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  and effective density ). Our new methods are more efficient in practice than linear programming  and more accurate than non-iterated Monte-Carlo approaches .
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.