Fundamental statistical properties of reconstruction methodology for TDDB with variability in BEOL/MOL/FEOL applications
2016 IEEE International Reliability Physics Symposium (IRPS)(2016)
摘要
In this work, we investigate the validity of the so-called big-data deconvolution approach with its use of sampling-based reconstruction methodology for general applications to BEOL and MOL dielectrics with substantial non-uniformity and multiple variability sources. Unlike conventional statistical sampling, we have found that all parameters (β and T
63
) and area scaling characteristics of reconstructed Weibull distributions along with T
63
variation (σT
63
) across the sampling units (chips or dies) show a strong dependence on sampling number per unit (chip). We developed the statistical theory to correctly characterize the sampling number dependence of reconstructed Weibull slope and σT
63
, including a criterion for the general applicability of the sampling-based reconstruction methodology. We have examined why the big-data deconvolution approach cannot be used for BEOL/MOL dielectrics with multiple variability sources. The sampling-number dependence of reconstructed T
63
fundamentally nullifies the feasibility of this approach while sampling-number dependence of area scaling should be always demonstrated prior to the use this methodology. Finally, we show that V
BD
results can provide misleading conclusions due to the different scaling property of variance in Vbd and Tbd in use of reconstruction methodology.
更多查看译文
关键词
TDDB,dielectric breakdown,Reliability,Variability,Non-uniformity,thickness variation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要