Spatiotemporal Pattern Modeling for Fault Detection and Classification in Semiconductor Manufacturing


This paper proposes a new approach to modeling the sequential flow characteristics of data patterns for detecting and classifying faulty processes in semiconductor manufacturing. Unlike conventional methods, which consider the spatial pattern distributions, the proposed approach models the spatial patterns local in time, transition time, staying time, and the sequential ordering of the local patterns through the process. To model these spatiotemporal patterns, we develop a sequential version of support vector data description (SVDD). This improves the precision of fault detection and easily detects the process start/end points; the moment a fault occurs can be captured immediately by checking the process start/end point based on the sequential order of SVDD. The detection of the moment a fault occurs is useful in analyzing the source of the fault.

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