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.

中文翻译：

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基于深度神经网络的敏感性分析的变异感知SRAM单元优化

在工艺，电压和温度变化的情况下，SRAM单元的稳定性会从标称值大幅波动。在设计步骤中，SRAM单元的优化在忽略波动的情况下会导致良率损失，从而降低稳定性。在寻找最佳设计参数时，通过考虑SRAM单元稳定性的均值和方差，对SRAM单元进行变化感知的优化可以防止良率损失问题。本文提出了一种使用深度神经网络（DNN）的新型SRAM优化方法。集成技术中的多个DNN代表了标称设计参数下SRAM单元稳定性的均值和方差。随后的DNN敏感性分析提取了 $ {K} $ 设计参数对SRAM单元稳定性的均值和方差具有最主要的影响。然后使用多维优化找到这些的最佳值 $ {K} $ 参数，以使平均稳定性最大化，同时将其方差最小化。与MC仿真相比，该方法的平均误差为2％。所提出的优化方法仅需561 s即可提供SRAM单元的最佳设计参数值。