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
Staff Research Scientist

I am currently a staff research scientist and tech lead in the Meta On-Device AI team with a focus on researching and productizing efficient AI algorithms and hardwares for next generation AR/VR devices. I received my Ph.D. degree in the Department of Electrical and Computer Engineering, University of Texas at Austin under the supervision of Prof. David Z. Pan and my bachelor degree in Peking University under the supervision of Prof. Ru Huang and Prof. Runsheng Wang. My research interests include efficient and secure AI algorithms and systems.

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