.png){kind=icon}
返回
Variation-Aware SRAM Cell Optimization Using Deep Neural Network-Based Sensitivity Analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSI.2021.3052985.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Under process, voltage, and temperature variations, SRAM cell stability largely fluctuates from the nominal value. In the design step, SRAM cell optimization while ignoring the fluctuation induces the yield loss for the stability. Variation-aware optimization of an SRAM cell can prevent the yield loss problem by considering the mean and variance of SRAM cell stability when finding optimal design parameters. This paper proposes a novel SRAM optimization method that uses a deep neural network (DNN). Multiple DNNs from ensemble techniques represent the mean and variance of SRAM cell stability for the nominal design parameters. Subsequent sensitivity analysis of DNN extracts the K design parameters that have the most dominant effects on the mean and variance of SRAM cell stability. Then multidimensional optimization is used to find the optimal values of these K parameters to maximize the mean stability while minimizing its variance. The proposed method achieved an average of 2% error compared to MC simulation. The proposed optimization method takes only 561 s to provide the most optimal design parameter values of an SRAM cell.
Keyword:
Static random access memory
read static noise margin (RSNM)
statistical analysis
sensitivity analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。