High Performance Antimony-Rich RexSb3Te for Phase-Change Random Access Memory Applications
JOURNAL OF NON-CRYSTALLINE SOLIDS(2024)
Donghua Univ
Abstract
Phase-change random access memory (PCRAM) demands for high-performance phase change materials, including operation speed, data retention, resistance drift etc. In this work, a high performance PCRAM device based on antimony-rich RexSb3Te material was put forward and studied. Excellent data retention, fast operation speed, low resistance drift, low power consumption and good cycle characteristics were achieved. The lattice information, bonding properties and surface morphology of the films were characterized by transmission electron microscopy, Raman spectroscopy and atomic force microscopy. Density functional theory calculations show that the Re dopant is more inclined to replace Sb and forms chemical bonds with Te, and the Re-Te bonds are more stable than the previous Sb-Te bonds, which is conducive to the formation of stable precursors to accelerate nucleation. The research shows that the antimony-rich Re0.18Sb3Te material has good overall performance, which provides a strategy for developing high-performance memory.
MoreTranslated text
Key words
Phase-change random access memory,Antimony-rich,Rhenium doping,Fast operation speed,Low resistance drift,Thermal stability
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