecond mobile backbone","author":"Vasu","year":"2023"},{"key":"10.1016\/j.eswa.2025.127121_b39","series-title":"Attention is all you need","author":"Vaswani","year":"2023"},{"key":"10.1016\/j.eswa.2025.127121_b40","series-title":"Repvit: Revisiting mobile cnn from vit perspective","author":"Wang","year":"2024"},{"key":"10.1016\/j.eswa.2025.127121_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2022.102513","article-title":"Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing","volume":"81","author":"Wang","year":"2023","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b42","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1080\/07408170600733236","article-title":"Detection and classification of defect patterns on semiconductor wafers","volume":"38","author":"Wang","year":"2006","journal-title":"Iie Transactions"},{"key":"10.1016\/j.eswa.2025.127121_b43","article-title":"A momentum contrastive learning framework for low-data wafer defect classification in semiconductor manufacturing","volume":"13","author":"Wang","year":"2023","journal-title":"Applied Sciences-Basel"},{"key":"10.1016\/j.eswa.2025.127121_b44","series-title":"Pyramid vision transformer: A versatile backbone for dense prediction without convolutions","author":"Wang","year":"2021"},{"key":"10.1016\/j.eswa.2025.127121_b45","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/TSM.2020.3020985","article-title":"Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition","volume":"33","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b46","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TSM.2022.3156583","article-title":"Mixed-type wafer defect recognition with multi-scale information fusion transformer","volume":"35","author":"Wei","year":"2022","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105975","article-title":"Wavelet integrated attention network with multi-resolution frequency learning for mixed-type wafer defect recognition","volume":"121","author":"Wei","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2025.127121_b48","series-title":"IEEE international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics","first-page":"192","article-title":"Wafer map defect patterns semi-supervised classification using latent vector representation","author":"Wei","year":"2023"},{"key":"10.1016\/j.eswa.2025.127121_b49","series-title":"Cbam: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.eswa.2025.127121_b50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSM.2014.2364237","article-title":"Wafer map failure pattern recognition and similarity ranking for large-scale data sets","volume":"28","author":"Wu","year":"2015","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b51","doi-asserted-by":"crossref","first-page":"79056","DOI":"10.1109\/ACCESS.2020.2990535","article-title":"A method for wafer defect detection using spatial feature points guided affine iterative closest point algorithm","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2025.127121_b52","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1109\/TSM.2010.2046108","article-title":"A wavelet-based approach in detecting visual defects on semiconductor wafer dies","volume":"23","author":"Yeh","year":"2010","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b53","article-title":"Wafer map defect patterns classification based on a lightweight network and data augmentation","author":"Yu","year":"2022","journal-title":"Caai Transactions on Intelligence Technology"},{"key":"10.1016\/j.eswa.2025.127121_b54","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1109\/TII.2021.3092372","article-title":"Multiple granularities generative adversarial network for recognition of wafer map defects","volume":"18","author":"Yu","year":"2022","journal-title":"Ieee Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2025.127121_b55","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TSM.2011.2154870","article-title":"Detection of spatial defect patterns generated in semiconductor fabrication processes","volume":"24","author":"Yuan","year":"2011","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"10.1016\/j.eswa.2025.127121_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104136","article-title":"Dmwmnet: A novel dual-branch multi-level convolutional network for high-performance mixed-type wafer map defect detection in semiconductor manufacturing","volume":"161","author":"Zhang","year":"2024","journal-title":"Computers in Industry"},{"key":"10.1016\/j.eswa.2025.127121_b57","series-title":"Cas-vit: Convolutional additive self-attention vision transformers for efficient mobile applications","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2025.127121_b58","series-title":"China semiconductor technology international conference","first-page":"1","article-title":"Metavit-trans: A framework for mixed-type defect detection of wafers with vision transformer combined with meta-learning and transfer learning","author":"Zhao","year":"2023"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425007432?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425007432?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T10:52:24Z","timestamp":1762339944000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417425007432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":58,"alternative-id":["S0957417425007432"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2025.127121","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MLR-WM-ViT: Global high-performance classification of mixed-type wafer map defect using a multi-level relay Vision Transformer","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2025.127121","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"127121"}}