Abstract
One of the most important steps in the manufacturing of semiconductor wafers is the purging process of chambers. In the manufacturing operations, some chemicals inserted into chambers might remain in the chambers after the operations, causing the appearance of contaminant particles on the surface of the subsequently produced wafers. The particles can damage the integrated circuits on the wafers, thereby reducing yields. It is necessary to regularly purge chambers and remove residual chemicals to maintain the cleanliness inside the chambers. In the thesis, we discuss an experiment conducted to study the effects of some purging factors. In the experiment, it was impossible to directly measure the degree of cleanliness inside the chamber, so that the number of particles on a wafer produced by the chamber was taken as a surrogate response to evaluate the cleanliness level inside the chamber. To accurately determine the cleaning efficiency of the purging methods, the process of purging-then-making-a-wafer was continuously repeated several times for each purging method, and in each repetition, the number of particles on the wafer was measured and recorded. For the experiment, we propose an innovative analysis method. We adopt some domain knowledge about purging and modify the generalized linear models in statistics to build a reasonable statistical model with various factorial effects for the data generated by the experiment. We follow the Fisher scoring method to derive an algorithm for the estimation of the parameters in the model. For the testing of effect significance, we present a method based on the likelihood ratio. We use computer simulations to verify the effectiveness of the methods, and find they can accurately estimate and test the parameters. We demonstrate these methods on a real data of purging experiment to identify the influential factorial effects, which are then used to suggest a best purging method for improving the yield of wafers.