Feature selection for high-dimensional industrial data

In the semiconductor industry the number of circuits per chip is still drastically increasing. This fact and strong competition lead to the particular importance of quality control and quality assurance. As a result a vast amount of data is recorded during the fabrication process, which is very complex in structure and massively affected by noise. The evaluation of this data is a vital task to support engineers in the analysis of process problems. The current work tackles this problem by identifying the features responsible for success or failure in the manufacturing process (feature selection).