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 Tae; Park, Jun Hong; Heo, Jaeyeong; Kim, Tae Geun
- Issue Date
- Mar-2024
- Publisher
- Elsevier BV
- Keywords
- Density functional theory; Resistive switching; Synaptic learning; Time-series analysis; Vapor-transport-deposited tin-sulfide
- Citation
- Applied Surface Science, v.648
- Indexed
- SCIE
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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