Examination of Ferroelectric Domain Dynamics in HZO under Endurance Cycling Stress
IEEE ELECTRON DEVICE LETTERS(2024)
Abstract
We investigate domain wall (DW) movement in hafnium zirconium oxide (HZO) under various temperature ( T ) and cycling stresses. It is demonstrated that cycling stress distinctly impacts the behavior in the DW relaxation, creep, and flow regimes. Specifically, cycling stress increases the energy barrier that DW must overcome to transition from the relaxation to creep regime. However, it has a negligible effect on the boundary condition of the electrical ( E )-field value between the creep and flow regimes. Consequently, a T-E phase diagram for HZO is presented, which distinctively delineates these regimes, and offers clear insights into the cycling stress-induced changes in ferroelectric DW dynamics.
MoreTranslated text
Key words
Creep,Switches,Stress,Iron,Current measurement,Dynamics,Boundary conditions,Hafnium zirconium oxide (HZO),ferroelectric tunnel junction (FTJ),switching mechanism,domain wall (DW)
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
Related Papers
2010
被引用105 | 浏览
1997
被引用45 | 浏览
2013
被引用44 | 浏览
2014
被引用154 | 浏览
2017
被引用171 | 浏览
2018
被引用170 | 浏览
2018
被引用227 | 浏览
2016
被引用641 | 浏览
2021
被引用17 | 浏览
2020
被引用16 | 浏览
2023
被引用12 | 浏览
2023
被引用8 | 浏览
2023
被引用13 | 浏览
2023
被引用4 | 浏览
2023
被引用4 | 浏览
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