Robust Resistive Switching in Solution-Processed Copper Bromide Binary Metal Halide System
JOURNAL OF ALLOYS AND COMPOUNDS（2025）
Abstract
Resistive switching devices utilizing metal halide materials hold great promise for flexible resistive randomaccess memory (RRAM) due to their low fabrication costs and low processing temperatures. However, the commercialization of these materials is hindered by issues related to phase instability and stoichiometric complexity. In this study, we demonstrate a binary metal halide-based resistive switching memory using copper bromide (CuBr) as the active component and that showed remarkable stability and reliability. The Au/CuBr/Au memristor exhibited a stable on/off ratio of 5 x 10 1 during low-voltage operation between 1.4 V to -1.6 V, a significant achievement for data storage devices. Detailed analysis using X-ray photoemission spectroscopy (XPS) provided insights into the chemical states and electronic structures of the CuBr layer, revealing that the resistive switching mechanism is driven by the formation of a metallic copper ion-based conductive filament (CF). This CF formation explains the area-dependent resistances and temperature-dependent resistances and is further supported by impedance spectroscopy. The devices also displayed exceptional switching stability, maintaining data integrity for more than 300 days at room temperature under ambient conditions. The device also exhibited impressive endurance, withstanding more than 1.2 x 104 cycles, highlighting its potential for long-term operational stability. Additionally, the air stability of the CuBr memristors and their low-temperature solution processing make them suitable for practical memory applications. In conclusion, this research provides a robust solution to the challenges faced by halide perovskite-based memristors, offering a highly stable, ambientcondition-resistant, and low-cost memory device. The work lays the foundation for next-generation memory devices that are both durable and environmentally adaptable, contributing significantly to the advancement of memory storage technologies.
MoreTranslated text
Key words
Copper bromide,Room-temperature,Resistive memory,Metal halide,Conductive filament (CF)
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