{"title":"Optimization of test/diagnosis/rework location(s) and characteristics in electronic systems assembly using real-coded genetic algorithms","authors":"Zhen Shi, P. Sandborn","doi":"10.1109/TEST.2003.1271080","DOIUrl":null,"url":null,"abstract":"This paper presents a framework for optimizing the location(s) and characteristics (fault coverage/test cost, rework success rate/rework cost) of Test/Diagnosis/Rework (TDR) operations in the assembly process for electronic systems. A new search algorithm called Waiting Sequence Search (WSS) is applied to traverse a general process flow in order to perform the cumulative calculation of a yielded cost objective function. Real-Coded Genetic Algorithms (RCGAs) are used to perform a multi-variable optimization that minimizes yielded cost. Several simple cases are analyzed for validation and a general complex process flow is used to demonstrate the applicability of the algorithm.","PeriodicalId":236182,"journal":{"name":"International Test Conference, 2003. Proceedings. ITC 2003.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Test Conference, 2003. Proceedings. ITC 2003.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TEST.2003.1271080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents a framework for optimizing the location(s) and characteristics (fault coverage/test cost, rework success rate/rework cost) of Test/Diagnosis/Rework (TDR) operations in the assembly process for electronic systems. A new search algorithm called Waiting Sequence Search (WSS) is applied to traverse a general process flow in order to perform the cumulative calculation of a yielded cost objective function. Real-Coded Genetic Algorithms (RCGAs) are used to perform a multi-variable optimization that minimizes yielded cost. Several simple cases are analyzed for validation and a general complex process flow is used to demonstrate the applicability of the algorithm.