An Enhanced Floating Gate Memory for the Online Training of Analog Neural Networks

被引：1

作者：

Gan, Lurong ^{[1
]}

Wang, Chen ^{[1
]}

Chen, Lin ^{[1
]}

Zhu, Hao ^{[1
]}

Sun, Qingqing ^{[1
]}

Zhang, David Wei ^{[1
]}

机构：

[1] Fudan Univ, Sch Microelect, State Key Lab ASIC & Syst, Shanghai 200433, Peoples R China

来源：

IEEE JOURNAL OF THE ELECTRON DEVICES SOCIETY | 2020年 / 8卷 / 01期

关键词：

Neural network; FG memory; U-shaped channel; erasing speed; online training; SYNAPSES;

D O I：

10.1109/JEDS.2020.2964820

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：

Floating gate (FG) memory has long erasing time, which limits its application as an electronic synapse in online training. This paper proposes a novel enhanced floating gate memory (EFM) by TCAD simulation. Here, three other structures are simulated just for comparison. The simulation results show that the erasing speed is about 34ns while the other three need the time over 1.8ms, which makes the operation speed of long-term potentiation (LTP) more symmetrical to long-term depression (LTD). In addition, both LTP and LTD are approximately linear in the simulation results. The speed, linearity, and symmetry of weight update are the keys to online training of analog neural networks. These excellent performances indicated a potential application of EFM in analog neuro-inspired computing.

引用

页码：84 / 91

页数：8

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