{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:51:30Z","timestamp":1781819490225,"version":"3.54.5"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000161","name":"National Institute of Standards and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000161","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100888","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T13:34:39Z","timestamp":1778765679000},"page":"100888","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A critical survey of machine learning - powered vision inspection in semiconductors manufacturing"],"prefix":"10.1016","volume":"30","author":[{"given":"Yamral Kassanew","family":"Akele","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2742-9061","authenticated-orcid":false,"given":"Vladimir","family":"Gurau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.array.2026.100888_bib4","doi-asserted-by":"crossref","unstructured":"S.-H. Huang and Y.-C. Pan, \u201cAutomated visual inspection in the semiconductor industry: a survey\u201d, Comput Ind, vol. 66, pp. 1-10. https:\/\/doi.org\/10.1016\/j.compind.2014.10.006.","DOI":"10.1016\/j.compind.2014.10.006"},{"key":"10.1016\/j.array.2026.100888_bib5","author":"Elicit"},{"key":"10.1016\/j.array.2026.100888_bib6","series-title":"Proc. IEEE Advanced Semiconductor Manufacturing Conf. (ASMC)","first-page":"250","article-title":"A deep learning model for identification of defect patterns in semiconductor wafer map","author":"Yang","year":"2019"},{"key":"10.1016\/j.array.2026.100888_bib7","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s10845-021-01906-9","article-title":"Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks","volume":"33","author":"Schlosser","year":"2022","journal-title":"J Intell Manuf"},{"issue":"15","key":"10.1016\/j.array.2026.100888_bib8","doi-asserted-by":"crossref","DOI":"10.3390\/app10155340","article-title":"Inspection and classification of semiconductor wafer surface defects using CNN deep learning networks","volume":"10","author":"Chien","year":"2020","journal-title":"Appl Sci"},{"key":"10.1016\/j.array.2026.100888_bib9","series-title":"Machine learning inspection system for anomaly detection of semiconductor devices","author":"Wong","year":"2023"},{"key":"10.1016\/j.array.2026.100888_bib10","doi-asserted-by":"crossref","DOI":"10.3390\/electronics14163260","article-title":"Region segmentation for efficient semiconductor inspection: a deep learning approach with transformers and atrous convolution","volume":"14","author":"Koara","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.array.2026.100888_bib11","series-title":"2020 int. Conf. Intelligent mechatronics and automation (IMPACT)","first-page":"1","article-title":"A novel feature-spanning machine learning technology for defect inspection","author":"Hsu","year":"2020"},{"key":"10.1016\/j.array.2026.100888_bib12","series-title":"Proc. 46th annu. Conf. IEEE industrial electronics Society (IECON), Singapore","first-page":"1","article-title":"Improving automated visual fault detection by combining a biologically plausible model of visual attention with deep learning","author":"Beuth","year":"2020"},{"issue":"1","key":"10.1016\/j.array.2026.100888_bib13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/JESTIE.2023.3326092","article-title":"Deep learning-based visual recognition for inline defects in production of semiconductors","volume":"5","author":"Limam","year":"2024","journal-title":"IEEE J Emerg Sel Topics Ind Electron"},{"key":"10.1016\/j.array.2026.100888_bib14","series-title":"2019 24th IEEE international conference on emerging technologies and factory automation (ETFA), Zaragoza, Spain","first-page":"1","article-title":"A novel visual fault detection and classification system for semiconductor manufacturing using stacked hybrid convolutional neural networks","author":"Schlosser","year":"2019"},{"issue":"2","key":"10.1016\/j.array.2026.100888_bib15","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/TSM.2018.2825482","article-title":"Deep-structured machine learning model for the recognition of mixed-defect patterns in semiconductor fabrication processes","volume":"31","author":"Tello","year":"2018","journal-title":"IEEE Trans Semicond Manuf"},{"issue":"3","key":"10.1016\/j.array.2026.100888_bib16","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TSM.2021.3089869","article-title":"Adversarial defect detection in semiconductor manufacturing process","volume":"34","author":"Kim","year":"2021","journal-title":"IEEE Trans Semicond Manuf"},{"key":"10.1016\/j.array.2026.100888_bib17","series-title":"2023 IEEE international symposium on the physical and failure analysis of integrated circuits (IPFA), Pulau Pinang, Malaysia","first-page":"1","article-title":"Test and reliability improvement with defect-image classification and machine-learning algorithms in semiconductor industry for automotive applications","author":"Berg\u00e8s","year":"2023"},{"key":"10.1016\/j.array.2026.100888_bib18","series-title":"2019 30th annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), saratoga Springs, NY, USA","first-page":"1","article-title":"Inline inspection improvement using machine learning on broadband plasma inspector in an advanced foundry fab","author":"Guo","year":"2019"},{"issue":"2","key":"10.1016\/j.array.2026.100888_bib19","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMM.19.2.024801","article-title":"Deep learning-based detection, classification, and localization of defects in semiconductor processes","volume":"19","author":"Patel","year":"2020","journal-title":"J Micro\/Nanolithogr MEMS MOEMS"},{"key":"10.1016\/j.array.2026.100888_bib20","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s42493-024-00103-z","article-title":"Deep learning-based detection of defects in wafer buffer zone during semiconductor packaging process","volume":"6","author":"Kim","year":"2024","journal-title":"Multiscale Sci Eng"},{"key":"10.1016\/j.array.2026.100888_bib21","series-title":"2021 32nd annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","first-page":"1","article-title":"Enhanced defect detection in after develop inspection with machine learning disposition","author":"McLaughlin","year":"2021"},{"issue":"1","key":"10.1016\/j.array.2026.100888_bib22","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/TSM.2019.2963656","article-title":"Deep learning for classification of the chemical composition of particle defects on semiconductor wafers","volume":"33","author":"O'Leary","year":"2020","journal-title":"IEEE Trans Semicond Manuf"},{"issue":"1","key":"10.1016\/j.array.2026.100888_bib23","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TASE.2016.2594288","article-title":"Multifeature, sparse-based approach for defects detection and classification in semiconductor units","volume":"15","author":"Haddad","year":"2018","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"10.1016\/j.array.2026.100888_bib24","article-title":"Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing","volume":"75","author":"Wang","year":"2022","journal-title":"Robot Comput Integr Manuf"},{"key":"10.1016\/j.array.2026.100888_bib25","series-title":"2024 IEEE 22nd international conference on industrial informatics (INDIN)","first-page":"1","article-title":"An EfficientNet-based transfer learning system for defect classification in manufacturing","author":"Rasheed","year":"2024"},{"key":"10.1016\/j.array.2026.100888_bib26","series-title":"2024 international symposium ELMAR","first-page":"1","article-title":"Fed-SEMI: a federated machine learning framework for nano-scale defect classification and detection in semiconductor manufacturing with decentralized and private data","author":"Dey","year":"2024"},{"issue":"7","key":"10.1016\/j.array.2026.100888_bib27","doi-asserted-by":"crossref","first-page":"11821","DOI":"10.3934\/mbe.2023526","article-title":"A full-flow inspection method based on machine vision to detect wafer surface defects","volume":"20","author":"Yu","year":"2023","journal-title":"Math Biosci Eng"},{"key":"10.1016\/j.array.2026.100888_bib28","article-title":"DeepSEM-Net: enhancing SEM defect analysis in semiconductor manufacturing with a dual-branch CNN-transformer architecture","volume":"190","author":"Qiao","year":"2024","journal-title":"Comput Ind Eng"},{"key":"10.1016\/j.array.2026.100888_bib29","series-title":"2020 IEEE 22nd Electronics Packaging Technology Conference (EPTC)","first-page":"1","article-title":"Deep learning analysis of 3D X-ray images for automated object detection and attribute measurement of buried package features","author":"Pahwa","year":"2020"},{"key":"10.1016\/j.array.2026.100888_bib30","series-title":"2023 34th annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","first-page":"1","article-title":"Combining full wafer inspection with deep learning to recognize wafers with critical defects","author":"Anger","year":"2023"},{"key":"10.1016\/j.array.2026.100888_bib31","series-title":"2024 35th annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","first-page":"1","article-title":"Effective downsampling techniques for SEM defect inspection using design insights in machine learning","author":"Xie","year":"2024"},{"key":"10.1016\/j.array.2026.100888_bib32","series-title":"2023 27th international computer science and engineering conference (ICSEC)","first-page":"1","article-title":"GENSS: defect classification method on extremely small datasets for semiconductor manufacturing","author":"Yuen","year":"2023"},{"key":"10.1016\/j.array.2026.100888_bib33","series-title":"Artificial intelligence for digitising industry \u2013 applications","first-page":"1","article-title":"Automated anomaly detection through assembly and packaging process","author":"Al-Baddai","year":"2021"},{"issue":"13","key":"10.1016\/j.array.2026.100888_bib34","doi-asserted-by":"crossref","first-page":"17743","DOI":"10.1364\/OE.27.017743","article-title":"Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm","volume":"27","author":"Purandare","year":"2019","journal-title":"Opt Express"},{"key":"10.1016\/j.array.2026.100888_bib35","series-title":"2024 IEEE 10th electronics system-integration technology conference","first-page":"1","article-title":"AI deep-learning approach for manufacturing optimization during chiplets and heterogeneous package inspection","author":"Chitchian","year":"2024"},{"key":"10.1016\/j.array.2026.100888_bib36","series-title":"2022 IEEE 72nd electronic components and technology conference (ECTC)","first-page":"1","article-title":"Automated detection and segmentation of HBMs in 3D X-ray images using semi-supervised deep learning","author":"Pahwa","year":"2022"},{"key":"10.1016\/j.array.2026.100888_bib37","series-title":"2020 IEEE Region 10 conference (TENCON)","first-page":"1","article-title":"Semiconductor wafer surface: automatic defect classification with deep CNN","author":"Phua","year":"2020"},{"issue":"4","key":"10.1016\/j.array.2026.100888_bib38","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1109\/TSM.2019.2941752","article-title":"A CNN-based transfer learning method for defect classification in semiconductor manufacturing","volume":"32","author":"Imoto","year":"2019","journal-title":"IEEE Trans Semicond Manuf"},{"key":"10.1016\/j.array.2026.100888_bib39","series-title":"2023 15th international conference on developments in eSystems engineering (DeSE)","first-page":"1","article-title":"CNN aided surface inspection for SMT manufacturing","author":"Loo","year":"2023"},{"key":"10.1016\/j.array.2026.100888_bib40","series-title":"2023 IEEE international conference on Electro Information Technology (eIT)","first-page":"1","article-title":"AI-based localization and classification of visual anomalies on semiconductor devices","author":"Le","year":"2023"},{"issue":"12","key":"10.1016\/j.array.2026.100888_bib41","doi-asserted-by":"crossref","first-page":"9668","DOI":"10.1109\/TIM.2020.3007292","article-title":"A novel method based on deep convolutional neural networks for wafer semiconductor surface defect inspection","volume":"69","author":"Wen","year":"2020","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.array.2026.100888_bib42","article-title":"Micro-crack defects detection of semiconductor Si-wafers based on barker code laser infrared thermography","volume":"124","author":"Bu","year":"2022","journal-title":"Infrared Phys Technol"},{"issue":"4","key":"10.1016\/j.array.2026.100888_bib43","article-title":"Detection of defective chips from nanostructures with a high-aspect ratio using hyperspectral imaging and deep learning","volume":"23","author":"Jun","year":"2024","journal-title":"J Micro\/Nanopatterning Mater Metrol"},{"issue":"122","key":"10.1016\/j.array.2026.100888_bib44","article-title":"Microsphere-assisted hyperspectral imaging: super-resolution, non-destructive metrology for semiconductor devices","volume":"13","author":"Park","year":"2024","journal-title":"Light Sci Appl"},{"key":"10.1016\/j.array.2026.100888_bib45","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1134\/S1061830925600297","article-title":"Infrared thermal imaging detection and image segmentation of micro-crack defects in semiconductor silicon wafer scanned by laser","volume":"61","author":"Tang","year":"2025","journal-title":"Russ J Nondestruct Test"},{"issue":"2","key":"10.1016\/j.array.2026.100888_bib46","article-title":"Toward realization of high-throughput hyperspectral imaging technique for semiconductor device metrology","volume":"21","author":"Yoon","year":"2022","journal-title":"J Micro\/Nanopatterning Mater Metrol"},{"key":"10.1016\/j.array.2026.100888_bib47","series-title":"Proc. 2024 Conf. Sci. Technol. Integr. Circuits (CSTIC), Shanghai, China","article-title":"Machine learning technologies for semiconductor manufacturing","author":"Sun","year":"2024"},{"key":"10.1016\/j.array.2026.100888_bib48","article-title":"Scalability and maintainability challenges and solutions in machine learning: systematic literature review\u201d","author":"Shivashankar","year":"2025","journal-title":"arXiv2504 11079"},{"key":"10.1016\/j.array.2026.100888_bib49","article-title":"A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing","volume":"16","author":"Ahmed","year":"2025","journal-title":"Decis Anal J"},{"key":"10.1016\/j.array.2026.100888_bib50","first-page":"1","article-title":"Application of artificial intelligence to improve chip defect detection using semiconductor equipment","volume":"98","author":"Fu","year":"2025","journal-title":"Eng Proc"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626002110?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626002110?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T20:53:16Z","timestamp":1781815996000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626002110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":47,"alternative-id":["S2590005626002110"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100888","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A critical survey of machine learning - powered vision inspection in semiconductors manufacturing","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100888","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100888"}}