The as-grown-generation (AG) model: A reliable model for reliability prediction under real use conditions

J. F. Zhang, Z. Ji, W. Zhang
{"title":"The as-grown-generation (AG) model: A reliable model for reliability prediction under real use conditions","authors":"J. F. Zhang, Z. Ji, W. Zhang","doi":"10.1109/IPFA.2017.8060059","DOIUrl":null,"url":null,"abstract":"Modeling the negative bias temperature instability (NBTI) can optimize circuit design. Several models have been proposed and all of them can fit test data well. These models are extracted typically by fitting short accelerated stress data. Their capability to predict NBTI aging outside the test range has not been fully demonstrated. This predictive capability for long term aging under low operation bias is what needed by circuit designers. In this work, we test the predictive capability of the well-known reaction-diffusion (RD) based framework for samples fabricated by a variety of processes. Results show that the RD model cannot make an acceptable generic prediction. The recently proposed As-grown-Generation (AG) model is then introduced. By dividing defects into two groups, as-grown and generated defects, and measuring the as-grown defects experimentally, we demonstrate that it can make reliable prediction for the same set of data where the RD model failed.","PeriodicalId":427409,"journal":{"name":"2017 IEEE 24th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 24th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPFA.2017.8060059","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

Modeling the negative bias temperature instability (NBTI) can optimize circuit design. Several models have been proposed and all of them can fit test data well. These models are extracted typically by fitting short accelerated stress data. Their capability to predict NBTI aging outside the test range has not been fully demonstrated. This predictive capability for long term aging under low operation bias is what needed by circuit designers. In this work, we test the predictive capability of the well-known reaction-diffusion (RD) based framework for samples fabricated by a variety of processes. Results show that the RD model cannot make an acceptable generic prediction. The recently proposed As-grown-Generation (AG) model is then introduced. By dividing defects into two groups, as-grown and generated defects, and measuring the as-grown defects experimentally, we demonstrate that it can make reliable prediction for the same set of data where the RD model failed.
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
成年(AG)模型:在实际使用条件下进行可靠性预测的可靠模型
对负偏置温度不稳定性(NBTI)进行建模可以优化电路设计。提出了几种模型,它们都能很好地拟合测试数据。这些模型通常是通过拟合短加速应力数据来提取的。它们预测测试范围外NBTI老化的能力尚未得到充分证明。这种低工作偏置下长期老化的预测能力是电路设计者所需要的。在这项工作中,我们测试了众所周知的基于反应扩散(RD)的框架对各种工艺制造的样品的预测能力。结果表明,RD模型不能做出可接受的通用预测。然后介绍了最近提出的as -grown generation (AG)模型。通过将缺陷分为两组,已生长的缺陷和已生成的缺陷,并通过实验测量已生长的缺陷,我们证明它可以对RD模型失败的同一组数据做出可靠的预测。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
K-band low-noise amplifier with stacked-diode ESD protection in nanoscale CMOS technology The as-grown-generation (AG) model: A reliable model for reliability prediction under real use conditions Reproducibility of implanted dosage measurement with CAMECA Wf Multiscale modeling of defect-related phenomena in high-k based logic and memory devices ESD protection design on T/R switch with embedded SCR in CMOS process
×
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