A technique to extract statistical model parameters for skewed Gaussian process variations is proposed. Statistical compact model extraction traditionally assumes that underlying process variations are Gaussian in nature. ON currents in certain high voltage technologies, which are linear in process deviations, show skew in their distribution and hence is indicative of skew in the underlying process variations. The use of skew-normal random variables is proposed to model such variations. Artificial neural networks (ANNs) are used to empirically model the functional relation of performance on process deviations and a framework to propagate skew-normal random variables through ANNs is proposed. A non-linear optimisation problem is formulated to extract the parameters that characterise the skew-normal process variations, with constraints imposed on the objective function to penalise any deviation from Gaussian variations. Results show that the extracted parameters, when simulated, match the performance parameter targets to within 3% for both Gaussian and skewed process variations.

中文翻译：

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偏正态分布的统计紧凑模型提取

提出了一种提取偏高斯过程变化统计模型参数的技术。传统上，统计紧凑模型提取假定基础过程变化本质上是高斯的。某些高压技术中的ON电流在过程偏差中呈线性，其分布存在偏斜，因此指示潜在的过程变化偏斜。提出使用偏态正态随机变量对这种变化建模。利用人工神经网络（ANN）对过程偏差的性能函数关系进行经验建模，并提出了一种通过神经网络传播偏态正态随机变量的框架。公式化了一个非线性优化问题，以提取表征时态正常过程变化的参数，在目标函数上施加约束，以惩罚与高斯变异的任何偏差。结果表明，提取的参数经过仿真后，对于高斯过程和偏斜过程变量，其性能参数目标均在3％以内。