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Self-assembled vapor-transport-deposited SnS nanoflake-based memory devices with synaptic learning properties

Authors
Khot, Atul C.Pawar, Pravin S.Dongale, Tukaram D.Nirmal, Kiran A.Sutar, Santosh S.Deepthi Jayan, K.Mullani, Navaj B.Kumbhar, Dhananjay D.Kim, Yong TaePark, Jun HongHeo, JaeyeongKim, Tae Geun
Issue Date
Mar-2024
Publisher
Elsevier B.V.
Keywords
Density functional theory; Resistive switching; Synaptic learning; Time-series analysis; Vapor-transport-deposited tin-sulfide
Citation
Applied Surface Science, v.648
Indexed
SCOPUS
Journal Title
Applied Surface Science
Volume
648
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/68808
DOI
10.1016/j.apsusc.2023.158994
ISSN
0169-4332
1873-5584
Abstract
The most salient features of resistive switching (RS) devices are low energy consumption, fast switching speed, and high-density integration, which render them promising candidates for realizing non-volatile memory and artificial synaptic devices. However, the growth of functional switching layers for RS devices needs innovative deposition techniques. Herein, we utilize a high-throughput vapor-transport-deposition (VTD) technique for synthesizing self-assembled tin-sulfide (SnS) nanoflakes, which are then used as a switching layer to fabricate an RS device. First principle calculations are conducted to understand the optoelectronic properties of SnS by employing density functional theory. The proposed Ag/SnS/Pt memory device exhibits substantial merits, including low-switching voltages (VSET: 0.22 V and VRESET: −0.20 V), suitable ON/OFF ratio (∼259), excellent endurance (106), and extended memory retention (106 s) characteristics. In addition, RS stochasticity is modeled using statistical time-series analysis via Holt's exponential smoothing. Interestingly, the device can emulate multiple synaptic functionalities, including potentiation, depression, paired-pulse facilitation, paired-pulse depression, excitatory postsynaptic current, inhibitory postsynaptic current, and advanced spike-timing dependent plasticity rules. Moreover, the proposed synaptic device can detect the edge of images by utilizing a convolutional neural network. The unique and efficient VTD-SnS-based device will be a potential candidate for high-density non-volatile memory and neuromorphic computing applications. © 2023 Elsevier B.V.
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