神经形态工程学
记忆电阻器
仿真
材料科学
人工神经网络
冯·诺依曼建筑
计算机科学
光电子学
电压
电容
电导
突触重量
长时程增强
钙钛矿(结构)
可塑性
电子工程
突触可塑性
人工智能
突触
人工神经元
峰值时间相关塑性
纳米技术
物理
电流
神经促进
电网
电阻随机存取存储器
作者
Tong Chen,Xia Xiao,Mengting Dong,Xiaofan Zhou,Ying Li,Jiajun Guo,Dongke Li
标识
DOI:10.1021/acsaelm.5c01418
摘要
Inspired by the highly parallel information processing of biological neural networks, neuromorphic computing based on artificial synapses offers a promising route to overcome the von Neumann bottleneck. Here, we report a flexible optoelectronic memristor employing two-dimensional (C6H5CH2NH3)2CuBr4 perovskite, which simultaneously supports electrical and optical stimuli. The device exhibits bidirectional and multilevel conductance modulation, emulating key synaptic functions including pairedpulse facilitation (PPF), spiketimingdependent plasticity (STDP), spikedurationdependent plasticity (SDDP), and long-term potentiation and depression (LTP, LTD). Ultraviolet illumination lowers SET/RESET voltages and induces photopairedpulse facilitation and cumulative conductance potentiation, demonstrating photoinduced synaptic plasticity. Besides, the memristor maintains stable switching characteristics over 1000 electrical cycles, 104 s retention, 500 bending cycles, and twoweek exposure at 50% relative humidity, underscoring its environmental and mechanical robustness. Finally, a 7 × 7 memristor array network is shown via simulation to perform unsupervised image learning with high fidelity. These results highlight a versatile, leadfree perovskite platform for flexible, lowpower, multimodal neuromorphic hardware.
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