Efficient FDC based on hierarchical tool condition monitoring scheme

Tool condition evaluation and prognosis has been an arduous challenge in modern semiconductor manufacturing environment, especially for the foundry and analog companies with high product-mix and complicated technology nodes. More and more embedded and external sensors are installed to capture the genuine tool status for tool fault identification and, thus, tool condition analysis based on real-time equipment data becomes promising but also much more complex with the rapidly-increased number of sensors. In this paper, the feasibility of Generalized Moving Variance (GMV) technique is validated to consolidate the pure variations within tool Fault Detection and Classification (FDC) data into one indicator. Based on GMV, a hierarchical tool condition monitor scheme is developed by analyzing the GMV within functional clusters of sensors. With the introduction of this hierarchy, abnormal tool condition can be diagnosed and drilled down into sensor level for an efficient root cause analysis.