基于物理的统计学在电气工程中的应用

W. P. Wheless, T. Lehman
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引用次数: 0

摘要

最被广泛接受和使用的概率定义是相对频率定义。严格遵守这种对概率的解释,需要从测量数据中实证地发展统计模型。使用概率的这一定义通常会产生足以预测最常见事件(即接近平均值的事件)发生的统计模型。然而,经验表明,由此产生的统计模型往往不足以预测罕见或极端事件的发生,因为这些事件的数据很少或根本没有数据。在电气工程和其他物理科学中,存在一种开发统计模型的替代方法。这种方法的前提是存在感兴趣的现象或相互作用的有效确定性模型。统计模型是在确定性模型的基础上,通过对确定性模型中参数的行为进行假设而得到的。基于物理的统计模型可用于推导强度和应力分布。本文提出了一个强度实例——半导体器件在电应力作用下的热失效。给出了基于推导分布的预测失效与实测数据的典型比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An application of physics-based statistics in electrical engineering
The most widely accepted and used definition of probability is the relative-frequency definition. Strict adherence to this interpretation of probability requires the empirical development of statistical models from measured data. Using this definition of probability usually results in statistical models that are adequate for predicting the occurrence of the most common events, i.e., events near the mean. However, experience has shown that the resulting statistical models are often not adequate for predicting the occurrence of rare or extreme events, for which little or no data exist. In electrical engineering and the other physical sciences, an alternative approach exists for developing the statistical models. This approach is predicated on the existence of valid deterministic models of the phenomena or interaction of interest. The statistical models are derived from the deterministic models by making assumptions about the behavior of the parameters in the deterministic models. Physics-based statistical modeling can be applied to derive both strength and stress distributions. In the present paper, a strength example is developed-the thermal failure of semiconductor devices subjected to electrical overstress. Typical comparisons of predicted failure, based on the derived distributions, to measured data are presented.
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