Effect of I/O oxide process optimization on the nbti dependence of Tinv scaling for a 20 nm bulk planar Replacement Gate process
Waikoloa, HI（2014）
IBM Microelectron.
Abstract
We present results on the beneficial effect of an additional thermal treatment on the NBTI aging of an I/O thick oxide process in 20nm Replacement Metal Gate (RMG) High-k Metal Gate (HKMG) technology. It is shown that for an as-grown thermal thick oxide gate process, the NBTI induced device threshold voltage shift (ΔVth) scales as Tinv-2 when stressed at a given gate voltage Vg. On the other hand, the ΔVth dependence on Vg is ΔVth ~ (Vg)4 for a given Tinv. These findings seem to be apparently inconsistent with well known power law NBTI dependence on Eox (~ (Vg/Tinv)). The expected NBTI dependence on Eox (ΔVth ~ (Vg/Tinv)4) is recovered when the as-grown thermal thick oxide is treated with an additional high temperature anneal. We have evaluated the NBTI induced ΔVth time evolution, its recovery behavior, temperature and voltage dependence under DC and AC bias stress conditions with and without the oxide thermal treatment. Our observations over the two gate stack processes (as-grown I/O oxide with and without anneal treatment) support the NBTI physical picture of two uncorrelated contributions to the NBTI damage. Namely, shallow hole trap activation in process induced pre-existing (before stress) traps as well as deep hole traps and/or interface states generation. The last component is quasi permanent and is dominant in both gate stack processes. In particular, for the as-grown I/O oxide the NBTI damage is mainly due to an increase contribution of the quasi permanent component with increasing Tinv. This unexpected Tinv dependence is reduced by the additional anneal treatment. The implication of these findings on the NBTI characterization and modeling will be discussed.
MoreTranslated text
Key words
MOSFET,high-k dielectric thin films,hole traps,interface states,negative bias temperature instability,rapid thermal annealing,semiconductor device reliability,AC bias stress conditions,DC bias stress conditions,I/O oxide process optimization,NBTI aging,NBTI dependence,NBTI induced device threshold voltage shift,RMG HKMG technology,as-grown thermal thick oxide gate process,bulk planar replacement gate process,gate stack processes,high temperature annealling,high-k metal gate technology,interface state generation,p-MOSFET reliability,process induced pre-existing traps,quasipermanent component,recovery behavior,shallow hole trap activation,size 20 nm,thermal treatment,voltage dependence,Bias temperature instability (BTI),High-k Replacement Metal Gate Transistor (RMG),NBTI dependence on Eox,technology qualification,thermal anneal,thick oxide process
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined