神经形态工程学
材料科学
计算机科学
长时程增强
人工神经网络
人工智能
化学
生物化学
受体
作者
Jialin Meng,Tianyu Wang,Lin Chen,Qingqing Sun,Hao Zhu,Ji Li,Shi‐Jin Ding,Wenzhong Bao,Peng Zhou,David Wei Zhang
出处
期刊:Nano Energy
[Elsevier BV]
日期:2021-01-22
卷期号:83: 105815-105815
被引量:69
标识
DOI:10.1016/j.nanoen.2021.105815
摘要
The booming artificial intelligence has led to an urgent demand for high-efficient information processing systems. Inspired by the interconnected synapses in human brain, we constructed a novel low-dimensional flexible hybrid photoelectric-modulated artificial heterosynapse with sub-femtojoule energy consumption (0.58 fJ/spike in long-term potentiation (LTP) and 0.86 fJ/spike in long-term depression (LTD)) and ultrafast response (50 ns), which is 105 folds faster than human brain (10 ms). The device synergistically utilizes the remarkable photoelectric properties of a 2D MoSSe channel and a 0D BPQD trap layer to achieve a better computing architecture. The artificial synapse successfully emulates neuromorphic functions under both electric and light stimuli. More importantly, the multi-terminal heterosynaptic plasticity can be modulated effectively by three factors in a cumulative/subtractive way, resembling biological synapses affected by an external neuromodulator, enabling higher order LTP correlations (percentage of increase is ~203% compared with electric modulation) and multiple memory states. The performance of the device was unaffected by substrate bending, indicating the robust stability and high flexibility under mechanical strains. Moreover, the Pavlov’s dog classical conditioning experiments were performed to realize associative learning with the synaptic device. These results highlight a new approach for constructing highly efficient wearable neuromorphic computing systems based on mixed low-dimensional structures.
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