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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 defects 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 converting KLA-Tencor reticle inspection defect maps into a
format compatible with KLA-Tencor's Klarity DefecTM data analysis database. The objective 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.
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