Browsing by Subject "Defect"
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Item Automatic semiconductor wafer map defect signature detection using a neural network classifier(2010-12) Radhamohan, Ranjan Subbaraya; Ghosh, Joydeep; El-Hamdi, MohamedThe application of popular image processing and classification algorithms, including agglomerative clustering and neural networks, is explored for the purpose of grouping semiconductor wafer defect map patterns. Challenges such as overlapping pattern separation, wafer rotation, and false data removal are examined and solutions proposed. After grouping, wafer processing history is used to automatically determine the most likely source of the issue. Results are provided that indicate these methods hold promise for wafer analysis applications.Item Design of magneto-inductive waveguide for sensing applications(2014-05) Chen, Ye, 1986-; Neikirk, Dean P., 1957-This dissertation has been motivated by the increasing application of sensing technologies in structural health monitoring. Many wireless sensor techniques exist for structural health monitoring while a challenge faced is the finite lifetime of batteries. The objective of this dissertation is to develop passive wireless technology to provide early warning of conditions that damage the structure. In this dissertation, sensing mechanism is proposed based on time and frequency domain characteristics of magneto-inductive (MI) waves. Experimental results are also presented to demonstrate the sensing mechanism. MI waves are predominantly magnetic waves that are supported in periodic arrays of magnetically coupled resonators and propagate within a narrow frequency band around the resonant frequency. The array is to be embedded in a structure and different types of transducers can be integrated for different sensing applications. With the onset of structure defect, the transducer introduces an impedance discontinuity that generates reflected MI waves along the array, which are monitored and processed by Smoothed Wigner-Ville distribution (WVD) to extract time-of-flight for frequency components in the narrow passband. The transmission and reflection coefficients of MI waves are also investigated based on the lumped-element circuit model of the array. Based on MI waves travel time, amplitude and group velocity, the position and severity of structure defect are decided. The sensing mechanisms for different distribution of defects are proposed. The validity of the sensing mechanism is examined in experiments. The guided wave testing is implemented in one-dimensional square-shaped printed spiral resonators with Q-factor of 161 at 13.6 MHz. It demonstrates that low MI waves propagation loss is achieved with value of 0.098 dB per element at mid-band with center-to-center distance of half an inch. A pitch-catch measurement system is built to capture traveling MI signal in resonant element and extract group velocity, and a pulse-echo measurement system is designed to monitor reflected MI signal and locate structure discontinuity. In both measurement systems, MI waves are excited with wide bandwidth voltage pulse, and a digitizer is attached to sense the MI signal in a specific resonant element circuit. A baseline signal is obtained from the healthy state to use as reference and comparison with the test case using pitch-catch system. The test signal subtracted from baseline signal infers the structure damage information with time and frequency domain characteristics. It can offer an effective method to estimate the structure discontinuity location, severity and type of damage. The experimental results are consistent with the theoretical predictions. At the end, future directions for the research to integrate with other technologies are suggested.Item Resistance training as a modality to enhance muscle regeneration in a rat skeletal muscle defect(2009-12) Taylor, Daniel Ryan; Farrar, Roger P.; Suggs, LauraTraumatic skeletal muscle injuries that include loss of large amounts of muscle mass are becoming more common in today’s warfare. Traditional treatments often do not prevent long term functional impairments. Using a decellularized extracellular matrix (ECM) as scaffolding to replace lost muscle tissue allows for transmission of force through the injury site, and provides a suitable microenvironment receptive to myofiber growth. Seeding the ECM with progenitor cells improves cellular content in the defect area. Exercise exposes the muscle to improved blood flow as well as higher than normal loading. This results in increased blood vessel density as well as higher levels of cellular content, and near complete restoration of function.Item Software defect data - predictability and exploration(2006-12) Kulkarni, Aniruddha P.; Hewett, Rattikorn; Shin, Michael; Denton, JasonSoftware defect reports have been prominently used in reliability modeling. Data about the defects found during software testing is recorded in software defect reports or bug reports. The data consists of defect information including defect number at various testing stages, complexity (of the defect), severity, information about the component to which the defect belongs, tester, and person fixing the defect. Reliability models mainly use data about the number of defects and its corresponding time to predict the remaining number of defects. This thesis proposes an empirical approach to systematically elucidate useful information from software defect reports by (1) employing a data exploration technique that analyzes relationships between software quality of different releases using appropriate statistics, and (2) constructing predictive models for forecasting time for fixing defects using existing machine learning and data mining techniques. This work differs from traditional software reliability in two ways. First, it aims to predict time for fixing defects, as opposed to the remaining number of defects. While the latter gives a useful measure of software quality, in practice it cannot be used directly for development planning since defect number is not linear with respect to time and resources required. On the contrary, prediction of the time for fixing defects can be used directly to help schedule and manage software activities. Second, while reliability models are mainly based on a small number of attributes of defect data with numerical attribute values, the proposed approach extends use of defect data to include more relevant attributes whose values can be both quantitative and qualitative. To illustrate the approach, we present an empirical study on a software defect report collected during the testing of a large medical software system. For data exploration, we use defects found per component and investigate relationships between defects in modules before and after release. For building predictive models, we apply various well-established machine learning and data mining algorithms including the decision tree learner, the Naive Bayes learner and neural networks with back propagation learning. The average results obtained from these algorithms are compared and also to illustrate the robustness of the proposed approach to predict time for fixing defects. The results obtained are promising with the top performance model having an average accuracy of 93.5%.Item Yield-reliability modeling: application to large processors(Texas Tech University, 2005-12) Wilde, Jason A.This thesis describes an experiment that was performed on a large processor with embedded static random access memory (SRAM) with built-in redundancy, manufactured using modern very large scale integration (VLSI) technologies. This experiment will serve to prove the hypothesis that redundant SRAM on a large processor can be used for reliability modeling not only for the SRAM, but also for the functional areas of the device. In order to apply the results of this experiment to production methodology, an existing model that can give the probability of a device failing after it has been repaired using redundant SRAM will be presented and verified. This model can serve as a filter for devices that enter reliability testing, which is costly and time consuming.Item Yield-reliability modeling: Application to large processors(2005-12) Wilde, Jason A.; Parten, Michael E.; Nutter, BrianThis thesis describes an experiment that was performed on a large processor with embedded static random access memory (SRAM) with built-in redundancy, manufactured using modern very large scale integration (VLSI) technologies. This experiment will serve to prove the hypothesis that redundant SRAM on a large processor can be used for reliability modeling not only for the SRAM, but also for the functional areas of the device. In order to apply the results of this experiment to production methodology, an existing model that can give the probability of a device failing after it has been repaired using redundant SRAM will be presented and verified. This model can serve as a filter for devices that enter reliability testing, which is costly and time consuming.