{"title":"首次探索顺序多模态为高质量的晶圆制造节省了大数据","authors":"L. Sheng, Wei Pan","doi":"10.1109/ASMC.2018.8373211","DOIUrl":null,"url":null,"abstract":"The sequenced multimodality has been for the first time proposed and explored for effectively identifying the problematic tools in wafer processing. To demonstrate the merits and values of practicing this new concept, two case studies in product yield and in inline reliability were provided in detail. This new methodology can help save big data for enhancing the quality of wafer manufacturing.","PeriodicalId":349004,"journal":{"name":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"478 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A first-time exploration into sequenced multimodality saves big data for high-quality wafer-manufacturing\",\"authors\":\"L. Sheng, Wei Pan\",\"doi\":\"10.1109/ASMC.2018.8373211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The sequenced multimodality has been for the first time proposed and explored for effectively identifying the problematic tools in wafer processing. To demonstrate the merits and values of practicing this new concept, two case studies in product yield and in inline reliability were provided in detail. This new methodology can help save big data for enhancing the quality of wafer manufacturing.\",\"PeriodicalId\":349004,\"journal\":{\"name\":\"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)\",\"volume\":\"478 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASMC.2018.8373211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASMC.2018.8373211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A first-time exploration into sequenced multimodality saves big data for high-quality wafer-manufacturing
The sequenced multimodality has been for the first time proposed and explored for effectively identifying the problematic tools in wafer processing. To demonstrate the merits and values of practicing this new concept, two case studies in product yield and in inline reliability were provided in detail. This new methodology can help save big data for enhancing the quality of wafer manufacturing.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
