Matching Items (506)
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
This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.
ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hydropower generation is one of the clean renewable energies which has received great attention in the power industry. Hydropower has been the leading source of renewable energy. It provides more than 86% of all electricity generated by renewable sources worldwide. Generally, the life span of a hydropower plant is considered as 30 to 50 years. Power plants over 30 years old usually conduct a feasibility study of rehabilitation on their entire facilities including infrastructure. By age 35, the forced outage rate increases by 10 percentage points compared to the previous year. Much longer outages occur in power plants older than 20 years. Consequently, the forced outage rate increases exponentially due to these longer outages. Although these long forced outages are not frequent, their impact is immense. If reasonable timing of rehabilitation is missed, an abrupt long-term outage could occur and additional unnecessary repairs and inefficiencies would follow. On the contrary, too early replacement might cause the waste of revenue. The hydropower plants of Korea Water Resources Corporation (hereafter K-water) are utilized for this study. Twenty-four K-water generators comprise the population for quantifying the reliability of each equipment. A facility in a hydropower plant is a repairable system because most failures can be fixed without replacing the entire facility. The fault data of each power plant are collected, within which only forced outage faults are considered as raw data for reliability analyses. The mean cumulative repair functions (MCF) of each facility are determined with the failure data tables, using Nelson's graph method. The power law model, a popular model for a repairable system, can also be obtained to represent representative equipment and system availability. The criterion-based analysis of HydroAmp is used to provide more accurate reliability of each power plant. Two case studies are presented to enhance the understanding of the availability of each power plant and represent economic evaluations for modernization. Also, equipment in a hydropower plant is categorized into two groups based on their reliability for determining modernization timing and their suitable replacement periods are obtained using simulation.
ContributorsKwon, Ogeuk (Author) / Holbert, Keith E. (Thesis advisor) / Heydt, Gerald T (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Group III-nitride semiconductor materials have been commercially used in fabrication of light-emitting diodes (LEDs) and laser diodes (LDs) covering the spectral range from UV to visible and infrared, and exhibit unique properties suitable for modern optoelectronic applications. Great advances have recently happened in the research and development in high-power and high-efficiency blue-green-white LEDs, blue LDs and other optoelectronic applications. However, there are still many unsolved challenges with these materials. In this dissertation, several issues concerning structural, electronic and optical properties of III-nitrides have been investigated using a combination of transmission electron microscopy (TEM), electron holography (EH) and cathodoluminescence (CL) techniques. First, a trend of indium chemical inhomogeneity has been found as the indium composition increases for the InGaN epitaxial layers grown by hydride vapor phase epitaxy. Second, different mechanisms contributing to the strain relaxation have been studied for non-polar InGaN epitaxial layers grown on zinc oxide (ZnO) substrate. Third, various structural morphologies of non-polar InGaN epitaxial layers grown on free-standing GaN substrate have been investigated. Fourth, the effect of the growth temperature on the performance of GaN lattice-matched InAlN electron blocking layers has been studied. Finally, the electronic and optical properties of GaN nanowires containing a AlN/GaN superlattice structure have been investigated showing relatively small internal electric field and superlattice- and defect-related emissions along the nanowires.
ContributorsSun, Kewei (Author) / Ponce, Fernando (Thesis advisor) / Smith, David (Committee member) / Treacy, Michael (Committee member) / Drucker, Jeffery (Committee member) / Schmidt, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized linear model (GLM) to simplify the computational process. A sensitivity study is also given to show the effects brought by parameters to the designs. Second, an extended version of I-optimal design for ALT is discussed and then a dual-objective design criterion is defined and showed with several examples. Also in order to evaluate different candidate designs, several graphical tools are developed. Finally, when there are more than one models available, different model checking designs are discussed.
ContributorsYang, Tao (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Borror, Connie (Committee member) / Rigdon, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this dissertation, remote plasma interactions with the surfaces of low-k interlayer dielectric (ILD), Cu and Cu adhesion layers are investigated. The first part of the study focuses on the simultaneous plasma treatment of ILD and chemical mechanical polishing (CMP) Cu surfaces using N2/H2 plasma processes. H atoms and radicals in the plasma react with the carbon groups leading to carbon removal for the ILD films. Results indicate that an N2 plasma forms an amide-like layer on the surface which apparently leads to reduced carbon abstraction from an H2 plasma process. In addition, FTIR spectra indicate the formation of hydroxyl (Si-OH) groups following the plasma exposure. Increased temperature (380 °C) processing leads to a reduction of the hydroxyl group formation compared to ambient temperature processes, resulting in reduced changes of the dielectric constant. For CMP Cu surfaces, the carbonate contamination was removed by an H2 plasma process at elevated temperature while the C-C and C-H contamination was removed by an N2 plasma process at elevated temperature. The second part of this study examined oxide stability and cleaning of Ru surfaces as well as consequent Cu film thermal stability with the Ru layers. The ~2 monolayer native Ru oxide was reduced after H-plasma processing. The thermal stability or islanding of the Cu film on the Ru substrate was characterized by in-situ XPS. After plasma cleaning of the Ru adhesion layer, the deposited Cu exhibited full coverage. In contrast, for Cu deposition on the Ru native oxide substrate, Cu islanding was detected and was described in terms of grain boundary grooving and surface and interface energies. The thermal stability of 7 nm Ti, Pt and Ru ii interfacial adhesion layers between a Cu film (10 nm) and a Ta barrier layer (4 nm) have been investigated in the third part. The barrier properties and interfacial stability have been evaluated by Rutherford backscattering spectrometry (RBS). Atomic force microscopy (AFM) was used to measure the surfaces before and after annealing, and all the surfaces are relatively smooth excluding islanding or de-wetting phenomena as a cause of the instability. The RBS showed no discernible diffusion across the adhesion layer/Ta and Ta/Si interfaces which provides a stable underlying layer. For a Ti interfacial layer RBS indicates that during 400 °C annealing Ti interdiffuses through the Cu film and accumulates at the surface. For the Pt/Cu system Pt interdiffuion is detected which is less evident than Ti. Among the three adhesion layer candidates, Ru shows negligible diffusion into the Cu film indicating thermal stability at 400 °C.
ContributorsLiu, Xin (Author) / Nemanich, Robert (Thesis advisor) / Chamberlin, Ralph (Committee member) / Chen, Tingyong (Committee member) / Smith, David (Committee member) / Ponce, Fernando (Committee member) / Arizona State University (Publisher)
Created2012
Description
The energy band gap of a semiconductor material critically influences the operating wavelength of an optoelectronic device. Realization of any desired band gap, or even spatially graded band gaps, is important for applications such as lasers, light-emitting diodes (LEDs), solar cells, and detectors. Compared to thin films, nanowires offer greater flexibility for achieving a variety of alloy compositions. Furthermore, the nanowire geometry permits simultaneous incorporation of a wide range of compositions on a single substrate. Such controllable alloy composition variation can be realized either within an individual nanowire or between distinct nanowires across a substrate. This dissertation explores the control of spatial composition variation in ternary alloy nanowires. Nanowires were grown by the vapor-liquid-solid (VLS) mechanism using chemical vapor deposition (CVD). The gas-phase supersaturation was considered in order to optimize the deposition morphology. Composition and structure were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy (EDS), and x-ray diffraction (XRD). Optical properties were investigated through photoluminescence (PL) measurements. The chalcogenides selected as alloy endpoints were lead sulfide (PbS), cadmium sulfide (CdS), and cadmium selenide (CdSe). Three growth modes of PbS were identified, which included contributions from spontaneously generated catalyst. The resulting wires were found capable of lasing with wavelengths over 4000 nm, representing the longest known wavelength from a sub-wavelength wire. For CdxPb1-xS nanowires, it was established that the cooling process significantly affects the alloy composition and structure. Quenching was critical to retain metastable alloys with x up to 0.14, representing a new composition in nanowire form. Alternatively, gradual cooling caused phase segregation, which created heterostructures with light emission in both the visible and mid-infrared regimes. The CdSSe alloy system was fully explored for spatial composition variation. CdSxSe1-x nanowires were grown with composition variation across the substrate. Subsequent contact printing preserved the designed composition gradient and led to the demonstration of a variable wavelength photodetector device. CdSSe axial heterostructure nanowires were also achieved. The growth process involved many variables, including a deliberate and controllable change in substrate temperature. As a result, both red and green light emission was detected from single nanowires.
ContributorsNichols, Patricia (Author) / Ning, Cun-Zheng (Thesis advisor) / Carpenter, Ray (Committee member) / Bennett, Peter (Committee member) / Smith, David (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
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
Of the potential technologies for pre-combustion capture, membranes offer the advantages of being temperature resistant, able to handle large flow rates, and having a relatively small footprint. A significant amount of research has centered on the use of polymeric and microporous inorganic membranes to separate CO2. These membranes, however, have limitations at high temperature resulting in poor permeation performance. To address these limitations, the use of a dense dual-phase membrane has been studied. These membranes are composed of conductive solid and conductive liquid phases that have the ability to selectively permeate CO2 by forming carbonate ions that diffuse through the membrane at high temperature. The driving force for transport through the membrane is a CO2 partial pressure gradient. The membrane provides a theoretically infinite selectivity. To address stability of the ceramic-carbonate dual-phase membrane for CO2 capture at high temperature, the ceramic phase of the membrane was studied and replaced with materials previously shown to be stable in harsh conditions. The permeation properties and stability of La0.6Sr0.4Co0.8Fe0.2O3-δ (LSCF)-carbonate, La0.85Ce0.1Ga0.3Fe0.65Al0.05O3-δ (LCGFA)-carbonate, and Ce0.8Sm0.2O1.9 (SDC)-carbonate membranes were examined under a wide range of experimental conditions at high temperature. LSCF-carbonate membranes were shown to be unstable without the presence of O2 due to reaction of CO2 with the ceramic phase. In the presence of O2, however, the membranes showed stable permeation behavior for more than one month at 900oC. LCGFA-carbonate membranes showed great chemical and permeation stability in the presence of various conditions including exposure to CH4 and H2, however, the permeation performance was quite low when compared to membranes in the literature. Finally, SDC-carbonate membranes showed great chemical and permeation stability both in a CO2:N2 environment for more than two weeks at 900oC as well as more than one month of exposure to simulated syngas conditions at 700oC. Ceramic phase chemical stability increased in the order of LSCF < LCGFA < SDC while permeation performance increased in the order of LCGFA < LSCF < SDC.
ContributorsNorton, Tyler (Author) / Lin, Jerry Y.S. (Thesis advisor) / Alford, Terry (Committee member) / Lind, Mary Laura (Committee member) / Smith, David (Committee member) / Torres, Cesar (Committee member) / Arizona State University (Publisher)
Created2013