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Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery

Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery dates. Discrete Event Simulation (DES) models are often used to model these complex manufacturing systems in order to generate estimates of the cycle time distribution. However, building models and executing them consumes sufficient time and resources. The objective of this research is to determine the influence of input parameters on the cycle time distribution of a semiconductor or high volume electronics manufacturing system. This will help the decision makers to implement system changes to improve the predictability of their cycle time distribution without having to run simulation models. In order to understand how input parameters impact the cycle time, Design of Experiments (DOE) is performed. The response variables considered are the attributes of cycle time distribution which include the four moments and percentiles. The input to this DOE is the output from the simulation runs. Main effects, two-way and three-way interactions for these input variables are analyzed. The implications of these results to real world scenarios are explained which would help manufactures understand the effects of the interactions between the input factors on the estimates of cycle time distribution. The shape of the cycle time distributions is different for different types of systems. Also, DES requires substantial resources and time to run. In an effort to generalize the results obtained in semiconductor manufacturing analysis, a non- complex system is considered.
ContributorsSalvi, Tanushree Ashutosh (Author) / Bekki, Jennifer M (Thesis advisor) / Sodemann, Angela (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the growing popularity of 3d printing in recreational, research, and commercial enterprises new techniques and processes are being developed to improve the quality of parts created. Even so, the anisotropic properties is still a major hindrance of parts manufactured in this method. The goal is to produce parts that

With the growing popularity of 3d printing in recreational, research, and commercial enterprises new techniques and processes are being developed to improve the quality of parts created. Even so, the anisotropic properties is still a major hindrance of parts manufactured in this method. The goal is to produce parts that mimic the strength characteristics of a comparable part of the same design and materials created using injection molding. In achieving this goal the production cost can be reduced by eliminating the initial investment needed for the creation of expensive tooling. This initial investment reduction will allow for a wider variant of products in smaller batch runs to be made available. This thesis implements the use of ultraviolet (UV) illumination for an in-process laser local pre-deposition heating (LLPH). By comparing samples with and without the LLPH process it is determined that applied energy that is absorbed by the polymer is converted to an increase in the interlayer temperature, and resulting in an observed increase in tensile strength over the baseline test samples. The increase in interlayer bonding thus can be considered the dominating factor over polymer degradation.
ContributorsKusel, Scott Daniel (Author) / Hsu, Keng (Thesis advisor) / Sodemann, Angela (Committee member) / Kannan, Arunachala M (Committee member) / Arizona State University (Publisher)
Created2017