Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La<sub>0.1</sub>Bi<sub>0.9</sub>FeO<sub>3</sub> Ferroelectric Tunnel Junctions
Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La<sub>0.1</sub>Bi<sub>0.9</sub>FeO<sub>3</sub>/La<sub>0.7</sub>Sr<sub>0.3</sub>MnO<sub>3</sub> ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.
Authors
- Pan Gao (ORCID: https://orcid.org/0000-0002-6238-0358)
- Caihong Jia (ORCID: https://orcid.org/0000-0002-4723-9524)
- Weifeng Zhang (ORCID: https://orcid.org/0000-0002-4514-4281)
- Guanghong Yang (ORCID: https://orcid.org/0000-0002-2755-747X)
- Mengyuan Duan
Institutions
- Henan University (CN)
- Henan Academy of Sciences (CN)
Publication Details
- Journal
- Nano Letters
- Published
- 2024-08-22
- DOI
- https://doi.org/10.1021/acs.nanolett.4c01924
- Citations
- 4
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.83
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Henan Province
- Key Scientific Research Project of Colleges and Universities in Henan Province