Browsing by Subject "Spatial correlation"
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Item Determination of the proper site spacing density over Texas(2005-08) Sonmez, Ibrahim; Schroeder, John L.; Peterson, Richard E.; Smith, Douglas A.The current meteorological surface observation network in Texas only monitors one out of every five counties. The observation sites are nominally spaced 150-200 km apart and report hourly measurements. For this reason, the current system is incapable of providing observations at a spatial and temporal resolution to document mesoscale weather features and provide short-range forecasting. To overcome this problem, various site spacing procedures are used to propose to fill the gaps in the monitoring system. In this study, site spacing determination procedures based on correlation level, power spectrum, and true field error variance are used to analyze the temperature, dew point temperature, wind speed and pressure parameters over Texas. Hourly observations from 126 surface observing sites located in Texas and the adjacent states with a data coverage period varying from 6 to 21 years are considered for the study. The existence of isotropic conditions over the domain is tested by examining the spatial correlation variations of the parameters. The existence of anisotropic conditions for each parameter resulted in the search for sub-regions within the domain. Cluster analysis indicated three separate clusters for the domain. For each cluster, the spatial correlation variations are examined and spectral analysis is applied to determine the governing scales for each parameter’s variation. Error amounts in obtaining the true Fourier coefficients are analyzed; the highest wave number that can reasonably estimated is determined for each parameter in each cluster. Finally, the error variance in determining the true field for each parameter is examined for site spacings of 200, 150, 100, 50, and 25 km for each cluster.Item Determination of the proper site spacing density over Texas(Texas Tech University, 2005-08) Sonmez, Ibrahim; Schroeder, John L.; Peterson, Richard E.; Smith, Douglas A.The current meteorological surface observation network in Texas only monitors one out of every five counties. The observation sites are nominally spaced 150-200 km apart and report hourly measurements. For this reason, the current system is incapable of providing observations at a spatial and temporal resolution to document mesoscale weather features and provide short-range forecasting. To overcome this problem, various site spacing procedures are used to propose to fill the gaps in the monitoring system. In this study, site spacing determination procedures based on correlation level, power spectrum, and true field error variance are used to analyze the temperature, dew point temperature, wind speed and pressure parameters over Texas. Hourly observations from 126 surface observing sites located in Texas and the adjacent states with a data coverage period varying from 6 to 21 years are considered for the study. The existence of isotropic conditions over the domain is tested by examining the spatial correlation variations of the parameters. The existence of anisotropic conditions for each parameter resulted in the search for sub-regions within the domain. Cluster analysis indicated three separate clusters for the domain. For each cluster, the spatial correlation variations are examined and spectral analysis is applied to determine the governing scales for each parameter’s variation. Error amounts in obtaining the true Fourier coefficients are analyzed; the highest wave number that can reasonably estimated is determined for each parameter in each cluster. Finally, the error variance in determining the true field for each parameter is examined for site spacings of 200, 150, 100, 50, and 25 km for each cluster.Item Integrated circuit outlier identification by multiple parameter correlation(Texas A&M University, 2004-09-30) Sabade, Sagar SureshSemiconductor 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.