Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Karunakaran, Deepak
- Creators: Turaga, Pavan
Description
Electromigration (EM) has been a serious reliability concern in microelectronics packaging for close to half a century now. Whenever the challenges of EM are overcome newer complications arise such as the demand for better performance due to increased miniaturization of semiconductor devices or the problems faced due to undesirable properties of lead-free solders. The motivation for the work is that there exists no fully computational modeling study on EM damage in lead-free solders (and also in lead-based solders). Modeling techniques such as one developed here can give new insights on effects of different grain features and offer high flexibility in varying parameters and study the corresponding effects. In this work, a new computational approach has been developed to study void nucleation and initial void growth in solders due to metal atom diffusion. It involves the creation of a 3D stochastic mesoscale model of the microstructure of a polycrystalline Tin structure. The next step was to identify regions of current crowding or ‘hot-spots’. This was done through solving a finite difference scheme on top of the 3D structure. The nucleation of voids due to atomic diffusion from the regions of current crowding was modeled by diffusion from the identified hot-spot through a rejection free kinetic Monte-Carlo scheme. This resulted in the net movement of atoms from the cathode to the anode. The above steps of identifying the hotspot and diffusing the atoms at the hot-spot were repeated and this lead to the initial growth of the void. This procedure was studied varying different grain parameters. In the future, the goal is to explore the effect of more grain parameters and consider other mechanisms of failure such as the formation of intermetallic compounds due to interstitial diffusion and dissolution of underbump metallurgy.
ContributorsKarunakaran, Deepak (Thesis advisor) / Jiao, Yang (Committee member) / Chawla, Nikhilesh (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2016
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