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25 October 2007 Automatic optimization of MEEF-driven defect disposition for contamination inspection challenges
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Ever-tightened design rules and ensuing aggressive OPC features pose significant challenges for wafer fabs in the pursuit of compelling yield and productivity. The introduction of advanced reticles considerably augments the mask error enhancement factor (MEEF) where progressive defects or haze, induced by repeated laser exposure, continue to be a source of reticle degradation threatening device yield. High resolution reticle inspection now emerges as a rescue venue for wafer fabs to assure their photomask integrity during intensive deep UV exposure. Integrated in the high resolution reticle inspection, a MEEF-driven lithographic detector "Litho3" can be used run-time to group critical defects into a single bin. Previous investigations evinced that critical defects identified by such detector were directly correlated with defects printed on wafer, upon which fab users can make cogent decisions towards reticle disposition and cleaning therefore reduce cycle time. One of the challenges of implementing such detector resides in the lengthy set up of user-defined parameters, from practitioner standpoint, can considerably extend reticle inspection time and inevitably delay production. To overcome this, an automatic simulation program has been written to optimize Litho3 settings based off a pre-inspection in which only default Litho3 values are needed. Upon completion of the pre-inspection, the images are then scanned and processed to extract the optimal Litho3 parameters that are largely dependent upon the feature size characteristics and local MEEF. Thus optimized Litho3 parameters can then be input into the recipe set up to enable a real-time inspection, as such fab user can timely access the defect criticality information for subsequent defect disposition. In the interest of printability validation, such defect information and associated coordinates can be passed onto defect review via XLINK for further analysis. Corresponding MEEF values are also available for all identified critical defects. Through this automatic program the set up time for Litho3 can be reduced by up to 90%. For high capacity production fabs running a pre-inspection is deemed infeasible; this automatic optimization program can also serve as a direct interpretation of any regular reticle inspection even without invoking Litho3 set up, yet in the end provide output in the context of defect criticality. Results acquired from this program were found in good accordance with those from the real-time Litho3 inspection, for both critical and non-critical layers of 90 nm design node. Such capability allows detailed study of defect criticality in relation to its size, defect optical transmittance, residing surface, its proximity to a printing pattern as well as lithography parameters such as NA and sigma. Furthermore, coupling this automatic program with high resolution inspection also assists in determining lithography process window and an indepth comprehension of defect progression mechanism.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tracy Huang, Aditya Dayal, Kaustuve Bhattacharyya, Joe Huang, William Chou, Yung-Feng Cheng, Shih-Ming Yen, James Cheng, and Peter Peng "Automatic optimization of MEEF-driven defect disposition for contamination inspection challenges", Proc. SPIE 6730, Photomask Technology 2007, 67302B (25 October 2007);

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