A robust and automated methodology for the analysis of Time-Dependent Variability at transistor level

In the past few years, Time-Dependent Variability has become a subject of growing concern in CMOS technologies. In particular, phenomena such as Bias Temperature Instability, Hot-Carrier Injection and Random Telegraph Noise can largely affect circuit reliability. It becomes therefore imperative to develop reliability-aware design tools to mitigate their impact on circuits. To this end, these phenomena must be first accurately characterized and modeled. And, since all these phenomena reveal a stochastic nature for deeply-scaled integration technologies, they must be characterized massively on devices to extract the probability distribution functions associated to their characteristic parameters. In this work, a complete methodology to characterize these phenomena experimentally, and then extract the necessary parameters to construct a Time-Dependent Variability model, is presented. This model can be used by a reliability simulator.