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.

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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
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article

High-Performance Artificial Synapse Device Based on Cs 3 Bi 2 Br 9 /NiO Heterostructure for Bio-Inspired Neuromorphic Computing

Shoulei Xu, L. B. Wang, Wenfei Li, Wen Deng et al.
3 citations
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
2.19
article

High-Performance Artificial Synapse Device Based on Cs 3 Bi 2 Br 9 /NiO Heterostructure for Bio-Inspired Neuromorphic Computing

Shoulei Xu, L. B. Wang, Wenfei Li, Wen Deng, Yuyang Huang, Juan Meng, Leilei Yang, Xiuqing Cao, Qingqing Zheng
article en
3 citations

Abstract

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.

ACS Applied Materials & InterfacesVol. 17(44)
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)
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
2.19
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