Localized source and mask optimization with narrow-band level-set method

Abstract

An efficient lithographic source and mask optimization approach is developed based on localized level set methods, which is reformulated as an inverse problem by tracking the evolution of level-set functions (LSFs) embedding the level-set representation of source and mask patterns. A distance regularized level-set (DRLS) term is incorporated into the level-set formulation enabling not only stable LSF evolution and accurate computation by maintaining a signed-distance property, but also a simple and efficient narrow-band implementation. Consequently, optimization dimensionality is significantly reduced by updating the pixels in the vicinity of zero level set (narrow band) instead of all the level sets, effectively reducing computation complexity, resulting in significantly improvements in pattern fidelity convergence in terms of runtime, computation load, Euler time step and caching memory requirement which are merited by numerical simulations.