| dc.description.abstract | Thanks to the rapid progress of semiconductor technology for the past 30 several years, many high-tech products could be hence developed. Therefore, we could hereby understand how important the role that semiconductor plays in the industrial development. However, semi- conductor is a field with high thresholds in technology, cost and standardization, therefore, all semiconductor companies aim for increasing their profitability without any exception. Among so many methodologies, Yield Management is highly emphasized and researched. In brief, Yield could be defined as a percentage of good chips over all chips in production line. In order to gain Yield enhancement and increase the profitability, we must predict Yield by analyzing the huge amount of data generated during the manufacturing process systematically.
In this thesis, several popular Yield Models in semiconductor field were evaluated by the same Defect Inspection data to understand which one is the best for each Defect Inspection Step, including those Yield Models developed by the Case Company. The Case Company intended to develop the simple Yield Models based on the classifications of the tested chips, and tried to apply such Models on all Defect Inspection Steps. After evaluation, this thesis could contribute several decision rules to the management level of the Case Company to help make decisions.
According to the above evaluation, we found those Yield Models developed by the Case Company all have pretty good performance in Yield prediction. There were about 58% of the Defect Inspection Steps revealed that the Yield Models developed by the Case Company had both precise and stable results in prediction. The prediction results became even better, 100%, if we only considered precision to evaluate those Yield Models. | en_US |