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In this paper, we propose a novel spatial variation modeling method based on hidden Markov tree (HMT) for nanoscale integrated circuits, which could efficiently improve the accuracy of full-wafer/chip spatial variations recovery at extremely low measurement cost. Applying this method, HMT is introduced to set up a statistical model for coefficients after exploring the underlying correlated representation...
In this paper, we propose a novel spatial variation modeling method based on robust dictionary learning for nanoscale integrated circuits. This method takes advantage of the historical data to efficiently improve the accuracy of wafer-level spatial variation modeling with extremely low measurement cost. Robust regression is adopted by our implementation to reduce the bias posed by outliers. An iterative...
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