Matching Items (324)
Description
This dissertation provides a fundamental understanding of the impact of bulk polymer properties on the nanometer length scale modulus. The elastic modulus of amorphous organic thin films is examined using a surface wrinkling technique. Potential correlations between thin film behavior and intrinsic properties such as flexibility and chain length are explored. Thermal properties, glass transition temperature (Tg) and the coefficient of thermal expansion, are examined along with the moduli of these thin films. It is found that the nanometer length scale behavior of flexible polymers correlates to its bulk Tg and not the polymers intrinsic size. It is also found that decreases in the modulus of ultrathin flexible films is not correlated with the observed Tg decrease in films of the same thickness. Techniques to circumvent reductions from bulk modulus were also demonstrated. However, as chain flexibility is reduced the modulus becomes thickness independent down to 10 nm. Similarly for this series minor reductions in Tg were obtained. To further understand the impact of the intrinsic size and processing conditions; this wrinkling instability was also utilized to determine the modulus of small organic electronic materials at various deposition conditions. Lastly, this wrinkling instability is exploited for development of poly furfuryl alcohol wrinkles. A two-step wrinkling process is developed via an acid catalyzed polymerization of a drop cast solution of furfuryl alcohol and photo acid generator. The ability to control the surface topology and tune the wrinkle wavelength with processing parameters such as substrate temperature and photo acid generator concentration is also demonstrated. Well-ordered linear, circular, and curvilinear patterns are also obtained by selective ultraviolet exposure and polymerization of the furfuryl alcohol film. As a carbon precursor a thorough understanding of this wrinkling instability can have applications in a wide variety of technologies.
ContributorsTorres, Jessica (Author) / Vogt, Bryan D (Thesis advisor) / Stafford, Christopher M (Committee member) / Richert, Ranko (Committee member) / Rege, Kaushal (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2011
Description
Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic for application in metallic and composite material systems. The primary means of interrogating a structure in this work is through the use of Lamb wave propagation which works well for the thin structures used in aerospace applications. Piezoelectric transducers (PZTs) were selected for this application since they can be used as both sensors and actuators of guided waves. Placement of these transducers is an important issue in wave based approaches as Lamb waves are sensitive to changes in material properties, geometry, and boundary conditions which may obscure the presence of damage if they are not taken into account during sensor placement. The placement scheme proposed in this dissertation arranges piezoelectric transducers in a pitch-catch mode so the entire structure can be covered using a minimum number of sensors. The stress distribution of the structure is also considered so PZTs are placed in regions where they do not fail before the host structure. In order to process the data from these transducers, advanced signal processing techniques are employed to detect the presence of damage in complex structures. To provide a better estimate of the damage for accurate life estimation, machine learning techniques are used to classify the type of damage in the structure. A data structure analysis approach is used to reduce the amount of data collected and increase computational efficiency. In the case of low velocity impact damage, fiber Bragg grating (FBG) sensors were used with a nonlinear regression tool to reconstruct the loading at the impact site.
ContributorsCoelho, Clyde (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Wu, Tong (Committee member) / Das, Santanu (Committee member) / Rajadas, John (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Ordered mesoporous materials have tunable pore sizes between 2 and 50 nm and are characterized by ordered pore structures and high surface areas (~1000 m2/g). This makes them particularly favorable for a number of membrane applications such as protein separation, polymer extrusion, nanowire fabrication and membrane reactors. These membranes can be fabricated as top-layers on macroporous supports or as embedded membranes in a dense matrix. The first part of the work deals with the hydrothermal synthesis and water-vapor/oxygen separation properties of supported MCM-48 and a new Al-MCM-48 type membrane for potential use in air conditioning systems. Knudsen-type permeation is observed in these membranes. The combined effect of capillary condensation and the aluminosilicate matrix resulted in the highest separation factor (142) in Al-MCM-48 membranes, with a water vapor permeance of 6×10-8mol/m2Pas. The second part focuses on synthesis of embedded mesoporous silica membranes with helically ordered pores by a novel Counter Diffusion Self-Assembly (CDSA) method. This method is an extension of the interfacial synthesis method for fiber synthesis using tetrabutylorthosilicate (TBOS) and cetyltrimethylammonium bromide (CTAB) as the silica source and surfactant respectively. The initial part of this study determined the effect of TBOS height and humidity on fiber formation. From this study, the range of TBOS heights for best microscopic and macroscopic ordering were established. Next, the CDSA method was used to successfully synthesize membranes, which were characterized to have good support plugging and an ordered pore structure. Factors that influence membrane synthesis and plug microstructure were determined. SEM studies revealed the presence of gaps between the plugs and support pores, which occur due to shrinking of the plug on drying. Development of a novel liquid deposition method to seal these defects constituted the last part of this work. Post sealing, excess silica was removed by etching with hydrofluoric acid. Membrane quality was evaluated at each step using SEM and gas permeation measurements. After surfactant removal by liquid extraction, the membranes exhibited an O2 permeance of 1.65x10-6mol/m2.Pa.s and He/O2 selectivity of 3.30. The successful synthesis of this membrane is an exciting new development in the area of ordered mesoporous membrane technology.
ContributorsSeshadri, Shriya (Author) / Lin, Jerry Y. S. (Thesis advisor) / Dai, Lenore (Committee member) / Rege, Kaushal (Committee member) / Smith, David J. (Committee member) / Vogt, Bryan (Committee member) / Arizona State University (Publisher)
Created2011
Description
A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Temporary bonding-debonding of flexible plastic substrates to rigid carriers may facilitate effective substrate handling by automated tools for manufacture of flexible microelectronics. The primary challenges in implementing practical temporary bond-debond technology originate from the stress that is developed during high temperature processing predominately through thermal-mechanical property mismatches between carrier, adhesive and substrate. These stresses are relaxed through bowing of the bonded system (substrate-adhesive-carrier), which causes wafer handling problems, or through delamination of substrate from rigid carrier. Another challenge inherent to flexible plastic substrates and linked to stress is their dimensional instability, which may manifest itself in irreversible deformation upon heating and cooling cycles. Dimensional stability is critical to ensure precise registration of different layers during photolithography. The global objective of this work is to determine comprehensive experimental characterization and develop underlying fundamental engineering concept that could enable widespread adoption and scale-up of temporary bonding processing protocols for flexible microelectronics manufacturing. A series of carriers with different coefficient of thermal expansion (CTE), modulus and thickness were investigated to correlate the thermo-mechanical properties of carrier with deformation behavior of bonded systems. The observed magnitude of system bow scaled with properties of carriers according to well-established Stoney's equation. In addition, rheology of adhesive impacted the deformation of bonded system. In particular, distortion-bowing behavior correlated directly with the relative loss factor of adhesive and flexible plastic substrate. Higher loss factor of adhesive compared to that of substrate allowed the stress to be relaxed with less bow, but led to significantly greater dimensional distortion. Conversely, lower loss factor of adhesive allowed less distortion but led to larger wafer bow. A finite element model using ANSYS was developed to predict the trend in bow-distortion of bonded systems as a function of the viscoelastic properties of adhesive. Inclusion of the viscoelasticity of flexible plastic substrate itself was critical to achieving good agreement between simulation and experiment. Simulation results showed that there is a limited range within which tuning the rheology of adhesive can control the stress-distortion. Therefore, this model can aid in design of new adhesive formulations compatible with different processing requirements of various flexible microelectronics applications.
ContributorsHaq, Jesmin (Author) / Raupp, Gregory B (Thesis advisor) / Vogt, Bryan D (Thesis advisor) / Dai, Lenore (Committee member) / Loy, Douglas (Committee member) / Li, Jian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Demands in file size and transfer rates for consumer-orientated products have escalated in recent times. This is primarily due to the emergence of high definition video content. Now factor in the consumer desire for convenience, and we find that wireless service is the most desired approach for inter-connectivity. Consumers expect wireless service to emulate wired service with little to virtually no difference in quality of service (QoS). The background section of this document examines the QoS requirements for wireless connectivity of high definition video applications. I then proceed to look at proposed solutions at the physical (PHY) and the media access control (MAC) layers as well as cross-layer schemes. These schemes are subsequently are evaluated in terms of usefulness in a multi-gigabit, 60 GHz wireless multimedia system targeting the average consumer. It is determined that a substantial gap in published literature exists pertinent to this application. Specifically, little or no work has been found that shows how an adaptive PHYMAC cross-layer solution that provides real-time compensation for varying channel conditions might be actually implemented. Further, no work has been found that shows results of such a model. This research proposes, develops and implements in Matlab code an alternate cross-layer solution that will provide acceptable QoS service for multimedia applications. Simulations using actual high definition video sequences are used to test the proposed solution. Results based on the average PSNR metric show that a quasi-adaptive algorithm provides greater than 7 dB of improvement over a non-adaptive approach while a fully-adaptive alogrithm provides over18 dB of improvement. The fully adaptive implementation has been conclusively shown to be superior to non-adaptive techniques and sufficiently superior to even quasi-adaptive algorithms.
ContributorsBosco, Bruce (Author) / Reisslein, Martin (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Underwater acoustic communications face significant challenges unprecedented in radio terrestrial communications including long multipath delay spreads, strong Doppler effects, and stringent bandwidth requirements. Recently, multi-carrier communications based on orthogonal frequency division multiplexing (OFDM) have seen significant growth in underwater acoustic (UWA) communications, thanks to their well well-known robustness against severely time-dispersive channels. However, the performance of OFDM systems over UWA channels significantly deteriorates due to severe intercarrier interference (ICI) resulting from rapid time variations of the channel. With the motivation of developing enabling techniques for OFDM over UWA channels, the major contributions of this thesis include (1) two effective frequencydomain equalizers that provide general means to counteract the ICI; (2) a family of multiple-resampling receiver designs dealing with distortions caused by user and/or path specific Doppler scaling effects; (3) proposal of using orthogonal frequency division multiple access (OFDMA) as an effective multiple access scheme for UWA communications; (4) the capacity evaluation for single-resampling versus multiple-resampling receiver designs. All of the proposed receiver designs have been verified both through simulations and emulations based on data collected in real-life UWA communications experiments. Particularly, the frequency domain equalizers are shown to be effective with significantly reduced pilot overhead and offer robustness against Doppler and timing estimation errors. The multiple-resampling designs, where each branch is tasked with the Doppler distortion of different paths and/or users, overcome the disadvantages of the commonly-used single-resampling receivers and yield significant performance gains. Multiple-resampling receivers are also demonstrated to be necessary for UWA OFDMA systems. The unique design effectively mitigates interuser interference (IUI), opening up the possibility to exploit advanced user subcarrier assignment schemes. Finally, the benefits of the multiple-resampling receivers are further demonstrated through channel capacity evaluation results.
ContributorsTu, Kai (Author) / Duman, Tolga M. (Thesis advisor) / Zhang, Junshan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011