Fast source independent estimation of lithographic difficulty supporting large scale source optimization

Abstract

Many chip design and manufacturing applications including design rules development, optical proximity correction tuning, and source optimization can benefit from rapid estimation of relative difficulty or printability. Simultaneous source optimization of thousands of clips has been demonstrated recently, but presents performance challenges. We describe a fast, source independent method to identify patterns which are likely to dominate the solution. In the context of source optimization the estimator may be used as a filter after clustering, or to influence the selection of representative cluster elements. A weighted heuristic formula identifies spectral signatures of several factors contributing to difficulty. Validation methods are described showing improved process window and reduced error counts on 22 nm layout compared with programmable illuminator sources derived from hand picked patterns, when the formula is used to influence training clip selection in source optimization. We also show good correlation with fail prediction on a source produced with hand picked training clips with some level of optical proximity correction tuning. © 2012 SPIE.