A Compact High-Dimensional Yield Analysis Method using Low-Rank Tensor Approximation

We develop a meta-model using Low-Rank Tensor Approximation to substitute expensive SPICE simulation

Xiao Shi; Hao Yan; Qiancun Huang; Chengzhen Xuan; Lei He; Longxing Shi

2021

Scholarcy highlights

  • “Curse of dimensionality” has become the major challenge for existing high-sigma yield analysis methods
  • We develop a meta-model using Low-Rank Tensor Approximation to substitute expensive SPICE simulation
  • We develop a novel global sensitivity analysis approach to generate a reduced LRTA meta-model which is more compact
  • Experiments on memory and analog circuits validate that the proposed LRTA method outperforms other state-of-the-art approaches in terms of accuracy and efficiency

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