Design-Technology Co-Optimizations for Symmetric Linear Synapse Behaviors in Ferroelectric FET Based Neuromorphic Computing

被引：1

作者：

Zhao, Guoqing ^{[1
]}

Wu, Shuhao ^{[1
]}

Zhan, Xuepeng ^{[1
]}

Tang, Mingfeng ^{[1
]}

Wei, Wei ^{[1
]}

Tai, Lu ^{[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, Qingdao 266237, Peoples R China

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

来源：

IEEE TRANSACTIONS ON NANOTECHNOLOGY | 2022年 / 21卷

基金：

中国国家自然科学基金;

关键词：

FeFETs; Switches; Logic gates; Depression; Zirconium; Temperature measurement; Nonvolatile memory; FeFET; domain switch; charge trapping; multilevel storage; reliability; neuromorphic computing;

D O I：

10.1109/TNANO.2022.3223183

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：

Benefitted from the strong compatibility and scalability, Hf0.5Zr0.5O2 (HZO) based ferroelectric field-effect transistor (FeFET) has gained extensive attention as artificial neuron and synapse. In this work, with in-depth understanding of the correlations between domain switching (DS) and charge trapping (CT), multi-states in FeFET are controlled precisely by modulating the channel conductance. When DS contribution dominates, cycle-to-cycle variations can be well suppressed in the intermediate storage states. With further co-optimizations by including CT and temperature impacts, symmetric linear conductance modulations and large conductance ratios are achieved simultaneously. The biological synaptic potentiation and depression characteristics demonstrate the great potential of HZO FeFET in neuromorphic computing.

引用

页码：747 / 751

页数：5

共 2 条

[1] Design-Technology Co-Optimizations (DTCO) for General-Purpose Computing In-Memory Based on 55nm NOR Flash Technology
Feng, Yang
Chen, Bing
Liu, Jing
Sun, Zhaohui
Hu, Hongyang
Zhang, Junyu
Zhan, Xuepeng
Chen, Jiezhi
2021 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM), 2021,
[2] Design-Technology Co-Optimization for NVM-Based Neuromorphic Processing Elements
Song, Shihao
Balaji, Adarsha
Das, Anup
Kandasamy, Nagarajan
ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 2022, 21 (06)

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