A Compact Fully Ferroelectric-FETs Reservoir Computing Network With Sub-100 ns Operating Speed

被引：25

作者：

Tang, Mingfeng ^{[1
]}

Zhan, Xuepeng ^{[1
]}

Wu, Shuhao ^{[1
]}

Bai, Maoying ^{[1
]}

Feng, Yang ^{[1
]}

Zhao, Guoqing ^{[1
]}

Wu, Jixuan ^{[1
]}

Chai, Junshuai ^{[2
]}

Xu, Hao ^{[2
]}

Wang, Xiaolei ^{[2
]}

Chen, Jiezhi ^{[1
]}

机构：

[1] Shandong Univ, Sch Informat Sci & Engn ISE, Qingdao 266237, Peoples R China

[2] Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China

来源：

IEEE ELECTRON DEVICE LETTERS | 2022年 / 43卷 / 09期

基金：

中国国家自然科学基金;

关键词：

FeFETs; Reservoirs; Training; Task analysis; Performance evaluation; Switches; Software packages; Ferroelectric FET; full-connection; HZO; reservoir computing;

D O I：

10.1109/LED.2022.3188496

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：

Reservoir computing (RC) is a low-cost and temporary-signal friendly computational framework, whose hardware implementation is hindered by integrating huge amounts and various kinds of devices. Benefitted from the logic in memory (LIM) capability, the process compatible Hf0.5Zr0.5O2 (HZO)-based ferroelectric field-effect-transistor (FeFET) is a promising candidate for implementing artificial networks. In this letter, a fully FeFETs RC network is proposed. Multiple functions can be achieved in a single device owing to its intrinsic characteristics, and the richness of virtual nodes is largely enhanced through full-connection structures. Impressively, only 44 FeFETs are required to construct a compact RC network with 100 ns operating speed and high accuracy in classification tasks. This paves the way to develop the high energy-efficiency FeFET RC networks.

引用

页码：1555 / 1558

页数：4

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