{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:17:30Z","timestamp":1768457850544,"version":"3.49.0"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,23]]},"DOI":"10.1109\/coins51742.2021.9524156","type":"proceedings-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T21:04:33Z","timestamp":1630616673000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Defective Wafer Detection Using Sensed Numerical Features"],"prefix":"10.1109","author":[{"given":"Kotcharat","family":"Kitchat","sequence":"first","affiliation":[]},{"given":"Ching-Yu","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Min-Te","family":"Sun","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/66.999602","article-title":"A neural-network approach for semiconductor wafer post-sawing inspection","volume":"15","author":"su","year":"2002","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"ref11","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref13","author":"gron","year":"2017","journal-title":"Hands-on machine learning with Scikit-Learn and TensorFlow concepts tools and techniques to build intelligent systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40585-3_14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/EDAPS.2018.8680907"},{"key":"ref16","article-title":"Sino-American Silicon Products Inc","year":"0"},{"key":"ref17","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume":"abs 1412 3555","author":"chung","year":"2014","journal-title":"CoRR"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/490\/7\/072062"},{"key":"ref19","first-page":"29","article-title":"A study of the credit risk analysis based on xgboost","volume":"21","author":"tianao","year":"2018","journal-title":"Software Engineering"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2016.7527389"},{"key":"ref3","article-title":"Semiconductor integrated test structures for electron beam inspection of semiconductor wafers","author":"sun","year":"2010"},{"key":"ref6","volume":"1","year":"1985","journal-title":"IJCAI&#x2019;85 Proceedings of the 9th International Joint Conference on Artificial Intelligence"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-04528-3","author":"holzinger","year":"2014","journal-title":"Biomedical Informatics Discovering Knowledge in Big Data"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2018.2795466"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.11.032"},{"key":"ref2","article-title":"High throughput brightfield\/darkfield wafer inspection system using advanced optical techniques","author":"fairley","year":"2007"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/34.3863"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aci.2018.02.002"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2013.774474"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.10.062"},{"key":"ref21","first-page":"1171","article-title":"Fusion decision model for vehicle lane change with gradient boosting decision tree","volume":"53","author":"xu","year":"2019","journal-title":"Journal of Zhejiang University (Engineering Science)"}],"event":{"name":"2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)","location":"Barcelona, Spain","start":{"date-parts":[[2021,8,23]]},"end":{"date-parts":[[2021,8,25]]}},"container-title":["2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9523985\/9523986\/09524156.pdf?arnumber=9524156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T07:00:08Z","timestamp":1699426808000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9524156\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/coins51742.2021.9524156","relation":{},"subject":[],"published":{"date-parts":[[2021,8,23]]}}}