Aaron Voon-Yew Thean

@article{ISI:000486813700001,
title = {A Fully Printed Flexible MoS_{2} Memristive Artificial Synapse with Femtojoule Switching Energy},
author = {Xuewei Feng and Yida Li and Lin Wang and Shuai Chen and Zhi Gen Yu and Wee Chong Tan and Nasiruddin Macadam and Guohua Hu and Li Huang and Li Chen and Xiao Gong and Dongzhi Chi and Tawfique Hasan and Aaron Voon-Yew Thean and Yong-Wei Zhang and Koh-Wee Ang},
doi = {10.1002/aelm.201900740},
times_cited = {6},
issn = {2199-160X},
year = {2019},
date = {2019-09-19},
journal = {ADVANCED ELECTRONIC MATERIALS},
volume = {5},
number = {12},
publisher = {WILEY},
address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
abstract = {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/MoS2/Ag memristor is realized in a cross-bar structure by developing a scalable and low temperature printing technique utilizing a functional molybdenum disulfide (MoS2) 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) omega) 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 MoS2 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}