Interval-valued statistical modeling of oxide chemical-mechanical polishing

Authors:
J. D. Ma;C. F. Fang;R. A. Rutenbar;Xiaolin Xie;D. S. Boning
Affiliations:
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., New York, NY, USA;Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., New York, NY, USA;Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., New York, NY, USA;Adv. Technol. Group, Synopsys Inc., Mountain View, CA, USA;IBM Syst. & Technol. Group, Austin, TX, USA
Venue:
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Year:
2005

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Abstract

Technology-oriented tools provide the raw data needed to optimize the fabrication process itself, and to predict problematic variational impacts on silicon design. Unfortunately, even in these physics-oriented tools, statistically uncertain quantities appear as crucial inputs. To date, Monte Carlo techniques have been the dominant solution method. We suggest an alternative in which uncertainties are represented as correlated intervals, and interval-valued computations replace the standard scalar operations in the numerical algorithm for the tool. We use an oxide chemical-mechanical polishing tool as an example, and show how to "retrofit" workable statistical models on top of the original algorithm. Accuracies to within /spl sim/1-10% of Monte Carlo simulation, and speedups of /spl sim/10-100X can be achieved, depending on whether we choose a formulation which emphasizes accuracy, or efficiency.