High-Performance Artificial Synapse Device Based on Cs <sub>3</sub> Bi <sub>2</sub> Br <sub>9</sub> /NiO Heterostructure for Bio-Inspired Neuromorphic Computing
The development of energy-efficient and biocompatible artificial synapses is essential to advance neuromorphic computing. Bismuth-based perovskites are promising candidates to replace toxic lead-based perovskites in resistive switching devices owing to their exceptional optoelectronic properties, high environmental friendliness, and stability. Here, we present a lead-free Cs<sub>3</sub>Bi<sub>2</sub>Br<sub>9</sub>/NiO heterostructure memristor capable of mimicking biological synaptic functions with exceptional robustness. By engineering a heterostructure with a NiO layer, ion migration in Cs<sub>3</sub>Bi<sub>2</sub>Br<sub>9</sub> is spatially confined, achieving a resistance switching change rate of less than 7.37% between cycles and enhanced long-term stability in ambient air (60 days). This Cs<sub>3</sub>Bi<sub>2</sub>Br<sub>9</sub>/NiO memristor exhibits excellent stability, impressive memory retention time (>7 × 10<sup>3</sup> s), durability (>100 cycles), good on/off ratio, and basic synaptic behavior. Furthermore, the training results of 200 data achieved an accuracy rate of 95.46% in the MNIST handwritten digit recognition task, which was superior to traditional analog neural networks. This work not only highlights the significant potential of lead-free perovskites for sustainable neuromorphic hardware but also provides a scalable preparation path for biocompatible electronics.
Authors
- Shoulei Xu (ORCID: https://orcid.org/0000-0002-1444-2534)
- L. B. Wang (ORCID: https://orcid.org/0000-0001-5559-4879)
- Wenfei Li (ORCID: https://orcid.org/0000-0001-7236-977X)
- Wen Deng (ORCID: https://orcid.org/0000-0002-4044-0284)
- Yuyang Huang
- Juan Meng (ORCID: https://orcid.org/0000-0002-6188-3172)
- Leilei Yang (ORCID: https://orcid.org/0000-0002-5082-2075)
- Xiuqing Cao
- Qingqing Zheng
Institutions
- Wannan Medical College (CN)
- Minzu University of China (CN)
- Guangxi University (CN)
- Guangxi University of Science and Technology (CN)
- Center For Remote Sensing (United States) (US)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-10-25
- DOI
- https://doi.org/10.1021/acsami.5c14332
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19