TY - GEN
T1 - An RRAM with a 2D material embedded double switching layer for neuromorphic computing
AU - Chen, Po An
AU - Ge, Rui Jing
AU - Lee, Jia Wei
AU - Hsu, Chun Hsiang
AU - Hsu, Wei Chou
AU - Akinwande, Deji
AU - Chiang, Meng Hsueh
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/8
Y1 - 2019/1/8
N2 - Resistive random-access memory (RRAM) has shown great potential for neuromorphic engineering, due to its ability of emulating neural network and simple structure. To mimic the brain-learning behavior, two types of neural actions, short-term plasticity (STP) and long-term potentiation (LTP), should be imitated perfectly. In this work, we propose a unique RRAM cell with a double switching layer, in which a 2D material is embedded as a separation layer. Within a proper voltage range of stress, the mobile oxygen ions are blocked by the single atomic layer, and hence the subsequent relaxation of oxygen ions leads to a volatile switching characteristic. Owing to this volatile characteristic, the proposed device can mimic neural actions, STP and LTP, by a simple pulse train with different repetitions and frequencies without the complicated pulse settings of spike-timing-dependent plasticity (STDP). For various learning algorithms in future brain-inspired applications, different switching materials with different bind energies and relaxation times of oxygen ions can be utilized.
AB - Resistive random-access memory (RRAM) has shown great potential for neuromorphic engineering, due to its ability of emulating neural network and simple structure. To mimic the brain-learning behavior, two types of neural actions, short-term plasticity (STP) and long-term potentiation (LTP), should be imitated perfectly. In this work, we propose a unique RRAM cell with a double switching layer, in which a 2D material is embedded as a separation layer. Within a proper voltage range of stress, the mobile oxygen ions are blocked by the single atomic layer, and hence the subsequent relaxation of oxygen ions leads to a volatile switching characteristic. Owing to this volatile characteristic, the proposed device can mimic neural actions, STP and LTP, by a simple pulse train with different repetitions and frequencies without the complicated pulse settings of spike-timing-dependent plasticity (STDP). For various learning algorithms in future brain-inspired applications, different switching materials with different bind energies and relaxation times of oxygen ions can be utilized.
UR - http://www.scopus.com/inward/record.url?scp=85061775347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061775347&partnerID=8YFLogxK
U2 - 10.1109/NMDC.2018.8605915
DO - 10.1109/NMDC.2018.8605915
M3 - Conference contribution
AN - SCOPUS:85061775347
T3 - 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018
BT - 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE Nanotechnology Materials and Devices Conference, NMDC 2018
Y2 - 14 October 2018 through 17 October 2018
ER -