<p dir="ltr">Technological advancements in the semiconductor industry have led to the development of fast, low-power, and high-performance electronic devices. With evolving process technologies, the size of an electronic device has greatly reduced, and the number of features a single device can support has steadily increased. To achieve this, billions of transistors are integrated into small electronic chips leading to an increase in the complexity of manufacturing processes. Electronic chips that are manufactured using such complex manufacturing processes are prone to have a large number of defects that are difficult to test, and cause reliability issues. To tackle these issues and produce highly reliable chips, there is a growing need to test each manufactured chip thoroughly. This requires the application of a large number of tests by a tester. The cost of testing an electronic chip primarily depends on the storage requirements of the tester, and the test application time required. The large number of tests required to rigorously test each chip leads to an increase in the testing cost. Earlier works reduced the testing cost by reducing the input storage requirements of the tester. The input storage requirements are reduced by using each stored test on the tester to apply several different tests to the circuit. Several different tests are also applied based on each stored test to improve the quality of a test set. The goal of this thesis is to aide in producing reliable chips, by creating test sets that can detect faults from different fault models. The test sets are created by improving the quality of a test set. </p><p><br></p><p dir="ltr">First, test sets with low storage requirements are produced for defect detection. A base test set is generated and stored. Each stored test is perturbed to produce several different tests. Algorithms are then described in two different scenarios to select a subset of the perturbed tests. The selected subset of tests improves the quality of defect detection with a minimal increase in the input storage requirements.</p><p><br></p><p dir="ltr">Next, test sets with low-storage requirements are produced for defect diagnosis. A fault detection test set is generated and stored. Each stored test is perturbed to produce several different tests. A procedure is then described to select a subset of the perturbed tests to be used as diagnostic tests. The diagnostic test set selected improves the quality of defect diagnosis with a minimal increase in the input storage requirements.</p><p><br></p><p dir="ltr">Finally, storage-aware test sets are produced targeting several fault models in two steps. In the first step, tests in a base test set are replaced with improved tests to produce an improved test set. The improved test set is stored, and it improves the quality of defect detection with no increase in the storage requirements. In the second step, each improved test is perturbed to produce several different tests. A procedure is then described to select a subset of the perturbed tests. The selected subset of tests further improves the quality of defect detection with a minimal increase in the input storage requirements.</p>

This study compares the impact of dimensional modelling and data vault modelling on the performance and maintenance effort of data warehouses. Dimensional modelling is a data warehouse modelling technique pioneered by Ralph Kimball in the 1980s that is much more effective at querying large volumes of data in relational databases than third normal form data models. Data vault modelling is a relatively new modelling technique for data warehouses that, according to its creator Dan Linstedt, was created in order to address the weaknesses of dimensional modelling. To date, no scientific comparison between the two modelling techniques have been conducted. A scientific comparison was achieved in this study, through the implementation of several experiments. The experiments compared the data warehouse implementations based on dimensional modelling techniques with data warehouse implementations based on data vault modelling techniques in terms of load performance, query performance, storage requirements, and flexibility to business requirements changes. An analysis of the results of each of the experiments indicated that the data vault model outperformed the dimensional model in terms of load performance and flexibility. However, the dimensional model required less storage space than the data vault model. With regards to query performance, no statistically significant differences existed between the two modelling techniques. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

This study compares the impact of dimensional modelling and data vault modelling on the performance and maintenance effort of data warehouses. Dimensional modelling is a data warehouse modelling technique pioneered by Ralph Kimball in the 1980s that is much more effective at querying large volumes of data in relational databases than third normal form data models. Data vault modelling is a relatively new modelling technique for data warehouses that, according to its creator Dan Linstedt, was created in order to address the weaknesses of dimensional modelling. To date, no scientific comparison between the two modelling techniques have been conducted. A scientific comparison was achieved in this study, through the implementation of several experiments. The experiments compared the data warehouse implementations based on dimensional modelling techniques with data warehouse implementations based on data vault modelling techniques in terms of load performance, query performance, storage requirements, and flexibility to business requirements changes. An analysis of the results of each of the experiments indicated that the data vault model outperformed the dimensional model in terms of load performance and flexibility. However, the dimensional model required less storage space than the data vault model. With regards to query performance, no statistically significant differences existed between the two modelling techniques. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014