{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:52:47Z","timestamp":1780411967705,"version":"3.54.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Institute of Information and communications Technology Planning and Evaluation"},{"name":"Ministriy of Science and ICT","award":["RS-2021-II211343"],"award-info":[{"award-number":["RS-2021-II211343"]}]},{"name":"Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea government","award":["RS-2023-00259605"],"award-info":[{"award-number":["RS-2023-00259605"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3611966","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:38:25Z","timestamp":1758303505000},"page":"177548-177558","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Wafer Probing With SwinProbeFormer: Self-Supervised Anomaly Detection via Window-Based Swin Transformer"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1410-786X","authenticated-orcid":false,"given":"Hyekyung","family":"Yoon","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6986-5210","authenticated-orcid":false,"given":"Dahyun","family":"Won","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5801-6096","authenticated-orcid":false,"given":"Sohyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuseop","family":"Lee","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiljae","family":"Lee","sequence":"additional","affiliation":[{"name":"SEMICS, Gwangju-si, Gyeonggi-do, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyungja","family":"Park","sequence":"additional","affiliation":[{"name":"SEMICS, Gwangju-si, Gyeonggi-do, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaehoon","family":"Joo","sequence":"additional","affiliation":[{"name":"SEMICS, Gwangju-si, Gyeonggi-do, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jangwon","family":"Seo","sequence":"additional","affiliation":[{"name":"SEMICS, Gwangju-si, Gyeonggi-do, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngsoo","family":"Ha","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8064-7167","authenticated-orcid":false,"given":"Myungjoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3333247"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3039614"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2023.2175591"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20742"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2024.3352407"},{"key":"ref6","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2022","journal-title":"arXiv:2210.02186"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref9","article-title":"AnomalyBERT: Self-supervised transformer for time series anomaly detection using data degradation scheme","author":"Jeong","year":"2023","journal-title":"arXiv:2305.04468"},{"key":"ref10","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref11","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3166512"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/app10155340"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2019.2902657"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ASMC.2019.8791815"},{"key":"ref16","article-title":"Wafer map defect classification using autoencoder-based data augmentation and convolutional neural network","author":"Bao","year":"2024","journal-title":"arXiv:2411.11029"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CIS-RAM55796.2023.10370763"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120765"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2014.2364237"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2020.2995548"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1115\/DETC2023-110284"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3390\/machines10111105"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2021.3065405"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/s23208363"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ISSM.2018.8651179"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"key":"ref27","first-page":"1","article-title":"Moderntcn: A modern pure convolution structure for general time series analysis","volume-title":"Proc. The 12th Int. Conf. Learn. Represent.","author":"Luo"},{"key":"ref28","article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","author":"Xu","year":"2021","journal-title":"arXiv:2110.02642"},{"key":"ref29","article-title":"ITransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2023","journal-title":"arXiv:2310.06625"},{"key":"ref30","first-page":"9881","article-title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00456"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.14733\/cadaps.2024.S7.15-27"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.2139\/ssrn.4909173","volume-title":"A scalable transformer network for multivariate time series anomaly detection in industrial environments","author":"Hoh","year":"2024"},{"key":"ref34","article-title":"Revisiting time series outlier detection: Definitions and benchmarks","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Lai"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00093"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11173610.pdf?arnumber=11173610","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T17:42:23Z","timestamp":1760722943000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11173610\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3611966","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}