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":"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}
引用次数: 1
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%.