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Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Carrier lifetime is one of the few parameters which can give information about the low defect densities in today's semiconductors. In principle there is no lower limit to the defect density determined by lifetime measurements. No other technique can easily detect defect densities as low as 10-9 - 10-10 cm-3 in a simple, contactless room temperature measurement. However in practice, recombination lifetime τr measurements such as photoconductance decay (PCD) and surface photovoltage (SPV) that are widely used for characterization of bulk wafers face serious limitations when applied to thin epitaxial layers, where the layer thickness is smaller than the minority carrier diffusion length Ln. Other methods such as microwave photoconductance decay (µ-PCD), photoluminescence (PL), and frequency-dependent SPV, where the generated excess carriers are confined to the epitaxial layer width by using short excitation wavelengths, require complicated configuration and extensive surface passivation processes that make them time-consuming and not suitable for process screening purposes. Generation lifetime τg, typically measured with pulsed MOS capacitors (MOS-C) as test structures, has been shown to be an eminently suitable technique for characterization of thin epitaxial layers. It is for these reasons that the IC community, largely concerned with unipolar MOS devices, uses lifetime measurements as a "process cleanliness monitor." However when dealing with ultraclean epitaxial wafers, the classic MOS-C technique measures an effective generation lifetime τg eff which is dominated by the surface generation and hence cannot be used for screening impurity densities. I have developed a modified pulsed MOS technique for measuring generation lifetime in ultraclean thin p/p+ epitaxial layers which can be used to detect metallic impurities with densities as low as 10-10 cm-3. The widely used classic version has been shown to be unable to effectively detect such low impurity densities due to the domination of surface generation; whereas, the modified version can be used suitably as a metallic impurity density monitoring tool for such cases.
ContributorsElhami Khorasani, Arash (Author) / Alford, Terry (Thesis advisor) / Goryll, Michael (Committee member) / Bertoni, Mariana (Committee member) / Arizona State University (Publisher)
Created2013
Description
High electron mobility transistors (HEMTs) based on Group III-nitride heterostructures have been characterized by advanced electron microscopy methods including off-axis electron holography, nanoscale chemical analysis, and electrical measurements, as well as other techniques. The dissertation was organized primarily into three topical areas: (1) characterization of near-gate defects in electrically stressed AlGaN/GaN HEMTs, (2) microstructural and chemical analysis of the gate/buffer interface of AlN/GaN HEMTs, and (3) studies of the impact of laser-liftoff processing on AlGaN/GaN HEMTs. The electrical performance of stressed AlGaN/GaN HEMTs was measured and the devices binned accordingly. Source- and drain-side degraded, undegraded, and unstressed devices were then prepared via focused-ion-beam milling for examination. Defects in the near-gate region were identified and their correlation to electrical measurements analyzed. Increased gate leakage after electrical stressing is typically attributed to "V"-shaped defects at the gate edge. However, strong evidence was found for gate metal diffusion into the barrier layer as another contributing factor. AlN/GaN HEMTs grown on sapphire substrates were found to have high electrical performance which is attributed to the AlN barrier layer, and robust ohmic and gate contact processes. TEM analysis identified oxidation at the gate metal/AlN buffer layer interface. This thin a-oxide gate insulator was further characterized by energy-dispersive x-ray spectroscopy and energy-filtered TEM. Attributed to this previously unidentified layer, high reverse gate bias up to −30 V was demonstrated and drain-induced gate leakage was suppressed to values of less than 10−6 A/mm. In addition, extrinsic gm and ft * LG were improved to the highest reported values for AlN/GaN HEMTs fabricated on sapphire substrates. Laser-liftoff (LLO) processing was used to separate the active layers from sapphire substrates for several GaN-based HEMT devices, including AlGaN/GaN and InAlN/GaN heterostructures. Warpage of the LLO samples resulted from relaxation of the as-grown strain and strain arising from dielectric and metal depositions, and this strain was quantified by both Newton's rings and Raman spectroscopy methods. TEM analysis demonstrated that the LLO processing produced no detrimental effects on the quality of the epitaxial layers. TEM micrographs showed no evidence of either damage to the ~2 μm GaN epilayer generated threading defects.
ContributorsJohnson, Michael R. (Author) / Mccartney, Martha R (Thesis advisor) / Smith, David J. (Committee member) / Goodnick, Stephen (Committee member) / Shumway, John (Committee member) / Chen, Tingyong (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Photodetectors in the 1.7 to 4.0 μm range are being commercially developed on InP substrates to meet the needs of longer wavelength applications such as thermal and medical sensing. Currently, these devices utilize high indium content metamorphic Ga1-xInxAs (x > 0.53) layers to extend the wavelength range beyond the 1.7 μm achievable using lattice matched GaInAs. The large lattice mismatch required to reach the extended wavelengths results in photodetector materials that contain a large number of misfit dislocations. The low quality of these materials results in a large nonradiative Shockley Read Hall generation/recombination rate that is manifested as an undesirable large thermal noise level in these photodetectors. This work focuses on utilizing the different band structure engineering methods to design more efficient devices on InP substrates. One prospective way to improve photodetector performance at the extended wavelengths is to utilize lattice matched GaInAs/GaAsSb structures that have a type-II band alignment, where the ground state transition energy of the superlattice is smaller than the bandgap of either constituent material. Over the extended wavelength range of 2 to 3 μm this superlattice structure has an optimal period thickness of 3.4 to 5.2 nm and a wavefunction overlap of 0.8 to 0.4, respectively. In using a type-II superlattice to extend the cutoff wavelength there is a tradeoff between the wavelength reached and the electron-hole wavefunction overlap realized, and hence absorption coefficient achieved. This tradeoff and the subsequent reduction in performance can be overcome by two methods: adding bismuth to this type-II material system; applying strain on both layers in the system to attain strain-balanced condition. These allow the valance band alignment and hence the wavefunction overlap to be tuned independently of the wavelength cutoff. Adding 3% bismuth to the GaInAs constituent material, the resulting lattice matched Ga0.516In0.484As0.970Bi0.030/GaAs0.511Sb0.489superlattice realizes a 50% larger absorption coefficient. While as, similar results can be achieved with strain-balanced condition with strain limited to 1.9% on either layer. The optimal design rules derived from the different possibilities make it feasible to extract superlattice period thickness with the best absorption coefficient for any cutoff wavelength in the range.
ContributorsSharma, Ankur R (Author) / Johnson, Shane (Thesis advisor) / Goryll, Michael (Committee member) / Roedel, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
Ball Grid Array (BGA) using lead-free or lead-rich solder materials are widely used as Second Level Interconnects (SLI) in mounting packaged components to the printed circuit board (PCB). The reliability of these solder joints is of significant importance to the performance of microelectronics components and systems. Product design/form-factor, solder material, manufacturing process, use condition, as well as, the inherent variabilities present in the system, greatly influence product reliability. Accurate reliability analysis requires an integrated approach to concurrently account for all these factors and their synergistic effects. Such an integrated and robust methodology can be used in design and development of new and advanced microelectronics systems and can provide significant improvement in cycle-time, cost, and reliability. IMPRPK approach is based on a probabilistic methodology, focusing on three major tasks of (1) Characterization of BGA solder joints to identify failure mechanisms and obtain statistical data, (2) Finite Element analysis (FEM) to predict system response needed for life prediction, and (3) development of a probabilistic methodology to predict the reliability, as well as, the sensitivity of the system to various parameters and the variabilities. These tasks and the predictive capabilities of IMPRPK in microelectronic reliability analysis are discussed.
ContributorsFallah-Adl, Ali (Author) / Tasooji, Amaneh (Thesis advisor) / Krause, Stephen (Committee member) / Alford, Terry (Committee member) / Jiang, Hanqing (Committee member) / Mahajan, Ravi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dealloying, the selective dissolution of an elemental component from an alloy, is an important corrosion mechanism and a technological significant means to fabricate nanoporous structures for a variety of applications. In noble metal alloys, dealloying proceeds above a composition dependent critical potential, and bi-continuous structure evolves "simultaneously" as a result of the interplay between percolation dissolution and surface diffusion. In contrast, dealloying in alloys that show considerable solid-state mass transport at ambient temperature is largely unexplored despite its relevance to nanoparticle catalysts and Li-ion anodes. In my dissertation, I discuss the behaviors of two alloy systems in order to elucidate the role of bulk lattice diffusion in dealloying. First, Mg-Cd alloys are chosen to show that when the dealloying is controlled by bulk diffusion, a new type of porosity - negative void dendrites will form, and the process mirrors electrodeposition. Then, Li-Sn alloys are studied with respect to the composition, particle size and dealloying rate effects on the morphology evolution. Under the right condition, dealloying of Li-Sn supported by percolation dissolution results in the same bi-continuous structure as nanoporous noble metals; whereas lattice diffusion through the otherwise "passivated" surface allows for dealloying with no porosity evolution. The interactions between bulk diffusion, surface diffusion and dissolution are revealed by chronopotentiometry and linear sweep voltammetry technics. The better understanding of dealloying from these experiments enables me to construct a brief review summarizing the electrochemistry and morphology aspects of dealloying as well as offering interpretations to new observations such as critical size effect and encased voids in nanoporous gold. At the end of the dissertation, I will describe a preliminary attempt to generalize the morphology evolution "rules of dealloying" to all solid-to-solid interfacial controlled phase transition process, demonstrating that bi-continuous morphologies can evolve regardless of the nature of parent phase.
ContributorsChen, Qing (Author) / Sieradzki, Karl (Thesis advisor) / Friesen, Cody (Committee member) / Buttry, Daniel (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT This work seeks to develop a practical solution for short range ultrasonic communications and produce an integrated array of acoustic transmitters on a flexible substrate. This is done using flexible thin film transistor (TFT) and micro electromechanical systems (MEMS). The goal is to develop a flexible system capable of communicating in the ultrasonic frequency range at a distance of 10 - 100 meters. This requires a great deal of innovation on the part of the FDC team developing the TFT driving circuitry and the MEMS team adapting the technology for fabrication on a flexible substrate. The technologies required for this research are independently developed. The TFT development is driven primarily by research into flexible displays. The MEMS development is driving by research in biosensors and micro actuators. This project involves the integration of TFT flexible circuit capabilities with MEMS micro actuators in the novel area of flexible acoustic transmitter arrays. This thesis focuses on the design, testing and analysis of the circuit components required for this project.
ContributorsDaugherty, Robin (Author) / Allee, David R. (Thesis advisor) / Chae, Junseok (Thesis advisor) / Aberle, James T (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2012
Description
HgCdTe is currently the dominant material for infrared sensing and imaging, and is usually grown on lattice-matched bulk CdZnTe (CZT) substrates. There have been significant recent efforts to identify alternative substrates to CZT as well as alternative detector materials to HgCdTe. In this dissertation research, a wide range of transmission electron microscopy (TEM) imaging and analytical techniques was used in the characterization of epitaxial HgCdTe and related materials and substrates for third generation IR detectors. ZnTe layers grown on Si substrates are considered to be promising candidates for lattice-matched, large-area, and low-cost composite substrates for deposition of II-VI and III-V compound semiconductors with lattice constants near 6.1 Å. After optimizing MBE growth conditions including substrate pretreatment prior to film growth, as well as nucleation and growth temperatures, thick ZnTe/Si films with high crystallinity, low defect density, and excellent surface morphology were achieved. Changes in the Zn/Te flux ratio used during growth were also investigated. Small-probe microanalysis confirmed that a small amount of As was present at the ZnTe/Si interface. A microstructural study of HgCdTe/CdTe/GaAs (211)B and CdTe/GaAs (211)B heterostructures grown using MBE was carried out. High quality MBE-grown CdTe on GaAs(211)B substrates was demonstrated to be a viable composite substrate platform for HgCdTe growth. In addition, analysis of interfacial misfit dislocations and residual strain showed that the CdTe/GaAs interface was fully relaxed. In the case of HgCdTe/CdTe/ GaAs(211)B, thin HgTe buffer layers between HgCdTe and CdTe were also investigated for improving the HgCdTe crystal quality. A set of ZnTe layers epitaxially grown on GaSb(211)B substrates using MBE was studied using high resolution X-ray diffraction (HRXRD) measurements and TEM characterization in order to investigate conditions for defect-free growth. HRXRD results gave critical thickness estimates between 350 nm and 375 nm, in good agreement with theoretical predictions. Moreover, TEM results confirmed that ZnTe layers with thicknesses of 350 nm had highly coherent interfaces and very low dislocation densities, unlike samples with the thicker ZnTe layers.
ContributorsKim, Jae Jin (Author) / Smith, David J. (Thesis advisor) / McCartney, Martha R. (Committee member) / Alford, Terry L. (Committee member) / Crozier, Peter A. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery. By considering graph relations between data samples while learning sparse models, graph-embedded codes can be obtained for use in unsupervised, supervised and semi-supervised problems. Using experiments on standard datasets, it is demonstrated that the codes obtained from the proposed methods outperform several baseline algorithms. In order to facilitate sparse learning with large scale data, the paradigm of ensemble sparse coding is proposed, and different strategies for constructing weak base models are developed. Experiments with image recovery and clustering demonstrate that these ensemble models perform better when compared to conventional sparse coding frameworks. When examples from the data manifold are available, manifold constraints can be incorporated with sparse models and two approaches are proposed to combine sparse coding with manifold projection. The improved performance of the proposed techniques in comparison to sparse coding approaches is demonstrated using several image recovery experiments. In addition to these approaches, it might be required in some applications to combine multiple sparse models with different regularizations. In particular, combining an unconstrained sparse model with non-negative sparse coding is important in image analysis, and it poses several algorithmic and theoretical challenges. A convex and an efficient greedy algorithm for recovering combined representations are proposed. Theoretical guarantees on sparsity thresholds for exact recovery using these algorithms are derived and recovery performance is also demonstrated using simulations on synthetic data. Finally, the problem of non-linear compressive sensing, where the measurement process is carried out in feature space obtained using non-linear transformations, is considered. An optimized non-linear measurement system is proposed, and improvements in recovery performance are demonstrated in comparison to using random measurements as well as optimized linear measurements.
ContributorsNatesan Ramamurthy, Karthikeyan (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Karam, Lina (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013