TY - GEN
T1 - Solution Processable Mos2 Based Memristive Synapse for Brain Inspired Computing
AU - Li, Hanrui
AU - Kumar, Dayanand
AU - Elatab, Nazek
N1 - KAUST Repository Item: Exported on 2023-09-04
Acknowledged KAUST grant number(s): ORA2022-5314
Acknowledgements: Research supported by the Semiconductor Initiative, King Abdullah University of Science and Technology, Saudi Arabia (KAUST Research Funding (KRF) under Award No. ORA2022-5314).
PY - 2023/7/2
Y1 - 2023/7/2
N2 - As a novel 2D material, MoS 2 has shown excellent electrical properties and resistive switching characteristics to work as a switching layer for non-volatile memory. In this work, we drop cast the MoS 2 solution to prepare the thin film and deposit an interfacial layer of Al 2 O 3 . We demonstrate the proposed memristive device with Cu/Al 2 O 3 /MoS 2 /Pt structure to work as an artificial synapse. The device shows a steady resistive switching behavior with the SET and RESET voltages of 1.3 V and -0.5 V, respectively. We further demonstrate the synapse behavior via a Hopfield Neural Network (HNN) and achieve image recognition and reconstruction with a high accuracy of 96% after 15 training epochs.
AB - As a novel 2D material, MoS 2 has shown excellent electrical properties and resistive switching characteristics to work as a switching layer for non-volatile memory. In this work, we drop cast the MoS 2 solution to prepare the thin film and deposit an interfacial layer of Al 2 O 3 . We demonstrate the proposed memristive device with Cu/Al 2 O 3 /MoS 2 /Pt structure to work as an artificial synapse. The device shows a steady resistive switching behavior with the SET and RESET voltages of 1.3 V and -0.5 V, respectively. We further demonstrate the synapse behavior via a Hopfield Neural Network (HNN) and achieve image recognition and reconstruction with a high accuracy of 96% after 15 training epochs.
UR - http://hdl.handle.net/10754/694007
UR - https://ieeexplore.ieee.org/document/10231292/
U2 - 10.1109/nano58406.2023.10231292
DO - 10.1109/nano58406.2023.10231292
M3 - Conference contribution
BT - 2023 IEEE 23rd International Conference on Nanotechnology (NANO)
PB - IEEE
ER -