Name
Abstract | ||
|---|---|---|
Performance failure has become a major threat for various memory and analog circuits. It is challenging to estimate the extremely small failure probability when failed samples are distributed in multiple disjoint regions. In this article, we propose an adaptive importance sampling (AIS) algorithm. AIS has several iterations of sampling region adjustments, while existing methods predecide a static sampling distribution. We design two adaptive frameworks based on resampling and population Metropolis-Hastings (MH) to iteratively search for failure regions. The experimental results of the AIS method exhibit better efficiency and higher accuracy. For SRAM bit cell with single failure region, the AIS method uses 2-$27{\times }$ fewer samples and reaches better accuracy when compared to several recent methods. For a two-stage amplifier circuit with multiple failure regions, the AIS method is $90{\times }$ faster than Monte Carlo and 7-23 ${\times }$ over other methods. For charge pump circuit and $C^{2}MOS$ master-slave latch circuit, the AIS method can reach 6-$18{\times }$ and 4-$6{\times }$ speedup over other methods, respectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCAD.2020.2966481 | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Keywords | DocType | Volume |
Adaptive importance sampling (AIS),failure probability,failure regions,Metropolis–Hastings (MH),process variation,resampling | Journal | 39 |
Issue | ISSN | Citations |
12 | 0278-0070 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shi Xiao | 1 | 9 | 5.97 |
| Hao Yan | 2 | 1 | 2.38 |
| Jinxin Wang | 3 | 1 | 0.35 |
| Jiajia Zhang | 4 | 26 | 16.64 |
| Longxing Shi | 5 | 116 | 39.08 |
| Lei He | 6 | 16 | 7.77 |