LELAPE:根据存储器辐射地面测试中的 SEUs 倍率对其进行分类的开源工具

IF 1.9 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Nuclear Science Pub Date : 2024-08-27 DOI:10.1109/TNS.2024.3450607
Juan A. Clemente;Mohammadreza Rezaei;Juan C. Fabero;Hortensia Mecha;Francisco J. Franco
{"title":"LELAPE:根据存储器辐射地面测试中的 SEUs 倍率对其进行分类的开源工具","authors":"Juan A. Clemente;Mohammadreza Rezaei;Juan C. Fabero;Hortensia Mecha;Francisco J. Franco","doi":"10.1109/TNS.2024.3450607","DOIUrl":null,"url":null,"abstract":"This article presents Listas de Eventos Localizando Anomalías al Preparar Estadísticas (LELAPE), an easy-to-use tool that aims at classifying the single-event upsets (SEUs) that were observed in radiation-ground experiments on a memory or a field-programmable gate array (FPGA) into single-bit upsets (SBUs) and multiple-cell upsets (MCUs) with various multiplicities. This tool takes as input one or several datasets obtained in radiation experiments and returns as output the list of events that were identified, without any limitation on the type of device (SRAMs, DRAMs, PSRAMs, FPGAs, and so on) or manufacturing technology (planar, FinFET, and so on). The classification method used consists in analyzing statistical anomalies found in the input dataset(s) that would not be found in a theoretical scenario where only single-bit upsets (SBUs) can occur. It will be proven that the prediction accuracy attained is very high, by using data issued from actual experiments carried out by the authors on several SRAMs under protons and neutrons with various energies. This tool has been made available to the Community through a Zenodo repository and protected by the European Union Public License (EUPL).","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"71 10","pages":"2260-2271"},"PeriodicalIF":1.9000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10649596","citationCount":"0","resultStr":"{\"title\":\"LELAPE: An Open-Source Tool to Classify SEUs According to Their Multiplicity in Radiation-Ground Tests on Memories\",\"authors\":\"Juan A. Clemente;Mohammadreza Rezaei;Juan C. Fabero;Hortensia Mecha;Francisco J. Franco\",\"doi\":\"10.1109/TNS.2024.3450607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents Listas de Eventos Localizando Anomalías al Preparar Estadísticas (LELAPE), an easy-to-use tool that aims at classifying the single-event upsets (SEUs) that were observed in radiation-ground experiments on a memory or a field-programmable gate array (FPGA) into single-bit upsets (SBUs) and multiple-cell upsets (MCUs) with various multiplicities. This tool takes as input one or several datasets obtained in radiation experiments and returns as output the list of events that were identified, without any limitation on the type of device (SRAMs, DRAMs, PSRAMs, FPGAs, and so on) or manufacturing technology (planar, FinFET, and so on). The classification method used consists in analyzing statistical anomalies found in the input dataset(s) that would not be found in a theoretical scenario where only single-bit upsets (SBUs) can occur. It will be proven that the prediction accuracy attained is very high, by using data issued from actual experiments carried out by the authors on several SRAMs under protons and neutrons with various energies. This tool has been made available to the Community through a Zenodo repository and protected by the European Union Public License (EUPL).\",\"PeriodicalId\":13406,\"journal\":{\"name\":\"IEEE Transactions on Nuclear Science\",\"volume\":\"71 10\",\"pages\":\"2260-2271\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10649596\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Nuclear Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10649596/\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Nuclear Science","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10649596/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了 Listas de Eventos Localizando Anomalías al Preparar Estadísticas (LELAPE),这是一种易于使用的工具,旨在将在存储器或现场可编程门阵列(FPGA)辐照实验中观察到的单次事件颠倒(SEUs)分为单比特颠倒(SBUs)和具有不同倍数的多单元颠倒(MCUs)。该工具将辐射实验中获得的一个或多个数据集作为输入,并将识别出的事件列表作为输出返回,对器件类型(SRAM、DRAM、PSRAM、FPGA 等)或制造技术(平面、FinFET 等)没有任何限制。使用的分类方法包括分析输入数据集中发现的统计异常,这些异常在理论情况下是不会出现的,因为理论情况下只可能出现单比特中断(SBU)。通过使用作者在不同能量的质子和中子作用下对多个 SRAM 进行的实际实验所获得的数据,可以证明预测精度非常高。该工具已通过 Zenodo 存储库提供给社区使用,并受到欧盟公共许可证(EUPL)的保护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
LELAPE: An Open-Source Tool to Classify SEUs According to Their Multiplicity in Radiation-Ground Tests on Memories
This article presents Listas de Eventos Localizando Anomalías al Preparar Estadísticas (LELAPE), an easy-to-use tool that aims at classifying the single-event upsets (SEUs) that were observed in radiation-ground experiments on a memory or a field-programmable gate array (FPGA) into single-bit upsets (SBUs) and multiple-cell upsets (MCUs) with various multiplicities. This tool takes as input one or several datasets obtained in radiation experiments and returns as output the list of events that were identified, without any limitation on the type of device (SRAMs, DRAMs, PSRAMs, FPGAs, and so on) or manufacturing technology (planar, FinFET, and so on). The classification method used consists in analyzing statistical anomalies found in the input dataset(s) that would not be found in a theoretical scenario where only single-bit upsets (SBUs) can occur. It will be proven that the prediction accuracy attained is very high, by using data issued from actual experiments carried out by the authors on several SRAMs under protons and neutrons with various energies. This tool has been made available to the Community through a Zenodo repository and protected by the European Union Public License (EUPL).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Nuclear Science
IEEE Transactions on Nuclear Science 工程技术-工程:电子与电气
CiteScore
3.70
自引率
27.80%
发文量
314
审稿时长
6.2 months
期刊介绍: The IEEE Transactions on Nuclear Science is a publication of the IEEE Nuclear and Plasma Sciences Society. It is viewed as the primary source of technical information in many of the areas it covers. As judged by JCR impact factor, TNS consistently ranks in the top five journals in the category of Nuclear Science & Technology. It has one of the higher immediacy indices, indicating that the information it publishes is viewed as timely, and has a relatively long citation half-life, indicating that the published information also is viewed as valuable for a number of years. The IEEE Transactions on Nuclear Science is published bimonthly. Its scope includes all aspects of the theory and application of nuclear science and engineering. It focuses on instrumentation for the detection and measurement of ionizing radiation; particle accelerators and their controls; nuclear medicine and its application; effects of radiation on materials, components, and systems; reactor instrumentation and controls; and measurement of radiation in space.
期刊最新文献
Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents IEEE Transactions on Nuclear Science information for authors IEEE Transactions on Nuclear Science publication information 2024 Index IEEE Transactions on Nuclear Science Vol. 71
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1