Recipe-Independent Indicator for Tool Health Diagnosis and Predictive Maintenance

Advanced sensor and information technologies have made real-time tool data readily accessible to tool and process engineers. A significant number of tool parameters (status variable identifications) are collected during wafer processing, and a large amount of tool data is acquired and available for fault detection and classification (FDC). Many IC makers have substantially improved the process capabilities by implementing FDC. With the real-time tool data, one can also evaluate the overall tool condition so that tool maintenance can be more effectively scheduled and the post-maintenance tool condition can be more easily qualified. However, due to the frequent change of recipes and the diversity of operations, the overall tool health is very difficult to evaluate. In this paper, we propose a recipe-independent health indicator based on the generalized moving variance. It is shown that the indicator faithfully reveals the tool condition regardless of recipe/operation changes. With the tool health indicator, possible tool faults can be identified and proper maintenance measures can be scheduled accordingly. The proposed indicator will be demonstrated and validated through the case studies of a plasma-enhanced chemical vapor deposition and a physical vapor deposition tool from a local fab.