A systematic approach to secure data collection across an OEM's fleet of tools

Doug Suerich, Veronica Consens
{"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.
一种系统的方法来确保OEM的工具车队的数据收集
本文讨论了PEER Group的一个团队设计和实现安全数据管理系统的方法,该系统旨在从安装在不同地理位置的各种晶圆厂的工具中收集数据,并将这些数据移动到基于云的存储系统。这项工作的动机是找到最佳方法,利用现代数据分析技术,在全球范围内匹配设备性能,从而实现预测决策。收集数据并将其输入远程分析软件以执行船队范围内的比较,存在与知识产权保护、大数据管理和实施风险相关的常见障碍。创建这样一个数据收集系统的主要工作不在于数据的收集或移动,而在于系统地识别和评估对数据共享的异议,这是一个出了名的秘密行业。通过明确地解决与安全数据共享相关的每个问题,我们能够创建一个系统,允许以所有利益相关者都可以接受的方式收集有限的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信