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In order to achieve an economical design-to-mask (DTM) development cycle in the low k1 domain, designers,
lithographers, and mask makers needed to move away from many sequentially isolated developmental activities onto one
collaborative environment managed by a computational lithography platform that integrates their respective
ecosystems. 1,2 A successful development cycle used to be achievable by designers providing designs to lithographers,
who then provided RET/OPC solutions to realize designs, but once k1 fell below a certain level, the lithographers could
not provide solutions to realize some critical designs, which then required feedback to designers for further redesigns
requiring further lithographic evaluation cycles. So collaboration and automations between lithographers and designers
became necessary to reduce feedback loops and development cycle time. RET and design solutions also were impacted
by mask making, and so mask maker's feedback on MRC and other constraints needed to be integrated for all three
groups to achieve an economical DTM.
As many lithographers attempted to print sub-80 nm pitches with 193 nm wavelength, it became necessary to use double
patterning to achieve feature resolution. With the effective pitch doubling on each split layer, there could be significant
increased design rule freedom for certain complex design situations. Using an integrated computational lithographic
platform, one could find design space sweet spots that could further achieve optimal lithographic performance. In this
paper, the optimization of design rules (DRD) for double pattern designs (~60 nm pitch) was explored with the mask
maker's perspective. The experiment to be presented started with a 2x nm design set of clips. Each set of clips
underwent size/width/space/pitch variations to generate a design space, and then each design space underwent SMO with
an inverse lithography technology (ILT) engine using various mask MRC's and manhattan segmentations. The
lithographic results were analyzed with respect to MRC and manhattan segmentation to show their impact on design
space and mask solutions.
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