{"title":"An effective SPC approach to monitoring semiconductor manufacturing processes with multiple variation sources","authors":"A. Chen, R. Guo, P.-J. Yeh","doi":"10.1109/ISSM.2000.993710","DOIUrl":null,"url":null,"abstract":"In this research, we develop an integrated sampling and statistical process control (SPC) strategy for semiconductor processes with multiple variation sources. We first construct a process model to characterize the complex nature of semiconductor processes. Three types of variations: among-site, among-zone and among-batch variations, are considered in the model. Based on this process model and rational sub-grouping techniques, multivariate. T/sup 2/ control charts are then proposed to monitor the process variations. It is shown that the proposed control charts are more effective than conventional charting techniques in detecting various types of process excursions.","PeriodicalId":104122,"journal":{"name":"Proceedings of ISSM2000. Ninth International Symposium on Semiconductor Manufacturing (IEEE Cat. No.00CH37130)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of ISSM2000. Ninth International Symposium on Semiconductor Manufacturing (IEEE Cat. No.00CH37130)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSM.2000.993710","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this research, we develop an integrated sampling and statistical process control (SPC) strategy for semiconductor processes with multiple variation sources. We first construct a process model to characterize the complex nature of semiconductor processes. Three types of variations: among-site, among-zone and among-batch variations, are considered in the model. Based on this process model and rational sub-grouping techniques, multivariate. T/sup 2/ control charts are then proposed to monitor the process variations. It is shown that the proposed control charts are more effective than conventional charting techniques in detecting various types of process excursions.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
