{"title":"A systematic approach to secure data collection across an OEM's fleet of tools","authors":"Doug Suerich, Veronica Consens","doi":"10.1109/ASMC.2018.8373164","DOIUrl":null,"url":null,"abstract":"This paper discusses the approaches that a team at PEER Group took to design and implement a secure data management system intended to collect data from tools installed at various fabs in different geographic locations and to move that data to a cloud-based storage system. The motivation for the work was to find the best way to match equipment performance across a global fleet of tools using modern analytics on the data to enable predictive decision-making. Gathering data and feeding it into remote analytics software to perform fleet-wide comparisons presents familiar obstacles related to IP protection, the management of big data, and implementation risk. The majority of the effort in creating such a data collection system did not lie in the collection or movement of the data, but rather in the systematic identification and assessment of objections to data sharing in a notoriously secretive industry. By explicitly addressing each of the concerns related to secure data sharing, we were able to create a system that allowed for limited collection of data in a means acceptable to all stakeholders.","PeriodicalId":349004,"journal":{"name":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASMC.2018.8373164","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
This paper discusses the approaches that a team at PEER Group took to design and implement a secure data management system intended to collect data from tools installed at various fabs in different geographic locations and to move that data to a cloud-based storage system. The motivation for the work was to find the best way to match equipment performance across a global fleet of tools using modern analytics on the data to enable predictive decision-making. Gathering data and feeding it into remote analytics software to perform fleet-wide comparisons presents familiar obstacles related to IP protection, the management of big data, and implementation risk. The majority of the effort in creating such a data collection system did not lie in the collection or movement of the data, but rather in the systematic identification and assessment of objections to data sharing in a notoriously secretive industry. By explicitly addressing each of the concerns related to secure data sharing, we were able to create a system that allowed for limited collection of data in a means acceptable to all stakeholders.