Tri-Gate Ferroelectric FET Characterization and Modelling for Online Training of Neural Networks at Room Temperature and 233K

被引：13

作者：

De, Sourav ^{[1
]}

Baig, Md Aftab ^{[1
]}

Qiu, Bo-Han ^{[1
]}

Lu, Darsen ^{[1
]}

Sung, Po-Jung ^{[2
,3
]}

Hsueh, FuK ^{[2
]}

Lee, Yao-Jen ^{[2
]}

Su, Chun-Jung ^{[2
]}

机构：

[1] Natl Cheng Kung Univ, Dept Elect Engn, Tainan, Taiwan

[2] Taiwan Semicond Res Inst, Hsinchu, Taiwan

[3] Natl Chiao Tung Univ, Dept Electrophys, Hsinchu, Taiwan

来源：

2020 DEVICE RESEARCH CONFERENCE (DRC) | 2020年

关键词：

D O I：

10.1109/drc50226.2020.9135186

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：2