Statistical inference of a time-to-failure distribution derived from linear degradation
Statistical inference of a time-to-failure distribution derived from linear degradation
复制标题
DOI：
10.2307/1271503
复制
发表时间：
1997-11
期刊：
影响因子：
2.5
通讯作者：
Jye-Chyi Lu;Jinho Park;Qing Yang
中科院分区：
文献类型：
--
作者：
Jye-Chyi Lu;Jinho Park;Qing Yang
在半导体退化的研究中，跨导损耗或阈值电压随时间变化的记录对于构建退化达到指定水平之前的时间累积分布函数 (cdf) 很有用。在本文中，我们提出了一个具有随机回归系数和标准差函数的模型，用于分析线性退化数据。提供了该模型的分析和实证动机。我们通过最大似然 (ML) 方法估计模型参数、cdf 及其分位数，并根据自举、渐近正态近似和反似然比检验构建置信区间。进行模拟以检查 ML 估计和置信区间的属性。工程数据集的分析说明了所提出的程序。
In the study of semiconductor degradation, records of transconductance loss or threshold voltage shift over time are useful in constructing the cumulative distribution function (cdf) of the time until the degradation reaches a specified level. In this article, we propose a model with random regression coefficients and a standard-deviation function for analyzing linear degradation data. Both analytical and empirical motivations of the model are provided. We estimate the model parameters, the cdf, and its quantiles by the maximum likelihood (ML) method and construct confidence intervals from the bootstrap, from the asymptotic normal approximation, and from inverting likelihood ratio tests. Simulations are conducted to examine the properties of the ML estimates and the confidence intervals. Analysis of an engineering dataset illustrates the proposed procedures.