Browsing by Author "Luo, Wen"
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Item Reliability characterization and prediction of high k dielectric thin film(Texas A&M University, 2006-04-12) Luo, WenAs technologies continue advancing, semiconductor devices with dimensions in nanometers have entered all spheres of human life. This research deals with both the statistical aspect of reliability and some electrical aspect of reliability characterization. As an example of nano devices, TaOx-based high k dielectric thin films are studied on the failure mode identification, accelerated life testing, lifetime projection, and failure rate estimation. Experiment and analysis on dielectric relaxation and transient current show that the relaxation current of high k dielectrics is distinctive to the trapping/detrapping current of SiO2; high k films have a lower leakage current but a higher relaxation current than SiO2. Based on the connection between polarization-relaxation and film integrity demonstrated in ramped voltage stress tests, a new method of breakdown detection is proposed. It monitors relaxation during the test, and uses the disappearing of relaxation current as the signal of a breakdown event. This research develops a Bayesian approach which is suitable to reliability estimation and prediction of current and future generations of nano devices. It combines the Weibull lifetime distribution with the empirical acceleration relationship, and put the model parameters into a hierarchical Bayesian structure. The value of the Bayesian approach lies in that it can fully utilize available information in modeling uncertainty and provide cogent prediction with limited resources in a reasonable period of time. Markov chain Monte Carlo simulation is used for posterior inference of the reliability projection and for sensitivity analysis over a variety of vague priors. Time-to-breakdown data collected in the accelerated life tests also are modeled with a bathtub failure rate curve. The decreasing failure rate is estimated with a non-parametric Bayesian approach, and the constant failure rate is estimated with a regular parametric Bayesian approach. This method can provide a fast and reliable estimation of failure rate for burn-in optimization when only a small sample of data is available.Item The impact of misspecifying cross-classified random effects models in cross-sectional and longitudinal multilevel data: a Monte Carlo study(2009-05-15) Luo, WenCross-classified random effects models (CCREMs) are used in the analyses of cross-sectional and longitudinal multilevel data that are not strictly hierarchical. Because of the complexity of this technique, many researchers simply ignore the cross-classified structures of their data and use hierarchical linear models. The study simulated crosssectional and longitudinal multilevel data with cross-classified structures and examined the impact of misspecifying CCREMs on parameter and standard error estimates in these data. The dissertation consists of two studies. Study One examines cross-sectional multilevel data and Study Two examines longitudinal multilevel data. In Study One, three-level cross-classified data were generated. Two random factors were crossed at either the top level or the intermediate level. It was found that ignoring a crossed random factor causes the variance of the remaining crossed factor and the adjacent levels to be overestimated. The fixed effects themselves are unbiased; however, the standard errors associated with the fixed effects are biased. When the ignored crossed factor is at the top level, the standard error of the intercept is underestimated whereas the standard error of the regression coefficients associated with the covariate of the intermediate level and the remaining crossed factor are overestimated. When the ignored crossed factor is at the intermediate level, only the standard error of the regression coefficients associated with the covariate of the bottom level is overestimated. In Study Two, longitudinal multilevel data were generated mirroring studies in which students are measured repeatedly and change schools over time. It was found that when the school level is modeled hierarchically above the student level rather than as a crossed factor, part of the variance at the school level is added to the student level, causing underestimation of the school-level variance and overestimation of the studentlevel variance and covariance. The standard errors of the intercept and the regression coefficients associated with the school-level predictors are underestimated, which may cause spurious significance for results. The findings of the dissertation enhanced our understanding of the functioning of CCREMs in both cross-sectional and longitudinal multilevel data. The findings can help researchers to determine when CCREMs should be used and to interpret their results with caution when they misspecify CCREMs.