神经形态工程学
材料科学
非阻塞I/O
记忆电阻器
生物相容性材料
可扩展性
异质结
MNIST数据库
突触
纳米技术
电阻式触摸屏
电阻随机存取存储器
光电子学
非易失性存储器
电铸
人工神经网络
电子工程
仿真
切换时间
耐久性
降级(电信)
晶体管
电压
CMOS芯片
数码产品
电气工程
等离子体子
工作(物理)
计算机体系结构
生物电子学
路径(计算)
块(置换群论)
物联网
理论(学习稳定性)
可重构性
德拉姆
极性(国际关系)
逻辑门
计算机科学
作者
Xiuqing Cao,Wenfei Li,Qingqing Zheng,Juan Meng,Leilei Yang,L. B. Wang,Yuyang Huang,Shoulei Xu,Wen Deng
标识
DOI:10.1021/acsami.5c14332
摘要
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 Cs3Bi2Br9/NiO heterostructure memristor capable of mimicking biological synaptic functions with exceptional robustness. By engineering a heterostructure with a NiO layer, ion migration in Cs3Bi2Br9 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 Cs3Bi2Br9/NiO memristor exhibits excellent stability, impressive memory retention time (>7 × 103 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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