Importance sampling Monte Carlo simulations for accurate estimation of SRAM yield

Variability is an important aspect of SRAM cell design. Failure probabilities of P
fail
≤10
⁻¹⁰
have to be estimated through statistical simulations. Accurate statistical techniques such as Importance Sampling Monte Carlo simulations are essential to accurately and efficiently estimate such low failure probabilities. This paper shows that a simple form of Importance Sampling is sufficient for simulating P
fail
≤10
⁻¹⁰
for the SRAM parameters Static Noise Margin, Write Margin and Read Current. For the SNM, a new simple technique is proposed that allows extrapolating the SNM distribution based on a limited number of trials. For SRAM total leakage currents, it suffices to take the averages into account for designing SRAM cells and modules. A guideline is proposed to ensure bitline leakage currents do not compromise SRAM functionality.