Matching Items (3)
Filtering by
- All Subjects: Electrical Engineering
- Genre: Academic theses
Description
With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is lacking. Reliable experimental and numerical analysis of lead-free solder joints in the intermediate strain rate regime need to be investigated. This dissertation mainly focuses on exploring the mechanical shock behavior of lead-free tin-rich solder alloys via multiscale modeling and numerical simulations. First, the macroscopic stress/strain behaviors of three bulk lead-free tin-rich solders were tested over a range of strain rates from 0.001/s to 30/s. Finite element analysis was conducted to determine appropriate specimen geometry that could reach a homogeneous stress/strain field and a relatively high strain rate. A novel self-consistent true stress correction method is developed to compensate the inaccuracy caused by the triaxial stress state at the post-necking stage. Then the material property of micron-scale intermetallic was examined by micro-compression test. The accuracy of this measure is systematically validated by finite element analysis, and empirical adjustments are provided. Moreover, the interfacial property of the solder/intermetallic interface is investigated, and a continuum traction-separation law of this interface is developed from an atomistic-based cohesive element method. The macroscopic stress/strain relation and microstructural properties are combined together to form a multiscale material behavior via a stochastic approach for both solder and intermetallic. As a result, solder is modeled by porous plasticity with random voids, and intermetallic is characterized as brittle material with random vulnerable region. Thereafter, the porous plasticity fracture of the solders and the brittle fracture of the intermetallics are coupled together in one finite element model. Finally, this study yields a multiscale model to understand and predict the mechanical shock behavior of lead-free tin-rich solder joints. Different fracture patterns are observed for various strain rates and/or intermetallic thicknesses. The predictions have a good agreement with the theory and experiments.
ContributorsFei, Huiyang (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Thesis advisor) / Tasooji, Amaneh (Committee member) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis discusses the evolution of conduction mechanism in the silver (Ag) on zinc oxide (ZnO) thin film system with respect to the Ag morphology. As a plausible substitute for indium tin oxide (ITO), TCO/Metal/TCO (TMT) structure has received a lot of attentions as a prospective ITO substitute due to its low resistivity and desirable transmittance. However, the detailed conduction mechanism is not fully understood. In an attempt to investigate the conduction mechanism of the ZnO/Ag/ZnO thin film system with respect to the Ag microstructure, the top ZnO layer is removed, which offers a better view of Ag morphology by using scanning electron microscopy (SEM). With 2 nm thick Ag layer, it is seen that the Ag forms discrete islands with small islands size (r), but large separation (s); also the effective resistivity of the system is extremely high. This regime is designated as dielectric zone. In this regime, thermionic emission and activated tunneling conduction mechanisms are considered. Based on simulations, when "s" was beyond 6 nm, thermionic emission dominates; with "s" less than 6 nm, activated tunneling is the dominating mechanism. As the Ag thickness increases, the individual islands coalesce and Ag clusters are formed. At certain Ag thickness, there are one or several Ag clusters that percolate the ZnO film, and the effective resistivity of the system exhibits a tremendous drop simultaneously, because the conducting electrons do not need to overcome huge ZnO barrier to transport. This is recognized as percolation zone. As the Ag thickness grows, Ag film becomes more continuous and there are no individual islands left on the surface. The effective resistivity decreases and is comparable to the characteristics of metallic materials, so this regime is categorized as metallic zone. The simulation of the Ag thin film resistivity is performed in terms of Ag thickness, and the experimental data fits the simulation well, which supports the proposed models. Hall measurement and four point probe measurement are carried out to characterize the electrical properties of the thin film system.
ContributorsZhang, Shengke (Author) / Alford, Terry L. (Thesis advisor) / Schroder, Dieter K. (Committee member) / Tasooji, Amaneh (Committee member) / Arizona State University (Publisher)
Created2012
Description
Semantic image segmentation has been a key topic in applications involving image processing and computer vision. Owing to the success and continuous research in the field of deep learning, there have been plenty of deep learning-based segmentation architectures that have been designed for various tasks. In this thesis, deep-learning architectures for a specific application in material science; namely the segmentation process for the non-destructive study of the microstructure of Aluminum Alloy AA 7075 have been developed. This process requires the use of various imaging tools and methodologies to obtain the ground-truth information. The image dataset obtained using Transmission X-ray microscopy (TXM) consists of raw 2D image specimens captured from the projections at every beam scan. The segmented 2D ground-truth images are obtained by applying reconstruction and filtering algorithms before using a scientific visualization tool for segmentation. These images represent the corrosive behavior caused by the precipitates and inclusions particles on the Aluminum AA 7075 alloy. The study of the tools that work best for X-ray microscopy-based imaging is still in its early stages.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
ContributorsBarboza, Daniel (Author) / Turaga, Pavan (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020