Ferroelectric memristors based on double perovskite Bi2FeCoO6 for synaptic performance and human expression recognition storage
This study reports for the first time the application of double perovskite thin-film devices based on the Bi 2 FeCoO 6 (BFCO) compound in non-volatile ferroelectric memristors. By spin-coating BFCO onto an N-type silicon (N-Si) substrate, a P-N junction was formed, yielding a thin-film device with ferroelectric properties. The device demonstrated a maximum polarization value of 46.09 μC/cm² and a high switching ratio of 293, along with excellent long-term stability (over 7 days) and high repeatability (1000 cycles). Furthermore, we investigated the synaptic characteristics of the device, including short-term plasticity, paired-pulse facilitation, and long-term potentiation/inhibition behaviors. By designing a confusion matrix recognition scenario with a binary neural network, we validated the potential of double perovskite ferroelectric memristors in intelligent learning applications. Additionally, leveraging the synaptic plasticity of the device, we developed a modal storage memory and recognition system for human emotions. This work not only provides new insights into the development of high-performance double perovskite ferroelectric memristors but also lays the foundation for optimizing synaptic performance in intelligent learning applications.
Authors
- Qijun Sun (ORCID: https://orcid.org/0000-0003-2130-7389)
- Qiu‐Xiang Liu (ORCID: https://orcid.org/0000-0001-9821-6042)
- Wen‐Min Zhong
- Xin‐Gui Tang (ORCID: https://orcid.org/0000-0001-6429-2098)
- Yanping Jiang (ORCID: https://orcid.org/0000-0002-5789-9059)
- Dong-Ping Yang
- Jun Li (ORCID: https://orcid.org/0000-0003-1156-3645)
Institutions
- Guangdong University of Technology (CN)
- Key Laboratory of Guangdong Province (CN)
- Guangzhou Panyu Polytechnic (CN)
Publication Details
- Journal
- Materials Today Electronics
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1016/j.mtelec.2024.100133
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.42