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Various Threshold Switching Devices for Integrate and Fire Neuron Applications
Author(s) -
Lee Donguk,
Kwak Myonghoon,
Moon Kibong,
Choi Wooseok,
Park Jaehyuk,
Yoo Jongmyung,
Song Jeonghwan,
Lim Seokjae,
Sung Changhyuck,
Banerjee Writam,
Hwang Hyunsang
Publication year - 2019
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201800866
Subject(s) - neuromorphic engineering , artificial neuron , materials science , threshold voltage , spice , neuron , voltage , computer science , power consumption , artificial neural network , optoelectronics , power (physics) , electronic engineering , transistor , electrical engineering , physics , neuroscience , artificial intelligence , engineering , quantum mechanics , biology
This study demonstrates an integrate and fire (I&F) neuron using threshold switching (TS) devices to implement spike‐based neuromorphic system. An I&F neuron can be realized using the hysteric voltage switch characteristics of a TS device. To investigate the effects of various TS devices on neuron behavior, neurons are compared using three different types of TS device: NbO 2 ‐based insulator‐to‐metal transition (IMT) device, B–Te‐based ovonic threshold switching device, and Ag/HfO 2 ‐based atomic‐switching TS device. The results show that the off‐state resistance and switching time of the TS devices determine the leaky/nonleaky characteristics and types of activation function of neuron, respectively. In addition, it is confirmed that the threshold voltage and on‐state resistance of the TS device determine the total power consumption of neuron. Furthermore, the feasibility of the TS devices in a spiking neural network is verified by simulation program with integrated circuit emphasis (SPICE) simulation. This work indicates applicability of TS‐based I&F neuron in neuromorphic hardware application.