With expected implementation of low k 1 lithography on 193nm scanners for 65nm node wafer production, high resolution defect inspection will be needed to insure reticle quality and reticle manufacture process monitoring. Reticle cost and reticle defectivity are both increasing with each shrink to the next node. Simultaneously, systems on chip (SoC) designs are increasing in which a large area of the exposure field typically contains dummy patterns and other features which are not electrically active. Knowing which defects will electrically impact device yield and performance can improve reticle manufacturing yield and cycle time --resulting in lower reticle costs.This investigation examines the feasibility of using additional design data layers for die-to-database reticle inspection to determine in real time the relevance of a reticle defect by its location in the device (Smart Inspection TM ). The impact to data preparation and inspection throughput is evaluated. The current prototype algorithm is built on the XPA and XPE die-to-database algorithms for chrome-on-glass and EPSM reticles, respectively. The algorithms implement variable sensitivity based on the additional design data regions. During defect review the defects are intelligently binned into the different predetermined design regions. Tests show the new Smart Inspection algorithm provides the capability of using higher than normal sensitivity in critical regions while reducing sensitivity in less critical regions to filter total defect counts and allow for the review of just defects that matter.Performance characterization of a variable sensitivity Smart Inspection algorithm is discussed in addition to the filtering of the total defect count during review to show the defects that matter to device performance. Using seven critical layer production reticles from a system on chip device we examine the applications of Smart Inspection by layer including active, poly, contact, metal and via layers. Data volumes for additional data layers show little impact to inspection data prep time. The total area of the reticle where defects do not matter is as high as 70% on some layers. Review capabilities will be examined for various applications such as reviewing defects in the various regions such as SRAM, dummy pattern, and redundant contact/via specified regions.Lastly, the economics of Smart Inspection will be modeled using the collected knowledge of the applications from the production reticle characterized in this investigation.
FlexRay programmable illumination and LithoTuner software is combined in several use cases. The first use case is source mask optimization (SMO) in which the process window is maximized for a static random access memory (SRAM) design. In a 55 nm half-pitch contact hole array (k 1 = 0.38, NA= 1.35, λ= 193 nm), the process window (PW) with FlexRay programmable illumination is twice as large as the PW with cQuad illumination defined through completely refractive illumination. The second use case is optical proximity error (OPE) minimization in which the large PW from SMO is realized on every scanner in the fab. The OPE error is reduced by 17% with LithoTuner and FlexRay. The third use case is matching two ArF scanners, a 1950i with FlexRay to a 1700i with diffractive optical element (DOE) illumination. With LithoTuner and FlexRay, the root means squared (rms) critical dimension (CD) error is reduced by 29% in this third use case. The last use case, intrafield dose optimization with DoseMapper is combined with FlexRay programmable source optimization to reduce the CD error on a wafer from a mean-to-target critical dimension (CD) error in the mask manufacturing process. This combination was optimized with LithoTuner to reduce the root mean square (rms) wafer CD error by 30%.
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