Modeling MOSFET Drain Current Non-Gaussian Distribution With Power-Normal Probability Density Function

In this letter, a family of power-normal probability density functions is proposed for the asymmetric non-Gaussian distribution of drain current. The results of the proposed methodology are compared against both statistical silicon data and SPICE model Monte Carlo simulation results. Excellent agreement is observed for the power-normal distribution with order of 2. With this proposed distribution, drain current at non-Gaussian high-sigma tail can be predicted by only median and variance extracted from statistical data of a small set of samples (e. g., 1k). For the first time, a simple analytic model is presented to capture memory read current non-Gaussian tail distribution near -6 sigma or even beyond, which is a major challenge in memory design for 28 nm technology node and below.