Directed Self-Assembly Based Cut Mask Optimization for Unidirectional Design

Abstract

Unidirectional design has attracted lots of attention with the scaling down of technology nodes. However, due to the limitation of traditional lithography, printing the randomly distributed dense cuts becomes a big challenge for highly scaled unidirectional layout. Recently directed self-assembly (DSA) has emerged as a promising lithography technique candidate for cut manufacturing because of its ability to form small cylinders inside the guiding templates and the actual pattern size can be greatly reduced. In this paper, we perform a comprehensive study on the DSA cut mask optimization problem. We first formulate it as integer linear programming (ILP) to assign cuts to different guiding templates, targeting at minimum conflicts and line-end extensions. As ILP may not be scalable for very large size problem, we further propose a speed-up method to decompose the problem into smaller ones and solve them separately. We then merge and legalize the solutions without much loss of result quality. The proposed approaches can be easily extended to handle more DSA guiding patterns with complicated shapes. Experimental results show that our methods can significantly reduce the total number of unresolvable patterns and the line-end extensions for the targeted layouts.