Ferroelectric polymer-based artificial synapse for neuromorphic computing

被引：80

作者：

Kim, Sungjun ^{[1
,2
]}

Heo, Keun ^{[2
]}

Lee, Sunghun ^{[2
]}

Seo, Seunghwan ^{[2
]}

Kim, Hyeongjun ^{[2
]}

Cho, Jeongick ^{[2
]}

Lee, Hyunkyu ^{[2
]}

Lee, Kyeong-Bae ^{[2
]}

Park, Jin-Hong ^{[2
]}

机构：

[1] Samsung Elect Co Ltd, Foundry Div, Yongin 17113, South Korea

[2] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea

来源：

NANOSCALE HORIZONS | 2021年 / 6卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

MEMORY; TRANSISTORS; NETWORK; DEVICE;

D O I：

10.1039/d0nh00559b

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far. Here, we investigated the effects by (i) the formation temperature of the ferroelectric material, poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) and (ii) the nature of the contactmetals (Ti, Cr, Pd) of the FeFET on the operating performance of a FeFET-based artificial synapse in terms of various synaptic performance indices. Excellent ferroelectric properties were induced by maximizing the size and coverage ratio of the b-phase domains by annealing the P(VDF-TrFE) film at 140 degrees C. A metal that forms a relatively high barrier improved the dynamic range and nonlinearity by suppressing the contribution of the tunneling current to the post-synaptic current. Subsequently, we studied the influence of the synaptic characteristics on the training and recognition tasks by using two MNIST datasets (fashion and handwritten digits) and the multi-layer perceptron concept of neural networks.

引用

页码：139 / 147

页数：9

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