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- Creators: Pfund, Michele
- Creators: Department of Information Systems
Description
Camp Carey is an annual freshman orientation program that takes place before the beginning of the semester in late July and early August. As the incoming W. P. Carey classes continue to grow each year, so to does the size of Camp. Beginning this project, we looked at potential that we could directly impact the quality of the camp experience, and ensure that Camp remains a memorable and quality experience for all involved. Camp is directed and facilitated every year by W. P. Carey staff members and a group upperclassmen, the camp directors and facilitators. Due to the direct impact that these upperclassmen have on the camp experience, we decided to focus our attention on improving the training provided to these individuals, and to emphasize a process of continuous data collection and improvement. The director training is broken into three modules that focus on risk management, facilitator selection, and facilitator training. Each of the seven exercises in the director training is based on a tool or practice used by modern companies in project management and human resources management. They were designed with three goals in mind: to immediately increase the directors' level of preparedness for Camp, to produce a written record to be used by directors in subsequent Camp seasons, and to provide directors with an introductory level of experience with concepts and tools that will benefit them in their professional careers. The facilitator training portion centers around the creation of a 1 credit, repeatable hybrid course to both reward facilitators, train them in proper conduct and materials for camp, as well as collect valuable feedback from the facilitators. The creation of a larger spring training session, designed to prepare the facilitators for activity facilitation, emergency preparedness, and representing W. P. Carey and ASU, and the implementation of a summer review training session are designed to prepare facilitators to lead the best camp possible. Further, the essays and surveys involved in the class are set up to gather valuable information and feedback from the facilitators for further improving the program year-over-year.
ContributorsJansma, Bradley (Co-author) / Cogell, Grant (Co-author) / Pfund, Michele (Thesis director) / Reali, David (Committee member) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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