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A Fully Printed Flexible MoS 2 Memristive Artificial Synapse with Femtojoule Switching Energy
Author(s) -
Feng Xuewei,
Li Yida,
Wang Lin,
Chen Shuai,
Yu Zhi Gen,
Tan Wee Chong,
Macadam Nasiruddin,
Hu Guohua,
Huang Li,
Chen Li,
Gong Xiao,
Chi Dongzhi,
Hasan Tawfique,
Thean Aaron VoonYew,
Zhang YongWei,
Ang KahWee
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.201900740
Subject(s) - neuromorphic engineering , memristor , materials science , resistive random access memory , printed electronics , nanotechnology , non volatile memory , switching time , optoelectronics , computer science , electrical engineering , voltage , inkwell , artificial neural network , engineering , artificial intelligence , composite material
Realization of memristors capable of storing and processing data on flexible substrates is a key enabling technology toward “system‐on‐plastics”. Recent advancements in printing techniques show enormous potential to overcome the major challenges of the current manufacturing processes that require high temperature and planar topography, which may radically change the system integration approach on flexible substrates. However, fully printed memristors are yet to be successfully demonstrated due to the lack of a robust printable switching medium and a reliable printing process. An aerosol‐jet‐printed Ag/MoS 2 /Ag memristor is realized in a cross‐bar structure by developing a scalable and low temperature printing technique utilizing a functional molybdenum disulfide (MoS 2 ) ink platform. The fully printed devices exhibit an ultra‐low switching voltage (0.18 V), a high switching ratio (10 7 ), a wide range of tuneable resistance states (10–10 10 Ω) for multi‐bit data storage, and a low standby power consumption of 1 fW and a switching energy of 4.5 fJ per transition set. Moreover, the MoS 2 memristor exhibits both volatile and non‐volatile resistive switching behavior by controlling the current compliance levels, which efficiently mimic the short‐term and long‐term plasticity of biological synapses, demonstrating its potential to enable energy‐efficient artificial neuromorphic computing.