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- All Subjects: engineering
- Creators: Sugar, Thomas
- Creators: Jiao, Yang
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Description
For decades, microelectronics manufacturing has been concerned with failures related to electromigration phenomena in conductors experiencing high current densities. The influence of interconnect microstructure on device failures related to electromigration in BGA and flip chip solder interconnects has become a significant interest with reduced individual solder interconnect volumes. A survey indicates that x-ray computed micro-tomography (µXCT) is an emerging, novel means for characterizing the microstructures' role in governing electromigration failures. This work details the design and construction of a lab-scale µXCT system to characterize electromigration in the Sn-0.7Cu lead-free solder system by leveraging in situ imaging.
In order to enhance the attenuation contrast observed in multi-phase material systems, a modeling approach has been developed to predict settings for the controllable imaging parameters which yield relatively high detection rates over the range of x-ray energies for which maximum attenuation contrast is expected in the polychromatic x-ray imaging system. In order to develop this predictive tool, a model has been constructed for the Bremsstrahlung spectrum of an x-ray tube, and calculations for the detector's efficiency over the relevant range of x-ray energies have been made, and the product of emitted and detected spectra has been used to calculate the effective x-ray imaging spectrum. An approach has also been established for filtering `zinger' noise in x-ray radiographs, which has proven problematic at high x-ray energies used for solder imaging. The performance of this filter has been compared with a known existing method and the results indicate a significant increase in the accuracy of zinger filtered radiographs.
The obtained results indicate the conception of a powerful means for the study of failure causing processes in solder systems used as interconnects in microelectronic packaging devices. These results include the volumetric quantification of parameters which are indicative of both electromigration tolerance of solders and the dominant mechanisms for atomic migration in response to current stressing. This work is aimed to further the community's understanding of failure-causing electromigration processes in industrially relevant material systems for microelectronic interconnect applications and to advance the capability of available characterization techniques for their interrogation.
In order to enhance the attenuation contrast observed in multi-phase material systems, a modeling approach has been developed to predict settings for the controllable imaging parameters which yield relatively high detection rates over the range of x-ray energies for which maximum attenuation contrast is expected in the polychromatic x-ray imaging system. In order to develop this predictive tool, a model has been constructed for the Bremsstrahlung spectrum of an x-ray tube, and calculations for the detector's efficiency over the relevant range of x-ray energies have been made, and the product of emitted and detected spectra has been used to calculate the effective x-ray imaging spectrum. An approach has also been established for filtering `zinger' noise in x-ray radiographs, which has proven problematic at high x-ray energies used for solder imaging. The performance of this filter has been compared with a known existing method and the results indicate a significant increase in the accuracy of zinger filtered radiographs.
The obtained results indicate the conception of a powerful means for the study of failure causing processes in solder systems used as interconnects in microelectronic packaging devices. These results include the volumetric quantification of parameters which are indicative of both electromigration tolerance of solders and the dominant mechanisms for atomic migration in response to current stressing. This work is aimed to further the community's understanding of failure-causing electromigration processes in industrially relevant material systems for microelectronic interconnect applications and to advance the capability of available characterization techniques for their interrogation.
ContributorsMertens, James Charles Edwin (Author) / Chawla, Nikhilesh (Thesis advisor) / Alford, Terry (Committee member) / Jiao, Yang (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this dissertation, the results of our comprehensive computational studies of disordered jammed (i.e., mechanically stable) packings of hard particles are presented, including the family of superdisks in 2D and ellipsoids in 3D Euclidean space. Following a very brief introduction to the hard-particle systems, the event driven molecular dynamics (EDMD) employed to generate the packing ensembles will be discussed. A large number of 2D packing configurations of superdisks are subsequently analyzed, through which a relatively accurate theoretical scheme for packing-fraction prediction based on local particle contact configurations is proposed and validated via additional numerical simulations. Moreover, the studies on binary ellipsoid packing in 3D are briefly discussed and the effects of different geometrical parameters on the final packing fraction are analyzed.
ContributorsXu, Yaopengxiao (Author) / Jiao, Yang (Thesis advisor) / Oswald, Jay (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2014
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a brief review is made about these three material systems. In Chapter 2, detailed discussion of the statistical morphological descriptors and a stochastic optimization approach for microstructure reconstruction is presented. In Chapter 3, the lattice particle method for micromechanical analysis of complex heterogeneous materials is introduced. In Chapter 4, a new class of hyperuniform heterogeneous material with superior mechanical properties is investigated. In Chapter 5, a bio-material system, i.e., cellularized collagen gel is modeled using correlation functions and stochastic reconstruction to study the collective dynamic behavior of the embed tumor cells. In chapter 6, LMPA soft robotic system is generated by generalizing the correlation functions and the rigidity tunability of this smart composite is discussed. In Chapter 7, a future work plan is presented.
ContributorsXu, Yaopengxiao (Author) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Wang, Qing Hua (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Increasing density of microelectronic packages, results in an increase in thermal and mechanical stresses within the various layers of the package. To accommodate the high-performance demands, the materials used in the electronic package would also require improvement. Specifically, the damage that often occurs in solders that function as die-attachment and thermal interfaces need to be addressed. This work evaluates and characterizes thermo-mechanical damage in two material systems – Electroplated Tin and Sintered Nano-Silver solder.
Tin plated electrical contacts are prone to formation of single crystalline tin whiskers which can cause short circuiting. A mechanistic model of their formation, evolution and microstructural influence is still not fully understood. In this work, growth of mechanically induced tin whiskers/hillocks is studied using in situ Nano-indentation and Electron Backscatter Diffraction (EBSD). Electroplated tin was indented and monitored in vacuum to study growth of hillocks without the influence of atmosphere. Thermal aging was done to study the effect of intermetallic compounds. Grain orientation of the hillocks and the plastically deformed region surrounding the indent was studied using Focused Ion Beam (FIB) lift-out technique. In addition, micropillars were milled on the surface of electroplated Sn using FIB to evaluate the yield strength and its relation to Sn grain size.
High operating temperature power electronics use wide band-gap semiconductor devices (Silicon Carbide/Gallium Nitride). The operating temperature of these devices can exceed 250oC, preventing use of traditional Sn-solders as Thermal Interface materials (TIM). At high temperature, the thermomechanical stresses can severely degrade the reliability and life of the device. In this light, new non-destructive approach is needed to understand the damage mechanism when subjected to reliability tests such as thermal cycling. In this work, sintered nano-Silver was identified as a promising high temperature TIM. Sintered nano-Silver samples were fabricated and their shear strength was evaluated. Thermal cycling tests were conducted and damage evolution was characterized using a lab scale 3D X-ray system to periodically assess changes in the microstructure such as cracks, voids, and porosity in the TIM layer. The evolution of microstructure and the effect of cycling temperature during thermal cycling are discussed.
Tin plated electrical contacts are prone to formation of single crystalline tin whiskers which can cause short circuiting. A mechanistic model of their formation, evolution and microstructural influence is still not fully understood. In this work, growth of mechanically induced tin whiskers/hillocks is studied using in situ Nano-indentation and Electron Backscatter Diffraction (EBSD). Electroplated tin was indented and monitored in vacuum to study growth of hillocks without the influence of atmosphere. Thermal aging was done to study the effect of intermetallic compounds. Grain orientation of the hillocks and the plastically deformed region surrounding the indent was studied using Focused Ion Beam (FIB) lift-out technique. In addition, micropillars were milled on the surface of electroplated Sn using FIB to evaluate the yield strength and its relation to Sn grain size.
High operating temperature power electronics use wide band-gap semiconductor devices (Silicon Carbide/Gallium Nitride). The operating temperature of these devices can exceed 250oC, preventing use of traditional Sn-solders as Thermal Interface materials (TIM). At high temperature, the thermomechanical stresses can severely degrade the reliability and life of the device. In this light, new non-destructive approach is needed to understand the damage mechanism when subjected to reliability tests such as thermal cycling. In this work, sintered nano-Silver was identified as a promising high temperature TIM. Sintered nano-Silver samples were fabricated and their shear strength was evaluated. Thermal cycling tests were conducted and damage evolution was characterized using a lab scale 3D X-ray system to periodically assess changes in the microstructure such as cracks, voids, and porosity in the TIM layer. The evolution of microstructure and the effect of cycling temperature during thermal cycling are discussed.
ContributorsLujan Regalado, Irene (Author) / Chawla, Nikhilesh (Thesis advisor) / Frear, Darrel (Committee member) / Rajagopalan, Jagannathan (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2018
Description
Nanolaminate materials are layered composites with layer thickness ≤ 100 nm. They exhibit unique properties due to their small length scale, the presence of a high number of interfaces and the effect of imposed constraint. This thesis focuses on the mechanical behavior of Al/SiC nanolaminates. The high strength of ceramics combined with the ductility of Al makes this combination desirable. Al/SiC nanolaminates were synthesized through magnetron sputtering and have an overall thickness of ~ 20 μm which limits the characterization techniques to microscale testing methods. A large amount of work has already been done towards evaluating their mechanical properties under indentation loading and micropillar compression. The effects of temperature, orientation and layer thickness have been well established. Al/SiC nanolaminates exhibited a flaw dependent deformation, anisotropy with respect to loading direction and strengthening due to imposed constraint. However, the mechanical behavior of nanolaminates under tension and fatigue loading has not yet been studied which is critical for obtaining a complete understanding of their deformation behavior. This thesis fills this gap and presents experiments which were conducted to gain an insight into the behavior of nanolaminates under tensile and cyclic loading. The effect of layer thickness, tension-compression asymmetry and effect of a wavy microstructure on mechanical response have been presented. Further, results on in situ micropillar compression using lab-based X-ray microscope through novel experimental design are also presented. This was the first time when a resolution of 50 nms was achieved during in situ micropillar compression in a lab-based setup. Pores present in the microstructure were characterized in 3D and sites of damage initiation were correlated with the channel of pores present in the microstructure.
The understanding of these deformation mechanisms paved way for the development of co-sputtered Al/SiC composites. For these composites, Al and SiC were sputtered together in a layer. The effect of change in the atomic fraction of SiC on the microstructure and mechanical properties were evaluated. Extensive microstructural characterization was performed at the nanoscale level and Al nanocrystalline aggregates were observed dispersed in an amorphous matrix. The modulus and hardness of co- sputtered composites were much higher than their traditional counterparts owing to denser atomic packing and the absence of synthesis induced defects such as pores and columnar boundaries.
The understanding of these deformation mechanisms paved way for the development of co-sputtered Al/SiC composites. For these composites, Al and SiC were sputtered together in a layer. The effect of change in the atomic fraction of SiC on the microstructure and mechanical properties were evaluated. Extensive microstructural characterization was performed at the nanoscale level and Al nanocrystalline aggregates were observed dispersed in an amorphous matrix. The modulus and hardness of co- sputtered composites were much higher than their traditional counterparts owing to denser atomic packing and the absence of synthesis induced defects such as pores and columnar boundaries.
ContributorsSingh, Somya (Author) / Chawla, Nikhilesh (Thesis advisor) / Neithalath, Narayanan (Committee member) / Jiao, Yang (Committee member) / Mara, Nathan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Engineering is a multidisciplinary field with a variety of applications. However, since there are so many disciplines of engineering, it is often challenging to find the discipline that best suits an individual interested in engineering. Not knowing which area of engineering most aligns to one’s interests is challenging when deciding on a major and a career. With the development of the Engineering Interest Quiz (EIQ), the goal was to help individuals find the field of engineering that is most similar to their interests. Initially, an Engineering Faculty Survey (EFS) was created to gather information from engineering faculty at Arizona State University (ASU) and to determine keywords that describe each field of engineering. With this list of keywords, the EIQ was developed. Data from the EIQ compared the engineering students’ top three results for the best engineering discipline for them with their current engineering major of study. The data analysis showed that 70% of the respondents had their major listed as one of the top three results they were given and 30% of the respondents did not have their major listed. Of that 70%, 64% had their current major listed as the highest or tied for the highest percentage and 36% had their major listed as the second or third highest percentage. Furthermore, the EIQ data was compared between genders. Only 33% of the male students had their current major listed as their highest percentage, but 55% had their major as one of their top three results. Women had higher percentages with 63% listing their current major as their highest percentage and 81% listing it in the top three of their final results.
ContributorsWagner, Avery Rose (Co-author) / Lucca, Claudia (Co-author) / Taylor, David (Thesis director) / Miller, Cindy (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Hydrocephalus is a chronic medical condition characterized by the excessive accumulation of cerebrospinal fluid in the brain. It is estimated that 1-2 of every 1000 babies in the United States is born with congenital hydrocephalus, with many individuals acquiring hydrocephalus later in life through brain injury. Despite these alarming statistics, current shunts for the treatment of hydrocephalus display operational failure rates as high as 40-50% within two years following implantation. Failure of current shunts is attributed to complexity of design, external implantation, and the requirement of multiple catheters. The presented hydrogel wafer check valve avoids all the debilitating features of current shunts, relying only on the swelling of hydrogel for operation, and is designed to directly replace failed arachnoid granulations- the brain’s natural cerebrospinal fluid drainage valves. The valve was validated via bench-top (1) hydrodynamic pressure-flow response characterizations, (2) transient response analysis, and (3) overtime performance response in brain-analogous conditions. In-vitro measurements display operation in range of natural CSF draining (cracking pressure, PT ~ 1–110 mmH2O and outflow hydraulic resistance, Rh ~ 24 – 152 mmH2O/mL/min), negligible reverse flow leakages (flow, QO > -10 µL/min), and demonstrate the valve’s operational reproducibility of this new valve as an implantable treatment.
ContributorsAmjad, Usamma Muhammad (Author) / Chae, Junseok (Thesis director) / Appel, Jennie (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05