TY - GEN
T1 - Simulation and Investigation of 2D FeFET Synapse with Identical Pulse Scheme for Neuromorphic Applications
AU - Hsu, Yu Jen
AU - Luo, Yi Chin
AU - Chen, Yu Chen
AU - Fan, Che Lun
AU - Su, Pin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This work investigates the synapse response under the identical gate pulse stimulation scheme for Hf02-based ferroelectric FETs (FeFETs) with 2D channel using calibrated Monte-Carlo simulations with the nucleation-limited switching model. Our study suggests that a larger effective activation field (Ea) and a narrower spread in the distribution of Ea should be engineered to achieve a better conductance response for the FeFET synapse. In addition, adequately increasing the thickness of the interfacial layer and the ferroelectric layer of the FeFET may facilitate the accumulative switching and improve the synapse response. Besides, we have shown that it is possible to tune the conductance response by back-gating, and an adequately applied negative back-gate bias may result in better linearity and symmetry. Our study may provide insights for the FeFET synapse design crucial to the accuracy and performance of neuromorphic computing.
AB - This work investigates the synapse response under the identical gate pulse stimulation scheme for Hf02-based ferroelectric FETs (FeFETs) with 2D channel using calibrated Monte-Carlo simulations with the nucleation-limited switching model. Our study suggests that a larger effective activation field (Ea) and a narrower spread in the distribution of Ea should be engineered to achieve a better conductance response for the FeFET synapse. In addition, adequately increasing the thickness of the interfacial layer and the ferroelectric layer of the FeFET may facilitate the accumulative switching and improve the synapse response. Besides, we have shown that it is possible to tune the conductance response by back-gating, and an adequately applied negative back-gate bias may result in better linearity and symmetry. Our study may provide insights for the FeFET synapse design crucial to the accuracy and performance of neuromorphic computing.
UR - http://www.scopus.com/inward/record.url?scp=85141055038&partnerID=8YFLogxK
U2 - 10.1109/SNW56633.2022.9889009
DO - 10.1109/SNW56633.2022.9889009
M3 - Conference contribution
AN - SCOPUS:85141055038
T3 - 2022 IEEE Silicon Nanoelectronics Workshop, SNW 2022
BT - 2022 IEEE Silicon Nanoelectronics Workshop, SNW 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Silicon Nanoelectronics Workshop, SNW 2022
Y2 - 11 June 2022 through 12 June 2022
ER -