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Cited 29 time in webofscience Cited 28 time in scopus
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Ferroelectric synaptic devices based on CMOS-compatible HfAlOx for neuromorphic and reservoir computing applicationsopen access

Authors
Kim, DahyeKim, JihyungYun, SeokyeonLee, JungwooSeo, EunchoKim, Sungjun
Issue Date
May-2023
Publisher
Royal Society of Chemistry
Keywords
Aluminum Compounds; Cmos Integrated Circuits; Ferroelectricity; Tunnel Junctions; Atomic Radius; Cmos Compatibility; Cmos Compatible; Computing Applications; Condition; Ferroelectric Tunnel Junctions; Low Power; Memristor; Neuromorphic Computing; Reservoir Computing; Hafnium Oxides; Article; Facilitation; Feasibility Study; Polarization; Spike; Synapse; Thickness
Citation
Nanoscale, v.15, no.18, pp 8366 - 8376
Pages
11
Indexed
SCIE
SCOPUS
Journal Title
Nanoscale
Volume
15
Number
18
Start Page
8366
End Page
8376
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/21217
DOI
10.1039/d3nr01294h
ISSN
2040-3364
2040-3372
Abstract
The hafnium oxide-based ferroelectric tunnel junction (FTJ) has been actively researched because of desirable advantages such as low power and CMOS compatibility to operate as a memristor. In the case of HfAlOx (HAO), the remanent polarization (P-r) value is high and the atomic radius of Al is smaller than that of Hf; therefore, ferroelectricity can be better induced without mechanical force. In this paper, we propose an FTJ using HAO as a ferroelectric layer through electrical analysis and experiments; further, we experimentally demonstrate its capability as a synaptic device. Moreover, we evaluate the maximum 2P(r) and TER value of the device according to the difference in conditions of thickness and cell area. The optimized device conditions are analyzed, and a large value of 2P(r) (>similar to 43 mu C cm(-2)) is obtained. Furthermore, we show that paired-pulse facilitation, paired-pulse depression, and spike-timing-dependent plasticity can be utilized in HAO-based FTJs. In addition, this study demonstrates the use of an FTJ device as a physical reservoir to implement reservoir computing. Through a series of processes, the synaptic properties of FTJs are verified for the feasibility of their implementation as an artificial synaptic device.
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