Double-exposure techniques are currently being explored as alternatives to the low k1 problem that arises from the current absence of next-generation lithography (NGL) tools. Off-axis illumination conditions such as annular, used in conjunction with binary chrome masks are able to resolve features as small as 100nm. However, these off-axis approaches only improve a limited set of pitches. While certain features on the layout are enhanced, others loose contrast and cannot be imaged properly. Dipole illumination is the extreme off-axis case, but this high/low contrast problem is lessened by a double exposure approach. Double exposure corrections require a global optimization of two masks. As is the case with any multi-dimensional problem, current model OPC algorithms are able to locally optimize the solution, but it is difficult to guarantee a global optimal set. Including in the correction mask-manufacturing constraints can reduce this apparent problem. By limiting the nu! mber of local optimal states accessible to the convergence criterion, it is possible to arrive at a better solution. This solution is lithographically correct and easier to manufacture. In this work we present a data flow using models created previously for a model-assisted dipole decomposition to rank different approaches based on final image contrast, pattern fidelity and focus dependency. We also provide insights on how angled features can be successfully imaged under a double dipole approach, showing how such features need to be studied from an image formation point of view, not under simple geometric principles that rule out the presence of angled features.

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