Matching Items (2)
Description
Yield is a key process performance characteristic in the capital-intensive semiconductor fabrication process. In an industry where machines cost millions of dollars and cycle times are a number of months, predicting and optimizing yield are critical to process improvement, customer satisfaction, and financial success. Semiconductor yield modeling is essential to identifying processing issues, improving quality, and meeting customer demand in the industry. However, the complicated fabrication process, the massive amount of data collected, and the number of models available make yield modeling a complex and challenging task. This work presents modeling strategies to forecast yield using generalized linear models (GLMs) based on defect metrology data. The research is divided into three main parts. First, the data integration and aggregation necessary for model building are described, and GLMs are constructed for yield forecasting. This technique yields results at both the die and the wafer levels, outperforms existing models found in the literature based on prediction errors, and identifies significant factors that can drive process improvement. This method also allows the nested structure of the process to be considered in the model, improving predictive capabilities and violating fewer assumptions. To account for the random sampling typically used in fabrication, the work is extended by using generalized linear mixed models (GLMMs) and a larger dataset to show the differences between batch-specific and population-averaged models in this application and how they compare to GLMs. These results show some additional improvements in forecasting abilities under certain conditions and show the differences between the significant effects identified in the GLM and GLMM models. The effects of link functions and sample size are also examined at the die and wafer levels. The third part of this research describes a methodology for integrating classification and regression trees (CART) with GLMs. This technique uses the terminal nodes identified in the classification tree to add predictors to a GLM. This method enables the model to consider important interaction terms in a simpler way than with the GLM alone, and provides valuable insight into the fabrication process through the combination of the tree structure and the statistical analysis of the GLM.
ContributorsKrueger, Dana Cheree (Author) / Montgomery, Douglas C. (Thesis advisor) / Fowler, John (Committee member) / Pan, Rong (Committee member) / Pfund, Michele (Committee member) / Arizona State University (Publisher)
Created2011
Description
Intimate Partner Violence (IPV) is a common experience among (lifetime prevalence 16.5% - 54.5%); however, current research, intervention programs, and policies tend to target women of child-bearing age, leaving older adult women feeling unseen and unheard. The purpose of this study was to provide a more accurate picture of violence against women over the life course. Guided by Life Course Theory, the characteristics of trajectories of IPV events and IPV-related help-seeking were assessed among a sample of community-dwelling women aged 60 or older residing in the Southwest United States (n = 52). Semi-structured retrospective interviews were conducted using a Life History Calendar (LHC). The characteristics of trajectories of IPV by type (physical, psychological, sexual) and by frequency (high, low) were examined. The impact of experiencing Adverse Childhood Experiences (ACES) on trajectories of violence were analyzed to account for childhood victimization in the life course. To better understand IPV-related help-seeking behaviors, the characteristics of trajectories of IPV-related help-seeking by age, type of IPV, and frequency of IPV were examined. Generalized linear mixed modeling was used to evaluate whether the probability of experiencing IPV and seeking IPV-related help changed over the life course. Half of the women in the sample experienced IPV at age 45 or later (n = 28; 53.8%), with approximately one-quarter of the women in an intimate relationship reporting IPV at time of interview (n = 6; 27.3%). Findings revealed curvilinear characteristics of IPV experience by type and frequency over the life course, with the probability of IPV events increasing earlier in life then decreasing later in life. Compared to previous studies that report IPV events decreasing in the latter 20s, the probability of experiencing IPV events increased later into adulthood (mid to late thirties among women in the study sample). The probability of seeking IPV-related help increased earlier in the life course and then declined, with the occurrence of IPV of all types significantly affecting trajectories of help-seeking behavior. Findings from this study contribute evidence needed for the recommendation of IPV screening into older adulthood and the adaptation of supportive services for older women seeking IPV-related help.
ContributorsGarbe, Renee Andersen (Author) / Stalker, Katie C (Thesis advisor) / Oh, Hyunsung (Committee member) / Messing, Jill (Committee member) / Arizona State University (Publisher)
Created2021