The objective of manufacturing test is to separate the faulty circuits from the good circuits after they have been manufactured. Three problems encompassed by this task will be mentioned here. First, the reduction of the power consumed during test. The behavior of the circuit during test is modified due to scan insertion and other testing techniques. Due to this, the power consumed during test can be abnormally large, up to several times the power consumed during functional mode. This can result in a good circuit to fail the test or to be damaged due to heating. Second, how to modify the design so that it is easily testable. Since not every possible digital circuit can be tested properly it is necessary to modify the design to alter its behavior during test. This modification should not alter the functional behavior of the circuit. An example of this is test point insertion, a technique aimed at reducing test time and decreasing the number of faulty circuits that pass the test. Third, the creation of a test set for a given design that will both properly accomplish the task and require the least amount of time possible to be applied. The precision in separation of faulty circuits from good circuits depends on the application for which the circuit is intended and, if possible, must be maximized. The test application time is should be as low as possible to reduce test cost. This dissertation contributes to the discipline of manufacturing test and will encompass advances in the afore mentioned areas. First, a method to reduce the power consumed during test is proposed. Second, in the design modification area, a new algorithm to compute test points is proposed. Third, in the test set creation area, a new algorithm to reduce test set application time is introduced. The three algorithms are scalable to current industrial design sizes. Experimental results for the three methods show their effectiveness.

O desbalanceamento de dados em experimentos está muitas vezes presente em diversas pesquisas nas mais variadas áreas do conhecimento. Embora existam muitas maneiras de análise de tais dados, além de diversos recursos computacionais já implementados em diversos softwares estatísticos, ainda perdura dúvidas entre os pesquisadores a respeito da opção de análise mais eficiente. A literatura fornece ao pesquisador direção na escolha da metodologia de análise a obter maior eficácia nos resultados de sua pesquisa, mas o número elevado de opções pode tornar a escolha difícil. Em se tratando de testes estatísticos, algumas das opções para se trabalhar com dados desbalanceados são os testes t e Wald-F, mas ainda resta ao pesquisador decidir entre as várias opções disponíveis nos pacotes, pois nem sempre as opções padrões são as mais indicadas. No presente trabalho foram realizadas simulações com diferentes cenários experimentais, utilizando-se o delineamento casualizado em blocos com um fator de tratamento em uma situação e o esquema de tratamentos em parcelas subdividas em outra, sendo comparados quatro métodos de cálculo do número aproximado de graus de liberdade (Containment, Residual, Satterthwaite e Kenward-Roger). Verificou-se que o método de Kenward-Roger controla de maneira mais eficiente a taxa de erro tipo I e não é inferior aos outros métodos com respeito ao poder do teste Wald-F. / The data imbalance in experiments is often present in several researches in various fields of knowledge. While there are many ways to analyze these data in addition to various computer resources already implemented in many statistical software, doubt still lingers among researchers about the most efficient analysis option. The literature provides the researcher direction in choosing the analysis methodology to get better in your search results, but the large number of options can make the difficult choice. When it comes to statistical tests, some of the options for working with unbalanced data are the tests t and Wald-F, but there is still the researcher to decide between the various options available in the packages because the defaults are not always the most suitable. This experiment was carried out simulations with different experimental scenarios, using the randomized block design with one factor in a situation treatment and treatment regimen subdivided parcels in another, and compared four methods of calculating the approximate number of degrees of freedom (Containment, Residual, Satterthwaite and Kenward-Roger). It has been found that the method of Kenward-Roger controls more efficiently the type I error rate and is not inferior to other methods with respect to the power of the test Wald-F.

O desbalanceamento de dados em experimentos está muitas vezes presente em diversas pesquisas nas mais variadas áreas do conhecimento. Embora existam muitas maneiras de análise de tais dados, além de diversos recursos computacionais já implementados em diversos softwares estatísticos, ainda perdura dúvidas entre os pesquisadores a respeito da opção de análise mais eficiente. A literatura fornece ao pesquisador direção na escolha da metodologia de análise a obter maior eficácia nos resultados de sua pesquisa, mas o número elevado de opções pode tornar a escolha difícil. Em se tratando de testes estatísticos, algumas das opções para se trabalhar com dados desbalanceados são os testes t e Wald-F, mas ainda resta ao pesquisador decidir entre as várias opções disponíveis nos pacotes, pois nem sempre as opções padrões são as mais indicadas. No presente trabalho foram realizadas simulações com diferentes cenários experimentais, utilizando-se o delineamento casualizado em blocos com um fator de tratamento em uma situação e o esquema de tratamentos em parcelas subdividas em outra, sendo comparados quatro métodos de cálculo do número aproximado de graus de liberdade (Containment, Residual, Satterthwaite e Kenward-Roger). Verificou-se que o método de Kenward-Roger controla de maneira mais eficiente a taxa de erro tipo I e não é inferior aos outros métodos com respeito ao poder do teste Wald-F. / The data imbalance in experiments is often present in several researches in various fields of knowledge. While there are many ways to analyze these data in addition to various computer resources already implemented in many statistical software, doubt still lingers among researchers about the most efficient analysis option. The literature provides the researcher direction in choosing the analysis methodology to get better in your search results, but the large number of options can make the difficult choice. When it comes to statistical tests, some of the options for working with unbalanced data are the tests t and Wald-F, but there is still the researcher to decide between the various options available in the packages because the defaults are not always the most suitable. This experiment was carried out simulations with different experimental scenarios, using the randomized block design with one factor in a situation treatment and treatment regimen subdivided parcels in another, and compared four methods of calculating the approximate number of degrees of freedom (Containment, Residual, Satterthwaite and Kenward-Roger). It has been found that the method of Kenward-Roger controls more efficiently the type I error rate and is not inferior to other methods with respect to the power of the test Wald-F.