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- All Subjects: engineering
- Creators: Liu, Yongming
- Creators: Alford, Terry
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
Soft magnetic alloys play a significant role for magnetic recording applications and highly sensitivity magnetic field sensors. In order to sustain the magnetic areal density growth, development of new synthesis techniques and materials is necessary. In this work, the effect of oxygen incorporation during electrodeposition of CoFe alloys on magnetic properties, magnetoresistance and structural properties has been studied. Understanding the magnetic properties often required knowledge of oxygen distribution and structural properties of the grown films. Transmission electron microscopy (TEM) was a powerful tool in this study to correlate the oxygen-distribution nanostructure to the magnetic properties of deposited films. Off-axis electron holography in TEM was used to measure magnetic domain wall width in the deposited films. Elemental depth profiles of Fe, Co, O were investigated by secondary ion mass spectroscopy (SIMS). Magnetic properties have been determined by superconducting quantum interference device (SQUID) measurements. Oxygen content in the CoFe deposited films was controlled by electrolyte composition. Films were deposited on Si 100 substrates and on other substrates such as Cu and Al. However, a good film quality was achieved on Si substrate. Electron energy loss and x-ray spectroscopies showed that the low oxygen films contained intragranular Fe2+ oxide (FeO) particles and that the high oxygen films contained intergranular Fe3+ (Fe2O3) along grain boundaries. The films with oxide present at the grain boundary had significantly increased coercivity, magnetoresistance and reduced saturation magnetization relative to the lower oxygen content films with intragranular oxide. The differences in magnetic properties between low oxygen and high oxygen concentration films were attributed to stronger mobile domain wall interactions with the grain boundary oxide layers. The very high magnetoresistance values were achieved for magnetic devices with nanocontact dimension < 100 nm and oxide incorporation in this nanoconfined geometry. The content of oxide phase in nanocontact was controlled by concentration of the Fe3+ ions in the electrodeposition solution. Magnetic device integrity was improved by varying amount of additive into plating solution. These results indicated that electrodeposited CoFe nanocontact is a novel class of materials with large application for magnetic field sensors.
ContributorsElhalawaty, Shereen (Author) / Carpenter, Ray (Thesis advisor) / Chamberlin, Ralph (Committee member) / McCartney, Martha (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2012
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Microstructure refinement and alloy additions are considered potential routes to increase high temperature performance of existing metallic superalloys used under extreme conditions. Nanocrystalline (NC) Cu-10at%Ta exhibits such improvements over microstructurally unstable NC metals, leading to enhanced creep behavior compared to its coarse-grained (CG) counterparts. However, the low melting point of Cu compared to other FCC metals, e.g., Ni, might lead to an early onset of diffusional creep mechanisms. Thus, this research seeks to study the thermo-mechanical behavior and stability of hierarchical (prepared using arc-melting) and NC (prepared by collaborators through powder pressing and annealing) Ni-Y-Zr alloys where Zr is expected to provide solid solution and grain boundary strengthening in hierarchical and NC alloys, respectively, while Ni-Y and Ni-Zr intermetallic precipitates (IMCs) would provide kinetic stability. Hierarchical alloys had microstructures stable up to 1100 °C with ultrafine eutectic of ~300 nm, dendritic arm spacing of ~10 μm, and grain size ~1-2 mm. Room temperature hardness tests along with uniaxial compression performed at 25 and 600 °C revealed that microhardness and yield strength of hierarchical alloys with small amounts of Y (0.5-1wt%) and Zr (1.5-3 wt%) were comparable to Ni-superalloys, due to the hierarchical microstructure and potential presence of nanoscale IMCs. In contrast, NC alloys of the same composition were found to be twice as hard as the hierarchical alloys. Creep tests at 0.5 homologous temperature showed active Coble creep mechanisms in hierarchical alloys at low stresses with creep rates slower than Fe-based superalloys and dislocation creep mechanisms at higher stresses. Creep in NC alloys at lower stresses was only 20 times faster than hierarchical alloys, with the difference in grain size ranging from 10^3 to 10^6 times at the same temperature. These NC alloys showed enhanced creep properties over other NC metals and are expected to have rates equal to or improved over the CG hierarchical alloys with ECAP processing techniques. Lastly, the in-situ wide-angle x-ray scattering (WAXS) measurements during quasi-static and creep tests implied stresses being carried mostly by the matrix before yielding and in the primary creep stage, respectively, while relaxation was observed in Ni5Zr for both hierarchical and NC alloys. Beyond yielding and in the secondary creep stage, lattice strains reached a steady state, thereby, an equilibrium between plastic strain rates was achieved across different phases, so that deformation reaches a saturation state where strain hardening effects are compensated by recovery mechanisms.
ContributorsSharma, Shruti (Author) / Peralta, Pedro (Thesis advisor) / Alford, Terry (Committee member) / Jiao, Yang (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2022
Description
Electron Paramagnetic Resonance (EPR) has facilitated great scientific advancements in many fields, like material science, engineering, medicine, biology, and health. EPR provided the ability to investigate samples on molecular level to detect chemical composition and identify harmful substances like free radicals. This thesis aims to explore current health and diagnostics EPR research and investigate the free radical content in related paramagnetic centers. Examining paramagnetic diagnostic markers of Cancer, Sicklecell disease, oxidative stress, and food oxidation. After exploring current literature on EPR, an experiment is designed and conducted to test seven different coffee samples (Turkish coffee, Espresso Coffee, European Coffee, Ground Arabic Coffee, American Coffee, Roasted Arabic Coffee, and Green Arabic Coffee), using Bruker ELEXSYS E580 spectrometer at x-band and under both room temperature (298 K) and low temperature (106 -113 K). Several microwave powers (1, mW, 0.25 mW, 0.16 mW, 0.06 mW, 0.04 mW) and different modulation frequency (10 G, 5 G, 3 G) are used. The results revealed average g-value was 2.009, highest linewidth was 16.312. Espresso coffee had the highest concentration of radicals, and green Arabic coffee beans had the lowest. Obtained spectra showed signals of Reactive Oxygen Species (ROS) radicals; believed to be result of natural oxidation process, as well as trace amounts of Fe3+ and other transition metals impurities, likely to be naturally found in coffee or resulting from the process of coffee production.
ContributorsMaki, Husain (Author) / Newman, Nathan (Thesis advisor) / Alford, Terry (Committee member) / Chamberlin, Ralph (Committee member) / Arizona State University (Publisher)
Created2022
Description
Organic materials have emerged as an attractive component of electronics over the past few decades, particularly in the development of efficient and stable organic light-emitting diodes (OLEDs) and organic neuromorphic devices. The electrical, chemical, physical, and optical studies of organic materials and their corresponding devices have been conducted for efficient and stable electronics. The development of efficient and stable deep blue OLED devices remains a challenge that has obstructed the progress of large-scale OLED commercialization. One approach was taken to achieve a deep blue emitter through a color tuning strategy. A new complex, PtNONS56-dtb, was designed and synthesized by controlling the energy gap between T1 and T2 energy states to achieve narrowed and blueshifted emission spectra. This emitter material showed an emission spectrum at 460 nm with a FWHM of 59 nm at room temperature in PMMA, and the PtNONS56-dtb-based device exhibited a peak EQE of 8.5% with CIE coordinates of (0.14, 0.27).
A newly developed host and electron blocking materials were demonstrated to achieve efficient and stable OLED devices. The indolocarbazole-based materials were designed to have good hole mobility and high triplet energy. BCN34 as an electron blocking material achieved the estimated LT80 of 12509 h at 1000 cd m-2 with a peak EQE of 30.3% in devices employing Pd3O3 emitter. Additionally, a device with bi-layer emissive layer structure, using BCN34 and CBP as host materials doped with PtN3N emitter, achieved a peak EQE of 16.5% with the LT97 of 351 h at 1000 cd m-2. A new neuromorphic device using Ru(bpy)3(PF6)2 as an active layer was designed to emulate the short-term characteristics of a biological synapse. This memristive device showed a similar operational mechanism with biological synapse through the movement of ions and electronic charges. Furthermore, the performance of the device showed tunability by adding salt. Ultimately, the device with 2% LiClO4 salt shows similar timescales to short-term plasticity characteristics of biological synapses.
ContributorsShin, Samuel (Author) / Li, Jian (Thesis advisor) / Adams, James (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2021
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Global photovoltaic (PV) module installation in 2018 is estimated to exceed 100 GW, and crystalline Si (c-Si) solar cell-based modules have a share more than 90% of the global PV market. To reduce the social cost of PV electricity, further developments in reliability of solar panels are expected. These will lead to realize longer module lifetime and reduced levelized cost of energy. As many as 86 failure modes are observed in PV modules [1] and series resistance increase is one of the major durability issues of all. Series resistance constitutes emitter sheet resistance, metal-semiconductor contact resistance, and resistance across the metal-solder ribbon. Solder bond degradation at the cell interconnect is one of the primary causes for increase in series resistance, which is also considered to be an invisible defect [1]. Combination of intermetallic compounds (IMC) formation during soldering and their growth due to solid state diffusion over its lifetime result in formation of weak interfaces between the solar cell and the interconnect. Thermal cycling under regular operating conditions induce thermo-mechanical fatigue over these weak interfaces resulting in contact reduction or loss. Contact reduction or loss leads to increase in series resistance which further manifests into power and fill factor loss. The degree of intermixing of metallic interfaces and contact loss depends on climatic conditions as temperature and humidity (moisture ingression into the PV module laminate) play a vital role in reaction kinetics of these layers. Modules from Arizona and Florida served as a good sample set to analyze the effects of hot and humid climatic conditions respectively. The results obtained in the current thesis quantifies the thickness of IMC formation from SEM-EDS profiles, where similar modules obtained from different climatic conditions were compared. The results indicate the thickness of the IMC and detachment degree to be growing with age and operating temperatures of the module. This can be seen in CuxSny IMC which is thicker in the case of Arizona module. The results obtained from FL
ii
aged modules also show that humidity accelerates the formation of IMC as they showed thicker AgxSny layer and weak interconnect-contact interfaces as compared to Arizona modules. It is also shown that climatic conditions have different effects on rate at which CuxSny and AgxSny intermetallic compounds are formed.
ii
aged modules also show that humidity accelerates the formation of IMC as they showed thicker AgxSny layer and weak interconnect-contact interfaces as compared to Arizona modules. It is also shown that climatic conditions have different effects on rate at which CuxSny and AgxSny intermetallic compounds are formed.
ContributorsBuddha, Viswa Sai Pavan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Alford, Terry (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2018