Meta-Model based High-Dimensional Yield Analysis using Low-Rank Tensor Approximation
Proceedings of the 56th Annual Design Automation Conference 2019(2019)
摘要
"Curse of dimensionality" has become the major challenge for existing high-sigma yield analysis methods. In this paper, we develop a meta-model using Low-Rank Tensor Approximation (LRTA) to substitute expensive SPICE simulation. The polynomial degree of our LRTA model grows linearly with circuit dimension. This makes it especially promising for high-dimensional circuit problems. Our LRTA meta-model is solved efficiently with a robust greedy algorithm, and calibrated iteratively with an adaptive sampling method. Experiments on bit cell and SRAM column validate that proposed LRTA method outperforms other state-of-the-art approaches in terms of accuracy and efficiency.
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关键词
Failure Probability, Low-Rank Tensor Approximation, Meta-Model, Process Variation
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