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Yield analysis is a critical step in memory designs considering a variety of performance constraints. Traditional circuit level Monte-Carlo simulations for yield estimation of Static Random Access Memory (SRAM) cell is quite time consuming due to their characteristic of low failure rate, while statistical method of yield sensitivity analysis is meaningful for its high efficiency. This thesis proposes a novel statistical model to conduct yield sensitivity prediction on SRAM cells at the simulation level, which excels regular circuit simulations in a significant runtime speedup. Based on the theory of Kriging method that is widely used in geostatistics, we develop a series of statistical model building and updating strategies to obtain satisfactory accuracy and efficiency in SRAM yield sensitivity analysis. Generally, this model applies to the yield and sensitivity evaluation with varying design parameters, under the constraints of most SRAM performance metric. Moreover, it is potentially suitable for any designated distribution of the process variation regardless of the sampling method.

No description available.

Epileptic seizure presents a formidable threat to the life of its sufferers, leaving them unconscious within seconds of its onset. Having a mortality rate that is at least twice that of the general population, it is a true cause for concern which has gained ample attention from various research communities. About 800 million people in the world will have at least one seizure experience in their lifespan. Injuries sustained during a seizure crisis are one of the leading causes of death in epilepsy. These can be prevented by an early detection of seizure accompanied by a timely intervention mechanism. The research presented in this dissertation explores Kriging methods to exploit spatial correlations of electroencephalogram (EEG) Signals from the brain, for fast and accurate seizure detection in the Internet of Medical Things (IoMT) using edge computing paradigms, by modeling the brain as a three-dimensional spatial object, similar to a geographical panorama. This dissertation proposes basic, hierarchical and distributed Kriging models, with a deep neural network (DNN) wrapper in some instances. Experimental results from the models are highly promising for real-time seizure detection, with excellent performance in seizure detection latency and training time, as well as accuracy, sensitivity and specificity which compare well with other notable seizure detection research projects.

A novel concept of multiple deterministic analyses is proposed to design safer and more damage-tolerant structures, particularly when excited by dynamic including seismic loading in time domain. Since the presence of numerous sources of uncertainty cannot be avoided or overlooked, the underlying risk is estimated to compare design alternatives. To generate the implicit performance functions explicitly, the basic response surface method is significantly improved. Then, several surrogate models are proposed. The advanced factorial design and Kriging method are used as the major building blocks. Using these basic schemes, seven alternatives are proposed. Accuracies of these schemes are verified using basic Monte Carlo simulations. After verifying all seven alternatives, the capabilities of the three most desirable schemes are compared using a case study. They correctly identified and correlated damaged states of structural elements in terms of probability of failure using only few hundreds of deterministic analyses. The modified Kriging method appears to be the best technique considering both efficiency and accuracy. Estimating the probability of failure, the post-Northridge seismic design criteria are found to be appropriate. After verifying the proposed method, a Site-Specific seismic safety assessment method for nonlinear structural systems is proposed to generate a suite of ground excitation time histories. The information of risk is used to design a structure more damage-tolerant. The proposed procedure is verified and showcased by estimating risks associated with three buildings designed by professional experts in the Los Angeles area satisfying the post-Northridge design criteria for the overall lateral deflection and inter-story drift. The accuracy of the estimated risk is again verified using the Monte Carlo simulation technique. In all cases, the probabilities of collapse are found to be less than 10% when excited by the risk-targeted maximum considered earthquake ground motion satisfying the intent of the code. The spread in the reliability indexes for each building for both limit states cannot be overlooked, indicating the significance of the frequency contents. The inter story drift is found to be more critical than the overall lateral displacement. The reliability indexes for both limit states are similar only for few cases. The author believes that the proposed methodology is an alternative to the classical random vibration and simulation approaches. The proposed site-specific seismic safety assessment procedure can be used by practicing engineers for routine applications. The proposed reliability methodology is not problem-specific. It is capable of handling systems with different levels of complexity and scalability, and it is robust enough for multi-disciplinary routine applications. In order to show the multi-disciplinary application of the proposed methodology, the probability of failure of lead-free solders in Ball Grid Array 225 surface-mount packaging for a given loading cycle is estimated. The accuracy of the proposed methodology is verified with the help of Monte Carlo simulation. After the verification, probability of failure versus loading cycles profile is calculated. Such a comprehensive study of its lifetime behavior and the corresponding reliability analyses can be useful for sensitive applications.

名古屋大学博士学位論文 学位の種類:工学博士 (論文) 学位授与年月日:平成1年12月8日

Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-04-29Bitstream added on 2014-06-13T21:02:58Z : No. of bitstreams: 1 angelico_jc_dr_botfca.pdf: 838568 bytes, checksum: f6ccaf64afa3b5032e50bf933d5913de (MD5) / Métodos estatísticos de interpolação são freqüentemente utilizados para se obter as características dos solos em locais não amostrados, visando diminuir o número de amostras necessárias para um bom mapeamento do campo. Nesse trabalho, a estimativa da variabilidade espacial de atributos do solo foi realizada de duas maneiras: primeiramente utilizando-se os métodos estatísticos da krigagem e da co-krigagem e posteriormente as redes neurais artificiais. Os resultados obtidos pelos dois métodos foram comparados, com a finalidade de se verificar a eficiência das redes neurais artificiais na estimativa de atributos do solo. Os resultados mostraram que as redes neurais artificiais, em particular as redes Perceptron, com uma e com duas camadas de neurônios, são capazes de estimar as variabilidades espaciais dos solos com precisão maior do que os métodos estatísticos da krigagem e da co-krigagem. As redes neurais artificiais também se mostraram eficientes na estimativa de uma determinada variável do solo em função de sua classe textural. / Statistic methods of interpolation are often used to get the soil characteristics in non-sampled places in order to decrease samples numbers, which are necessary to obtain a good field mapping. In this project, the estimation of soil spatial variability attributes was done in two different ways. First, it was used statistic methods of kriging and cokriging, and in second instance, it was used artificial neural networks. The results computed by both techniques were compared each other in order to verify the efficiency of the artificial neural networks in estimating soil attributes. The results indicated that artificial neural networks, especially Perceptron networks, both with one and two layers of neurons, are able to estimate the soil spatial variability much better than the kriging and the cokriging methods. The artificial neural networks have also showed very efficient in estimating soil variables with respect to its textural class.

Resumo: Métodos estatísticos de interpolação são freqüentemente utilizados para se obter as características dos solos em locais não amostrados, visando diminuir o número de amostras necessárias para um bom mapeamento do campo. Nesse trabalho, a estimativa da variabilidade espacial de atributos do solo foi realizada de duas maneiras: primeiramente utilizando-se os métodos estatísticos da krigagem e da co-krigagem e posteriormente as redes neurais artificiais. Os resultados obtidos pelos dois métodos foram comparados, com a finalidade de se verificar a eficiência das redes neurais artificiais na estimativa de atributos do solo. Os resultados mostraram que as redes neurais artificiais, em particular as redes Perceptron, com uma e com duas camadas de neurônios, são capazes de estimar as variabilidades espaciais dos solos com precisão maior do que os métodos estatísticos da krigagem e da co-krigagem. As redes neurais artificiais também se mostraram eficientes na estimativa de uma determinada variável do solo em função de sua classe textural. / Abstract: Statistic methods of interpolation are often used to get the soil characteristics in non-sampled places in order to decrease samples numbers, which are necessary to obtain a good field mapping. In this project, the estimation of soil spatial variability attributes was done in two different ways. First, it was used statistic methods of kriging and cokriging, and in second instance, it was used artificial neural networks. The results computed by both techniques were compared each other in order to verify the efficiency of the artificial neural networks in estimating soil attributes. The results indicated that artificial neural networks, especially Perceptron networks, both with one and two layers of neurons, are able to estimate the soil spatial variability much better than the kriging and the cokriging methods. The artificial neural networks have also showed very efficient in estimating soil variables with respect to its textural class. / Orientador: Ivan Nunes da Silva / Coorientador: José Alfredo Covolan Ulson / Banca: Angelo Cataneo / Banca: Luiz Gonzaga Campos Porto / Banca: Gastão Moraes da Silveira / Banca: Casimiro Dias Gadanha Júnior / Doutor

In recent years nickel is mostly produced from lateritic ore deposits such as nontronite, limonite, etc. Resource estimation is difficult for laterite deposits as they have a weak and heterogeneous form. 3D modeling software are rather suitable for deposits having tabular or vein type ores. In this study the most appropriate estimation technique for resource estimation of nickel laterite deposits was investigated. One of the known nickel laterite deposits in Turkey is located at T&uuml / rkmen&ccedil / ardagi - G&ouml / rdes region. Since the nickel (Ni) grade recovered from drilling studies seem to be very low, a reconciliation pit having dimensions of 40 m x 40 m x 15 m in x-y-z directions was planned by Meta Nikel Kobalt Mining Company (META), the license owner of the mine, to produce nickel ore. 13 core drilling and 13 reverse circulation drilling (RC) and 26 column samplings adjacent to each drillholes were located in this area. Those three sampling results were compared to each other and as well as the actual production values obtained from reconciliation pit. On the other side 3D computer modeling was also used to model the nickel resource in T&uuml / rkmen&ccedil / ardagi - G&ouml / rdes laterites. The results obtained from both inverse distance weighting and kriging methods were compared to the results of actual production to find out the applicability of 3D modeling to laterite deposits. Modeling results showed that Ni grade of the reconciliation pit in T&uuml / rkmen&ccedil / ardagi - G&ouml / rdes, considering 0.5% Ni cut-off value, by using drillholes data, inverse distance weighting method estimates 622 tonnes with 0.553% Ni and kriging method estimates 749 tonnes with 0.527% Ni. The actual production pit results provided 4,882 tonnes of nickel ore with 0.649% Ni grade. These results show that grade values seem to be acceptable but in terms of tonnage, there are significant differences between theoretical estimated values and production values.

This thesis presents a comparison of the development of the concentrations of nitrogen oxides and sulfur dioxide in the air in the Usti region. The work described generally air pollution and related terms such as the sources of pollution emission, immission and atmospheric deposition. The study also evaluated pollution nitrogen oxides and sutur dioxide in the Usti region. This work describes the methods that were used in the calculations and evaluations. It also deals with the selecting appropriate measuring stations to handle the development of the concentrations of these substances in the air. The aim of this work is to find trends in time series of concentrations of NOx and SO2 and statistical evaluation of development levels in selected cities Ústí Region. The next section is evaluated spatial analysis of the concentration of nitrogen oxides and sulfur dioxide in the air in the Ústí Region. This area was chosen primarily because it is characterized by significant air pollution.