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Statistical nature of hard breakdown recovery in high-κ dielectric stacks studied using ramped voltage stress
Journal article   Peer reviewed

Statistical nature of hard breakdown recovery in high-κ dielectric stacks studied using ramped voltage stress

X. Feng, N. Raghavan, S. Mei, S. Dong, K.L. Pey and H. Wong
Microelectronics and reliability, Vol.88-90, pp.164-168
09/2018

Abstract

Clustering model Hard breakdown recovery High-κ dielectric Ramped voltage stress Weibull distribution Weibull slope
In replacing the conventional SiO2 gate dielectric with high-κ materials, new challenges emerge on understanding the kinetics of dielectric breakdown due to the different properties of the new bulk oxide and the interfacial layers at the substrate and gate electrode interface as well. Among several complexities, dielectric relaxation and recovery have received a lot of attention due to their promising applications in resistive random access memory (RRAM). In this study, we explore the stochastic nature of hard breakdown recovery in HfO2, taking advantage of ramped voltage stress (RVS) measurements, which are theoretically equivalent to the widely used constant voltage stress (CVS), while being significantly less time-consuming. We found that the possibility of recovery is largely dependent on the ramp rate during RVS as the dielectric needs adequate time and sufficient thermal budget to recover. The clustering model is found to be a good fit to the RVS data sets for post-recovery subsequent breakdown events and the extent of defect clustering is found to be more intense after increasing number of recovery events. The breakdown mechanism in the stack is confirmed by measuring the resistance change trends with temperature. [Display omitted] •Multiple hard breakdown recovery events are observed in high-κ gate stacks during slow voltage ramp.•These breakdown and recovery events predominantly happen in the same filament location for multiple cycles.•Statistics of the voltage to first and successive breakdowns follows the clustering model, not Weibull distribution.•Cluster factor reduces in value for increasing number of breakdown and recovery cycles due to filament wear-out.•Filament in hard breakdown mode still comprises of oxygen vacancies rather than a metallic filament for this stack.

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