记忆电阻器
计算机科学
人工神经网络
神经形态工程学
冯·诺依曼建筑
人工神经元
人工智能
电气工程
工程类
操作系统
作者
Durjoy Dev,Adithi Krishnaprasad,Zhezhi He,Sonali Das,Mashiyat Sumaiya Shawkat,Madison Manley,Olaleye Aina,Deliang Fan,Yeonwoong Jung,Tania Roy
出处
期刊:Conference digest
日期:2019-06-01
卷期号:: 193-194
被引量:4
标识
DOI:10.1109/drc46940.2019.9046335
摘要
The phenomenal evolution of information and communication drives future technologies towards highly parallel, energy-efficient self-learning systems like the human brain. The limitations of current von Neumann computation systems have paved the way for artificial neural networks (ANN) to meet these criteria. The memristor has become an emerging candidate to realize ANN through emulating biological synapse and neuron behavior [1]-[3]. We previously reported an artificial neuron with 2D Mos2and graphene electrode, but the operating voltage was high, and the output current was low [4]. In this work, we harness threshold switching in Mos2enabled by Ag electrode, to emulate integration and firing behavior of neuron and demonstrate digit recognition application with these devices. The simple vertical structure of Ag/MoS2/ Au threshold switching memristor (TSM), with very low threshold voltage (Vth=0.4-0.5V), displays the four crucial features of neuron - all-or-nothing spiking, threshold-driven firing, post firing refractory period and stimulus strength based frequency response.
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