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.

This work aimed to characterize and optimize the variables that influence the Gas Diffusion Layer (GDL) preparation using design of experiment (DOE) approach. In the process of GDL preparation, the quantity of carbon support and Teflon were found to have significant influence on the Proton Exchange Membrane Fuel Cell (PEMFC). Characterization methods like surface roughness, wetting characteristics, microstructure surface morphology, pore size distribution, thermal conductivity of GDLs were examined using laser interferometer, Goniometer, SEM, porosimetry and thermal conductivity analyzer respectively. The GDLs were evaluated in single cell PEMFC under various operating conditions of temperature and relative humidity (RH) using air as oxidant. Electrodes were prepared with different PUREBLACK® and poly-tetrafluoroethylene (PTFE) content in the diffusion layer and maintaining catalytic layer with a Pt-loading (0.4 mg cm-2). In the study, a 73.16 wt.% level of PB and 34 wt.% level of PTFE was the optimal compositions for GDL at 70 °C for 70% RH under air atmosphere.

For most electrochemical processes the oxygen reduction is very vita reaction. Pt loading in the electrocatalyst contributes towards the total cost of electrochemical devices. Reducing the Pt loading in electrocatalysts with high efficiency is important for the development of fuel cell technologies. To this end, this thesis work reports the approach to lower down the Pt loading in electrocatalyst based on N-doped carbon nanotubes derived from Zeolitic Imidazolate Frameworks (ZIF-67) for oxygen reduction. This electrocatalyst perform with higher electrocatalytic activity and stability for oxygen reduction in fuel cell testing. The electrochemical properties are mainly due to the synergistic effect from N-doped carbon nanotubes derived from ZIF and Pt loading. The strategy with low Pt loading forecasts in emerging highly active and less expensive electrocatalysts in electrochemical energy devices.

This thesis focuses on: (i) methods to obtain greater power density by optimizing content of wet-proofing agent (PTFE) and fine-grained, hydrophobic, microporous layer (MPL); (ii) modeling full factorial analysis of PEMFC for evaluation with experimental results and predicting further improvements in performance; (iii) methods to obtain high levels of performance with low Pt loading electrodes based on N-doped carbon nanotubes derived from ZIF-67 and Pt.