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 MonteCarlo 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].

• Type: Conference
• Year: 2000
• Where: DAC
• Authors: Yu Chen, Andrew B. Kahng, Gabriel Robins, Alexander Zelikovsky
• Keywords: DAC 2000, Density Variation, Design Automation, Layout Density Control, Practical Iterated Methods