Equipment fault detection using spatial signatures

This paper describes a new methodology for equipment fault detection. The key features of this methodology are that it allows for the incorporation of spatial information and that it can be used to detect and diagnose equipment faults simultaneously. This methodology consists of constructing a virtual wafer surface from spatial data and using physically based spatial signature metrics to compare the virtual wafer surface to an established baseline process surface in order to detect equipment faults. Statistical distributional studies of the spatial signature metrics provide the justification of determining the significance of the spatial signature. Data collected from a rapid thermal chemical vapor deposition (RTCVD) process and from a plasma enhanced chemical vapor deposition (PECVD) process are used to illustrate the procedures. This method detected equipment faults for all 11 wafers that were subjected to induced equipment faults in the RTCVD process, and even diagnosed the type of equipment fault for 10 of these wafers. This method also detected 42 of 44 induced equipment faults in the PECVD process.