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To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced operators review reticle defects found by an inspection tool and then manually classify each defect as pass, warn, or fail based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer defects on a completed wafer, potential defe cts on all associated reticles must be manually searched on a layer-by-layer basis in an effort to identify the reticle responsible for the wafer yield loss. This problem reticle search process is a very tedious and time-consuming task and may cause extended manufacturing line-down situations. Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be spent working on other more productive activities. This paper examines an automated software solution for co nverting KLA-Tencor reticle inspection defect maps into a format compatible with KLA-Tencors Klarity Defect data analysis database. The objec tive is to use the graphical charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing reticle inspection sample plans in an effort to support lean manufacturing initiatives for wafer fabs. Keywords: Klarity, reticle, inspection, wafer, analysis, automated |
