Name
Abstract | ||
|---|---|---|
Abstract“Curse of dimensionality” has become the major challenge for existing high-sigma yield analysis methods. In this article, 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 the 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 a bootstrap-assisted adaptive sampling method. We also develop a novel global sensitivity analysis approach to generate a reduced LRTA meta-model which is more compact. It further accelerates the procedure of model calibration and yield estimation. 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3483941 | ACM Transactions on Design Automation of Electronic Systems |
Keywords | DocType | Volume |
Process variation, failure probability, meta-model, low-rank tensor approximation, global sensitivity analysis | Journal | 27 |
Issue | ISSN | Citations |
2 | 1084-4309 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Shi | 1 | 0 | 0.34 |
| Hao Yan | 2 | 1 | 2.38 |
| Qiancun Huang | 3 | 0 | 0.34 |
| Chengzhen Xuan | 4 | 0 | 0.34 |
| Lei He | 5 | 16 | 7.77 |
| Longxing Shi | 6 | 0 | 0.34 |