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 min- imizing the total amount of inserted fill. Comparisons with previ- ous 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 in linear programming and more accurate than non-iterated Monte-Carlo approaches.

Chen, Yu, Andrew Kahng, Gabriel Robins, and Alexander Zelikovsky. "Practical Iterated Fill Synthesis for CMP Uniformity." University of Virginia Dept. of Computer Science Tech Report (2000).