神经形态工程学
冯·诺依曼建筑
突触
计算机科学
人工神经网络
晶体管
材料科学
瓶颈
神经科学
人工智能
电气工程
工程类
电压
生物
嵌入式系统
操作系统
作者
Shuiyuan Wang,Xianghui Hou,Lan Liu,Jingyu Li,Yuwei Shan,Shiwei Wu,David Wei Zhang,Peng Zhou
出处
期刊:Research
[American Association for the Advancement of Science]
日期:2019-01-01
卷期号:2019: 1618798-1618798
被引量:37
标识
DOI:10.34133/2019/1618798
摘要
The von Neumann bottleneck has spawned the rapid expansion of neuromorphic engineering and brain-like networks. Synapses serve as bridges for information transmission and connection in the biological nervous system. The direct implementation of neural networks may depend on novel materials and devices that mimic natural neuronal and synaptic behavior. By exploiting the interfacial effects between MoS 2 and AlOx, we demonstrate that an h-BN-encapsulated MoS 2 artificial synapse transistor can mimic the basic synaptic behaviors, including EPSC, PPF, LTP, and LTD. Efficient optoelectronic spikes enable simulation of synaptic gain, frequency, and weight plasticity. The Pavlov classical conditioning experiment was successfully simulated by electrical tuning, showing associated learning behavior. In addition, h-BN encapsulation effectively improves the environmental time stability of our devices. Our h-BN-encapsulated MoS 2 artificial synapse provides a new paradigm for hardware implementation of neuromorphic engineering.
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