• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Risk analysis >Bayesian Network Model with Application to Smart Power Semiconductor Lifetime Data
【24h】

Bayesian Network Model with Application to Smart Power Semiconductor Lifetime Data

机译：贝叶斯网络模型及其在智能功率半导体寿命数据中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this article, Bayesian networks are used to model semiconductor lifetime data obtained from a cyclic stress test system. The data of interest are a mixture of log-normal distributions, representing two dominant physical failure mechanisms. Moreover, the data can be censored due to limited test resources. For a better understanding of the complex lifetime behavior, interactions between test settings, geometric designs, material properties, and physical parameters of the semiconductor device are modeled by a Bayesian network. Statistical toolboxes in MATLAB (R) have been extended and applied to find the best structure of the Bayesian network and to perform parameter learning. Due to censored observations Markov chain Monte Carlo (MCMC) simulations are employed to determine the posterior distributions. For model selection the automatic relevance determination (ARD) algorithm and goodness-of-fit criteria such as marginal likelihoods, Bayes factors, posterior predictive density distributions, and sum of squared errors of prediction (SSEP) are applied and evaluated. The results indicate that the application of Bayesian networks to semiconductor reliability provides useful information about the interactions between the significant covariates and serves as a reliable alternative to currently applied methods.
机译：在本文中，贝叶斯网络用于对从循环应力测试系统获得的半导体寿命数据进行建模。感兴趣的数据是对数正态分布的混合，代表两种主要的物理故障机制。此外，由于有限的测试资源，可以检查数据。为了更好地理解复杂的寿命行为，半导体器件的测试设置，几何设计，材料特性和物理参数之间的相互作用通过贝叶斯网络建模。已经扩展了MATLAB（R）中的统计工具箱，并将其应用于查找贝叶斯网络的最佳结构并执行参数学习。由于审查的观察，马尔可夫链蒙特卡罗（MCMC）模拟被用来确定后验分布。对于模型选择，应用并评估了自动相关性确定（ARD）算法和拟合优度标准，例如边际似然，贝叶斯因子，后验预测密度分布和预测平方误差总和（SSEP）。结果表明，贝叶斯网络在半导体可靠性上的应用提供了有关重要协变量之间相互作用的有用信息，并且可以作为当前应用方法的可靠替代方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号