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Cited 17 time in webofscience Cited 18 time in scopus
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Impact of HfO2 Dielectric Layer Placement in Hf0.5Zr0.5O2-Based Ferroelectric Tunnel Junctions for Neuromorphic Applications

Authors
Kim, JuriPark, YongjinLee, JungwooLim, EunjinLee, Jung-KyuKim, Sungjun
Issue Date
May-2024
Publisher
Wiley-VCH GmbH
Keywords
ferroelectric tunnel junction; Hf0.5Zr0.5O2; HfO2 dielectric layer; reservoir computing; sneak current
Citation
Advanced Materials Technologies, v.9, no.10, pp 1 - 8
Pages
8
Indexed
SCIE
SCOPUS
Journal Title
Advanced Materials Technologies
Volume
9
Number
10
Start Page
1
End Page
8
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/21982
DOI
10.1002/admt.202400050
ISSN
2365-709X
2365-709X
Abstract
The use of Hf0.5Zr0.5O2 (HZO) films within hafnia-based ferroelectric tunnel junctions (FTJ) presents a promising avenue for next-generation non-volatile memory devices. HZO exhibits excellent ferroelectric properties, ultra-thinness, low power consumption, nondestructive readout, and compatibility with silicon devices. In this study, Mo/HZO/n(+) Si devices are investigated, incorporating a 1 nm HfO2 dielectric layer at the top and bottom of the HZO ferroelectric layer. Comparing the FTJ device configurations, it is observed that the metal-ferroelectric-dielectric-semiconductor (MFIS) outperforms the metal-dielectric-ferroelectric-semiconductor (MIFS) in terms of ferroelectricity, displaying a high 2P(r) value of approximate to 69 mu C cm(-2). Additionally, MFIS exhibits lower leakage current, higher tunneling electro-resistance ratio, and a thin dead layer during short pulse switching, as confirmed through DC double sweeping of I-V characteristics. The modified half-bias scheme demonstrates a maximum array size of 191 for MFIS, showcasing its superior performance over MIFS. Synaptic characteristics, including potentiation, depression, paired-pulse facilitation, spike-rate-dependent plasticity, and excitatory postsynaptic current, are measured using MFIS, highlighting its outstanding ferroelectric properties. As a physical reservoir, the FTJ device implements 16 states of 4 bits in reservoir computing. Finally, pattern recognition using a deep learning neural network achieves high accuracy with using the Modified National Institute of Standards and Technology dataset.
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