Achim Gratz

Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data

The analysis, control, and optimization of manufacturing processes in the semiconductor industry are applications with significant economic impact. Modern semiconductor manufacturing processes feature an increasing number of processing steps with an increasing complexity of the steps themselves to generate a flood of multivariate monitoring data. This exponentially increasing complexity and the associated information processing and productivity demand impose stringent requirements, which are hard to meet using state-of-the-art monitoring and analysis methods and tools. This chapter deals with the application of selected methods from soft computing to the analysis of deviations from allowed parameters or operation ranges, i.e., anomaly or novelty detection, and the discovery of nonobvious multivariate dependencies of the involved parameters and the structure in the data for improved process control. Methods for online observation and offline interactive analysis employing novelty classification, dimensionality reduction, and interactive data visualization techniques are investigated in this feasibility study, based on an actual application problem and data extracted from a CMOS submicron process. The viability and feasibility of the investigated methods are demonstrated. In particular, the results of the interactive data visualization and automatic feature selection methods are most promising. The chapter introduces to semiconductor manufacturing data acquisition, application problems, and the regarded soft-computing methods in a tutorial fashion. The results of the conducted data analysis and classification experiments are presented, and an outline of a system architecture based on this feasibility study and suited for industrial service is introduced.