Practical iterated fill synthesis for CMP uniformity

We propose practical iterated methods for layout density control for CMP uniformity, based on linear programming, Monte-Carlo and greedy algorithms. We experimentally study the tradeoffs between two main filling objectives: minimizing density variation, and minimizing the total amount of inserted fill. Comparisons with previous filling methods show the advantages of our new iterated Monte-Carlo and iterated greedy methods. We achieve near-optimal filling with respect to each of the objectives and for both density models (spatial density [3] and effective density [8]). Our new methods are more efficient in practice than linear programming [3] and more accurate than non-iterated Monte-Carlo approaches [1].