TY - JOUR
T1 - An Artificial Neuron Using a Bipolar Electrochemical Metallization Switch and Its Enhanced Spiking Properties through Filament Confinement
AU - Kim, Taehyun
AU - Kim, Seung Hwan
AU - Park, Jae Hyeun
AU - Park, June
AU - Park, Euyjin
AU - Kim, Seung Geun
AU - Yu, Hyun Yong
N1 - Funding Information:
T.K. and S.‐H.K. contributed equally to this work. This work was supported in part by the Nano·Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2016M3A7B4910426), in part by the Basic Science Research Program within the Ministry of Science, ICT, and Future Planning through the NRF of Korea under Grant (2020R1A2C2004029), and in part by the NRF of Korea Grant funded by the Ministry of Science and ICT for Original Technology Program (No. 2020M3F3A2A01082329).
Publisher Copyright:
© 2020 Wiley-VCH GmbH
PY - 2021/1
Y1 - 2021/1
N2 - Neural networks composed of artificial neurons and synapses mimicking the human nervous system have received much attention because of their promising potential in future computing systems. In particular, spiking neural networks (SNNs), which are faster and more energy-efficient than conventional artificial neural networks, have recently been the focus of attention. However, because typical neural devices for SNNs are based on complementary metal-oxide-semiconductors that exhibit high consumption of power and require a large area, it is difficult to use them to implement a large-scale network. Thus, a new structure should be developed to overcome the typical problems that have been encountered and to emulate bio-realistic functions. This study proposes a versatile artificial neuron based on the bipolar electrochemical metallization threshold switch, which exhibits four requisite characteristics for a spiking neuron: all-or-nothing spiking, threshold-driven spiking, refractory period, and strength-modulated frequency. Furthermore, unique features such as an inhibitory postsynaptic potential and the bipolar switching characteristic for changing synaptic weight are realized. Additionally, by using a filament confinement technique, a high on/off ratio (≈6 × 107), a low threshold voltage (0.19 V), low variability (0.014), and endurance over 106 cycles are achieved. This research will serve as a stepping-stone for advanced large-scale neuromorphic systems.
AB - Neural networks composed of artificial neurons and synapses mimicking the human nervous system have received much attention because of their promising potential in future computing systems. In particular, spiking neural networks (SNNs), which are faster and more energy-efficient than conventional artificial neural networks, have recently been the focus of attention. However, because typical neural devices for SNNs are based on complementary metal-oxide-semiconductors that exhibit high consumption of power and require a large area, it is difficult to use them to implement a large-scale network. Thus, a new structure should be developed to overcome the typical problems that have been encountered and to emulate bio-realistic functions. This study proposes a versatile artificial neuron based on the bipolar electrochemical metallization threshold switch, which exhibits four requisite characteristics for a spiking neuron: all-or-nothing spiking, threshold-driven spiking, refractory period, and strength-modulated frequency. Furthermore, unique features such as an inhibitory postsynaptic potential and the bipolar switching characteristic for changing synaptic weight are realized. Additionally, by using a filament confinement technique, a high on/off ratio (≈6 × 107), a low threshold voltage (0.19 V), low variability (0.014), and endurance over 106 cycles are achieved. This research will serve as a stepping-stone for advanced large-scale neuromorphic systems.
KW - bipolar threshold switches
KW - electrochemical metallization
KW - filament confinement
KW - neuromorphic computing
KW - spiking neurons
UR - http://www.scopus.com/inward/record.url?scp=85096831214&partnerID=8YFLogxK
U2 - 10.1002/aelm.202000410
DO - 10.1002/aelm.202000410
M3 - Article
AN - SCOPUS:85096831214
VL - 7
JO - Advanced Electronic Materials
JF - Advanced Electronic Materials
SN - 2199-160X
IS - 1
M1 - 2000410
ER -