Practical iterated fill synthesis for CMP uniformity

  • Authors:
  • Yu Chen;Andrew B. Kahng;Gabriel Robins;Alexander Zelikovsky

  • Affiliations:
  • UCLA Computer Science Dept., Los Angeles, CA;UCLA Computer Science Dept., Los Angeles, CA;Department of Computer Science, University of Virginia, Charlottesville, VA;Department of Computer Science, Georgia State University, Atlanta, GA

  • Venue:
  • Proceedings of the 37th Annual Design Automation Conference
  • Year:
  • 2000

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Abstract

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].