@inproceedings{5d4a401f8bd4429e9a884d6d801b3a50,
title = "Tri-Gate Ferroelectric FET Characterization and Modelling for Online Training of Neural Networks at Room Temperature and 233K",
abstract = "This paper reports detailed analysis on switching dynamics and device variability over a wide range of temperatures for deeply scaled (40nm gate length) tri-gate ferroelectric FETs with 10nm HZO fabricated using gate first process on SOI wafers. Our experimental results manifest, 99% ferroelectric switching at room temperature and at 233K. A memory window over 5V and strong gate length dependence of memory window is observed. Highly linear and symmetric multilevel switching characteristics makes our ferroelectric FETs suitable for neuromorphic applications, as demonstrated with neural network online training simulations.",
author = "Sourav De and Baig, {Md Aftab} and Qiu, {Bo Han} and Darsen Lu and Sung, {Po Jung} and Hsueh, {Fu K.} and Lee, {Yao Jen} and Su, {Chun Jung}",
note = "Funding Information: Acknowledgments: This work was supported by Ministry of Science and Technology (MOST) in Taiwan, under grant MOST-108-2634-F-006-08. We are grateful to Taiwan Semiconductor Research Institute for nanofabrication facilities and services, and Dr. Wen-Jay Lee and Nan-Yow Chen of National Center for High-Performance Computing for advices on AI computation.; 2020 Device Research Conference, DRC 2020 ; Conference date: 21-06-2020 Through 24-06-2020",
year = "2020",
month = jun,
doi = "10.1109/DRC50226.2020.9135186",
language = "English",
series = "Device Research Conference - Conference Digest, DRC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 Device Research Conference, DRC 2020",
address = "United States",
}