Henry Chen, Jimmy Chang, Sheng-Tsung Tsao, Junjun Zhang, Jie Du, Congcong Fan, Alex Huang, David Xu, Sam Liu, Liang Wu, Kimi Yang, Ning Gu, L. Ren, Jian Wu, A. Tan, Sunny Xia, Ivan Mao
{"title":"使用YieldStar基于衍射的叠加测量进行实时过程监控","authors":"Henry Chen, Jimmy Chang, Sheng-Tsung Tsao, Junjun Zhang, Jie Du, Congcong Fan, Alex Huang, David Xu, Sam Liu, Liang Wu, Kimi Yang, Ning Gu, L. Ren, Jian Wu, A. Tan, Sunny Xia, Ivan Mao","doi":"10.1109/IWAPS51164.2020.9286803","DOIUrl":null,"url":null,"abstract":"Real-time process monitoring (RTPM) is a method for semiconductor manufacturing monitoring and tuning using a physical prediction model. It is a fast and nondestructive process excursion measurement method which takes inputs from diffraction-based overlay measurements from YieldStar. The prediction model is created by a physical model which receives standard manufacturing information as input. The prediction capability has been validated in a manufacturing environment experiment with thin film thickness prediction difference less than 3%.","PeriodicalId":165983,"journal":{"name":"2020 International Workshop on Advanced Patterning Solutions (IWAPS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Real time process monitoring using diffraction-based overlay measurements from YieldStar\",\"authors\":\"Henry Chen, Jimmy Chang, Sheng-Tsung Tsao, Junjun Zhang, Jie Du, Congcong Fan, Alex Huang, David Xu, Sam Liu, Liang Wu, Kimi Yang, Ning Gu, L. Ren, Jian Wu, A. Tan, Sunny Xia, Ivan Mao\",\"doi\":\"10.1109/IWAPS51164.2020.9286803\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real-time process monitoring (RTPM) is a method for semiconductor manufacturing monitoring and tuning using a physical prediction model. It is a fast and nondestructive process excursion measurement method which takes inputs from diffraction-based overlay measurements from YieldStar. The prediction model is created by a physical model which receives standard manufacturing information as input. The prediction capability has been validated in a manufacturing environment experiment with thin film thickness prediction difference less than 3%.\",\"PeriodicalId\":165983,\"journal\":{\"name\":\"2020 International Workshop on Advanced Patterning Solutions (IWAPS)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Workshop on Advanced Patterning Solutions (IWAPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWAPS51164.2020.9286803\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Workshop on Advanced Patterning Solutions (IWAPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWAPS51164.2020.9286803","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real time process monitoring using diffraction-based overlay measurements from YieldStar
Real-time process monitoring (RTPM) is a method for semiconductor manufacturing monitoring and tuning using a physical prediction model. It is a fast and nondestructive process excursion measurement method which takes inputs from diffraction-based overlay measurements from YieldStar. The prediction model is created by a physical model which receives standard manufacturing information as input. The prediction capability has been validated in a manufacturing environment experiment with thin film thickness prediction difference less than 3%.