Characterization of Random Telegraph Noise in Scaled High-κ/Metal-Gate MOSFETs with SiO2/HfO2 Gate Dielectrics

Abstract

In the paper, random telegraph noise (RTN) in high-κ/metal-gate MOSFETs is investigated. The RTN in high-κ MOSFETs is found different compared to that in SiON MOSFETs, and faces challenges in characterization. Therefore, the characterization method is improved based on clustering and Hidden Markov Model, which greatly enhances the ability to extract RTN with non-negligible “ghost noise” in high-κ MOSFETs. The RTN signal and “ghost noise” in devices fabricated by two SiO2/HfO2 stack processes with two different formation methods are compared. It is found that the real RTN signal in SiO2/HfO2 MOSFETs originates from the oxide defects in the HfO2 layer, while the “ghost noise” originates from the SiO2 interfacial layer and has strong dependence on the quality and formation process of interfacial layer.

Publication
In ECS Transactions
Meng Li
Meng Li
Assistant Professor

I am currently a tenure-track assistant professor jointly affiliated with Institute for Artificial Intelligence and School of Integrated Circuits in Peking University. My research interests focus on efficient and secure multi-modality AI acceleration algorithms and hardwares.

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