NBTI in Nanoscale MOSFETs - The Ultimate Modeling Benchmark

TL;DR

This paper compares reaction-diffusion and reaction-limited models for NBTI in nanoscale MOSFETs, using experimental data to determine which model best explains device degradation and recovery.

Contribution

It provides a definitive experimental benchmark distinguishing between the two main NBTI models, favoring reaction-limited mechanisms over diffusion-based ones.

Findings

Diffusion is unlikely to be the limiting factor in NBTI degradation.

Experimental data aligns with reaction-limited models.

Understanding physical mechanisms is key to device reliability.

Abstract

After nearly half a century of research into the bias temperature instability (BTI), two classes of models have emerged as the strongest contenders: one class of models, the reaction-diffusion models, is built around the idea that hydrogen is released from the interface and that it is the diffusion of some form of hydrogen that controls both degradation and recovery. While many different variants of the reaction-diffusion idea have been published over the years, the most commonly used recent models are based on non-dispersive reaction rates and non- dispersive diffusion. The other class of models is based on the idea that degradation is controlled by first-order reactions with widely distributed (dispersive) reaction rates. We demonstrate that these two classes give fundamentally different predictions for the stochastic degradation and recovery of nanoscale devices, therefore providing the ultimate modeling benchmark. Using de- tailed experimental time-dependent defect spectroscopy (TDDS) data obtained on such nanoscale devices, we investigate the compatibility of these models with experiment. Our results show that the diffusion of hydrogen (or any other species) is unlikely to be the limiting aspect that determines degradation. On the other hand, the data are fully consistent with reaction-limited models. We finally argue that only the correct understanding of the physical mechanisms leading to the significant device-to-device variation observed in the degradation in nanoscale devices will enable accurate reliability projections and device optimization.