Semiconductor manufacturers must ensure that chips conform to their specifications before they are shipped to customers. This is achieved by testing various parameters of a chip to determine whether it is defective or not. Separating defective chips from fault-free ones is relatively straightforward for functional or other Boolean tests that produce a go/no-go type of result. However, making this distinction is extremely challenging for parametric tests. Owing to continuous distributions of parameters, any pass/fail threshold results in yield loss and/or test escapes. The continuous advances in process technology, increased process variations and inaccurate fault models all make this even worse. The pass/fail thresholds for such tests are usually set using prior experience or by a combination of visual inspection and engineering judgment. Many chips have parameters that exceed certain thresholds but pass Boolean tests. Owing to the imperfect nature of tests, to determine whether these chips (called "outliers") are indeed defective is nontrivial. To avoid wasted investment in packaging or further testing it is important to screen defective chips early in a test flow. Moreover, if seemingly strange behavior of outlier chips can be explained with the help of certain process parameters or by correlating additional test data, such chips can be retained in the test flow before they are proved to be fatally flawed. In this research, we investigate several methods to identify true outliers (defective chips, or chips that lead to functional failure) from apparent outliers (seemingly defective, but fault-free chips). The outlier identification methods in this research primarily rely on wafer-level spatial correlation, but also use additional test parameters. These methods are evaluated and validated using industrial test data. The potential of these methods to reduce burn-in is discussed.

Semiconductor manufacturers must ensure that chips conform to their specifications before they are shipped to customers. This is achieved by testing various parameters of a chip to determine whether it is defective or not. Separating defective chips from fault-free ones is relatively straightforward for functional or other Boolean tests that produce a go/no-go type of result. However, making this distinction is extremely challenging for parametric tests. Owing to continuous distributions of parameters, any pass/fail threshold results in yield loss and/or test escapes. The continuous advances in process technology, increased process variations and inaccurate fault models all make this even worse. The pass/fail thresholds for such tests are usually set using prior experience or by a combination of visual inspection and engineering judgment. Many chips have parameters that exceed certain thresholds but pass Boolean tests. Owing to the imperfect nature of tests, to determine whether these chips (called "outliers") are indeed defective is nontrivial. To avoid wasted investment in packaging or further testing it is important to screen defective chips early in a test flow. Moreover, if seemingly strange behavior of outlier chips can be explained with the help of certain process parameters or by correlating additional test data, such chips can be retained in the test flow before they are proved to be fatally flawed. In this research, we investigate several methods to identify true outliers (defective chips, or chips that lead to functional failure) from apparent outliers (seemingly defective, but fault-free chips). The outlier identification methods in this research primarily rely on wafer-level spatial correlation, but also use additional test parameters. These methods are evaluated and validated using industrial test data. The potential of these methods to reduce burn-in is discussed.

The elderly population have developed some resistance toward accepting community-based social services. Such resistance could be detrimental to the well-being of low-income seniors by hindering their chance of accessing services intended for them in the first place. Art therapy is seen as a promising intervention against client resistance. This study evaluated the effectiveness of art therapy on reducing resistance to services among low-income seniors living in independent living communities. Through a pre-experimental design, this study analyzed administrative data for a sample of 37 participants from a social service agency in Southern California. Results from two non-parametric tests (WilcoxonSigned-Rank and Mann-Whitney-U) revealed that art therapy is very effective in reducing resistance among seniors. Implications for gerontology and social services providers were discussed.

This study computed trends in extreme precipitation events of Florida for 1950-2010. Hourly aggregated rainfall data from 24 stations of the National Climatic Data Centre were analyzed to derive time-series of extreme rainfalls for 12 durations, ranging from 1 hour to 7 day. Non-parametric Mann-Kendall test and Theil-Sen Approach were applied to detect the significance of trends in annual maximum rainfalls, number of above threshold events and average magnitude of above threshold events for four common analysis periods. Trend Free Pre-Whitening (TFPW) approach was applied to remove the serial correlations and bootstrap resampling approach was used to detect the field significance of trends. The results for annual maximum rainfall revealed dominant increasing trends at the statistical significance level of 0.10, especially for hourly events in longer period and daily events in recent period. The number of above threshold events exhibited strong decreasing trends for hourly durations in all time periods.

This thesis deals with questions which are related to the creation of case studies for statistical data analysis using applied computer technology. The main aim is focused on showing the solution of statistical case studies in the field of electrical engineering. Solved case studies include task, exemplary solution and conclusion. Clarity of explained theory and the results understanding and interpretation is accentuated. This thesis can be used for practical education of applied statistical methods, it’s also supplemented with commented outputs from Minitab. Trial version of Minitab has been used for solution of case studies.

An arterial traffic control system influences the travel time characteristics of a corridor, including the average corridor travel time and the travel time reliability. However, reliability measures have typically been outside of the focus of arterial control system performance evaluation studies. To assess the effectiveness of arterial traffic control performance evaluation studies are normally limited to average measures of travel time, speed, or delay. As an advanced traffic management system, adaptive traffic control has been developed to address real time demand variability. Thus, an evaluation of the adaptive traffic control system based on reliability may be as important as evaluation based on average travel time or delay. In addition, arterial control systems may also affect the performance of side street traffic as well as arterial corridor traffic. The performance of side street traffic is another measure that should be used in the assessment of the effectiveness of any arterial traffic control system. Finally, an arterial's operational performance often changes throughout a day and over the arterial length. Thus, a system-wide measure that reflects the range of observed operations is needed to thoroughly assess the performance. Given these issues the goal of this research is the development of procedures to evaluate adaptive traffic control's effect on arterial characteristics such as travel time distribution, reliability, side street performance, and system-wide performance. The developed procedures were applied to the evaluation of an adaptive traffic control system, SCATS (Sydney Coordinated Adaptive Traffic System) in Cobb County, Georgia that replaced a semi-actuated coordinated control system. After the procedures were applied, it was found that SCATS produced a less extreme shape of travel time distribution, possibly due to the adaptive feature, but that it did not make statistically significant changes in the selected overall analysis measures. Also, it was found that the results of the performance evaluation can vary depending on the measures selected or the study period and location.

Human pleasant touches promote feelings of security, supportiveness, and wellbeing. Conversely, human unpleasant touches promote the body for either “fight or flight” or “short term acute stress” during emergencies, feeling of stress or danger. The promoted stress response is released from the hypothalamus by the sympathetic nerve system further to the spinal cord to reach the signals to the adrenal medulla, where stress hormones adrenaline is released. Adrenaline, which is characterized by a mimic sympathetic nerve system, interacts with α and β receptors on different organs. The aim for this study was to investigate whether the stroker (partner/stranger) touch effects on adrenaline hormone releases. The null hypothesis for this study entails a significant adrenaline reduction in partners’ touches compared with strangers’ touches. Indirect competitive ELISA method was used, and concentration data of a total of sixteen participants was obtained. Whitney-U test was carried out to compare group differences within stroker (stranger/partner) touches and adrenaline releasing level. In addition, correlation in adrenaline with noradrenaline and oxytocin hormones was obtained using Spearman’s correlation test. The significant p-value 0.05 was conducted. The result of this study showed no differences between stroker (partner/stranger) associated with adrenaline hormone release. Correlation between partner maximum (max) concentration data for both oxytocin and adrenaline had significant differences. However, max variables for adrenaline and noradrenaline within stroker did not show significant differences. The conclusion of this study is that the gentle touch stimulus used in this study was not enough to detect stress hormone in adrenaline.

The unique problems encountered when analyzing weather data sets - that is, measurements taken while conducting a meteorological experiment- have forced statisticians to reconsider the conventional analysis methods and investigate permutation test procedures. The problems encountered when analyzing weather data sets are simulated for a Monte Carlo study, and the results of the parametric and permutation t-tests are compared with regard to significance level, power, and the average coilfidence interval length. Seven population distributions are considered - three are variations of the normal distribution, and the others the gamma, the lognormal, the rectangular and empirical distributions. The normal distribution contaminated with zero measurements is also simulated. In those simulated situations in which the variances are unequal, the permutation test procedure was performed using other test statistics, namely the Scheffe, Welch and Behrens-Fisher test statistics. / Mathematical Sciences / M. Sc. (Statistics)

The unique problems encountered when analyzing weather data sets - that is, measurements taken while conducting a meteorological experiment- have forced statisticians to reconsider the conventional analysis methods and investigate permutation test procedures. The problems encountered when analyzing weather data sets are simulated for a Monte Carlo study, and the results of the parametric and permutation t-tests are compared with regard to significance level, power, and the average coilfidence interval length. Seven population distributions are considered - three are variations of the normal distribution, and the others the gamma, the lognormal, the rectangular and empirical distributions. The normal distribution contaminated with zero measurements is also simulated. In those simulated situations in which the variances are unequal, the permutation test procedure was performed using other test statistics, namely the Scheffe, Welch and Behrens-Fisher test statistics. / Mathematical Sciences / M. Sc. (Statistics)

Aujourd'hui, le contrôle des procédés de fabrication est une tâche essentielle pour assurer une production de haute qualité. A la fin du processus de fabrication du semi-conducteur, un test électrique, appelé test paramétrique (PT), est effectuée. PT vise à détecter les plaques dont le comportement électrique est anormal, en se basant sur un ensemble de paramètres électriques statiques mesurées sur plusieurs sites de chaque plaque. Le but de ce travail est de mettre en place un système de détection dynamique au niveau de PT, pour détecter les plaques anormales à partir d'un historique récent de mesures électriques. Pour cela, nous développons un système de détection en temps réel basé sur une technique de réapprentissage optimisée, où les données d'apprentissage et le modèle de détection sont mis à jour à travers une fenêtre temporelle glissante. Le modèle de détection est basé sur les machines à vecteurs supports à une classe (1-SVM), une variante de l'algorithme d'apprentissage statistique SVM largement utilisé pour la classification binaire. 1-SVM a été introduit dans le cadre des problèmes de classification à une classe pour la détection des anomalies. Pour améliorer la performance prédictive de l'algorithme de classification 1-SVM, deux méthodes de sélection de variables ont été développées. La première méthode de type filtrage est basé sur un score calculé avec le filtre MADe,une approche robuste pour la détection univariée des valeurs aberrantes. La deuxième méthode de type wrapper est une adaptation à l'algorithme 1-SVM de la méthode d'élimination récursive des variables avec SVM (SVM-RFE). Pour les plaques anormales détectées, nous proposons une méthode permettant de déterminer leurs signatures multidimensionnelles afin d'identifier les paramètres électriques responsables de l'anomalie. Finalement, nous évaluons notre système proposé sur des jeux de données réels de STMicroelecronics, et nous le comparons au système de détection basé sur le test de T2 de Hotelling, un des systèmes de détection les plus connus dans la littérature. Les résultats obtenus montrent que notre système est performant et peut fournir un moyen efficient pour la détection en temps réel. / Nowadays, control of manufacturing process is an essential task to ensure production of high quality. At the end of the semiconductor manufacturing process, an electric test, called Parametric Test (PT), is performed. The PT aims at detecting wafers whose electrical behavior is abnormal, based on a set of static electrical parameters measured on multiple sites of each wafer. The purpose of this thesis is to develop a dynamic detection system at PT level to detect abnormal wafers from a recent history of electrical measurements. For this, we develop a real time detection system based on an optimized learning technique, where training data and detection model are updated through a moving temporal window. The detection scheme is based on one class Support Vector Machines (1-SVM), a variant of the statistical learning algorithm SVM widely used for binary classification. 1-SVM was introduced in the context of one class classification problems for anomaly detection. In order to improve the predictive performance of the 1-SVM classification algorithm, two variable selection methods are developed. The first one is a filter method based on a calculated score with MADe filter, a robust approach for univariate outlier detection. The second one is of wrapper type that adapts the SVM Recursive Feature Elimination method (SVM-RFE) to the 1-SVM algorithm. For detected abnormal wafers, we propose a method to determine their multidimensional signatures to identify the electrical parameters responsible for the anomaly. Finally, we evaluate our proposed system on real datasets of STMicroelecronics and compare it to the detection system based on Hotelling's T2 test, one of the most known detection systems in the literature. The results show that our system yields very good performance and can provide an efficient way for real-time detection.