Modeling and Mitigation of Parametric Time-Dependent Variability in Digital Systems

Abstract

Current and future semiconductor technology nodes, bring about a variety of challenges that pertain to the reliability and dependability of digital integrated systems. Compounds, such as high-κ materials in the transistor gate stack, tend to intensify the time-zero and time-dependent variability of transistors. A case, can certainly be made for the phenomena like Bias Temperature Instability (BTI) and Random Telegraph Noise (RTN). The use of such modern materials is also coupled to an aggressive downscaling trend, which further amplifies matter discretization within the transistor’s channel. These are two major manufacturing trends that give rise to time-dependent variability in integrated digital systems. It stands to reason, that as the materials become more variable, the error rates experienced at the circuit- or even system-level are also intensified. Coupled to increasing integrated circuit functionality (namely, the “More than Moore” trend), it is reasonable to expect that future digital chips will be exhibiting reliability profiles that vary across the chip’s lifetime. The goal of the research presented in the current text is to study the above observations both regarding the modeling and the mitigation of time-dependent variability. Target systems, naturally, constitute digital integrated circuits, such as processors and memories thereof. The current research contributes with specific reductions to practice that aim to confirm existing techniques and develop novel insight into the modeling and mitigation of time-dependent variability. As such, the current text is broadly split into two major parts.