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

Atul C. Khot, Pravin S. Pawar, Tukaram D. Dongale, Kiran A. Nirmal, Santosh S. Sutar, K. Deepthi Jayan, Navaj B. Mullani, Dhananjay D. Kumbhar, Yong Tae Kim, Jun Hong Park, Jaeyeong Heo, Tae Geun Kim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Article number158994
JournalApplied Surface Science
Volume648
DOIs
Publication statusPublished - 2024 Mar 1

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Density functional theory
  • Resistive switching
  • Synaptic learning
  • Time-series analysis
  • Vapor-transport-deposited tin-sulfide

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Surfaces and Interfaces
  • Surfaces, Coatings and Films

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