Name
Title | ||
|---|---|---|
Adaptive Clustering and Sampling for High-Dimensional and Multi-Failure-Region SRAM Yield Analysis. |
Abstract | ||
|---|---|---|
Statistical circuit simulation is exhibiting increasing importance for memory circuits under process variation. It is challenging to accurately estimate the extremely low failure probability as it becomes a high-dimensional and multi-failure-region problem. In this paper, we develop an Adaptive Clustering and Sampling (ACS) method. ACS proceeds iteratively to cluster samples and adjust sampling distribution, while most existing approaches pre-decide a static sampling distribution. By adaptively searching in multiple cone-shaped subspaces, ACS obtains better accuracy and efficiency. This result is validated by our experiments. For SRAM bit cell with single failure region, ACS requires 3-5X fewer samples and achieves better accuracy compared with existing approaches. For 576-dimensional SRAM column circuit with multiple failure regions, ACS is 2050X faster than MC without compromising accuracy, while other methods fail to converge to correct failure probability in our experiment.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299902.3309748 | ISPD |
Keywords | Field | DocType |
Process Variation, Failure Probability, SRAM, High Dimension, Failure Regions | Sampling distribution,Mathematical optimization,Computer science,Algorithm,Static random-access memory,Linear subspace,Process variation,Sampling (statistics),Cluster sampling,Cluster analysis,Bit cell | Conference |
ISBN | Citations | PageRank |
978-1-4503-6253-5 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shi Xiao | 1 | 9 | 5.97 |
| Hao Yan | 2 | 1 | 2.38 |
| Jinxin Wang | 3 | 0 | 0.34 |
| Xiaofen Xu | 4 | 0 | 0.34 |
| Fengyuan Liu | 5 | 0 | 0.34 |
| Longxing Shi | 6 | 116 | 39.08 |
| Lei He | 7 | 16 | 7.77 |