Meta-Model based High-Dimensional Yield Analysis using Low-Rank Tensor Approximation

“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.