{"title":"Diagnostic system for IC manufacturing","authors":"J. Kibarian, A. Strojwas","doi":"10.1109/ASMC.1991.167392","DOIUrl":null,"url":null,"abstract":"A diagnostic system for intrawafer variability analysis which is part of the CMU (Carnegie Mellon University) diagnostic system is described. The system presented can be used to analyze parametric data for the purposes of understanding the causes of yield loss. Data from an industrial fabrication line were analyzed to show the validity of the approach. Key features of this approach are a brief learning phase and the ability to use both spatial information and electrical characteristics in the analysis. The intrawafer variability analysis system can interpret the data by comparing the features with either historical data, test structure measurements, or simulated sensitivity information. This is possible because of the flexibility built into the feature selection method.<<ETX>>","PeriodicalId":177186,"journal":{"name":"[1991 Proceedings] IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop","volume":"86 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991 Proceedings] IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASMC.1991.167392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A diagnostic system for intrawafer variability analysis which is part of the CMU (Carnegie Mellon University) diagnostic system is described. The system presented can be used to analyze parametric data for the purposes of understanding the causes of yield loss. Data from an industrial fabrication line were analyzed to show the validity of the approach. Key features of this approach are a brief learning phase and the ability to use both spatial information and electrical characteristics in the analysis. The intrawafer variability analysis system can interpret the data by comparing the features with either historical data, test structure measurements, or simulated sensitivity information. This is possible because of the flexibility built into the feature selection method.<>