Ultrathin All-Solid-State MoS2-Based Electrolyte Gated Synaptic Transistor with Tunable Organic–Inorganic Hybrid Film

Jungyeop Oh, Seohak Park, Sang Hun Lee, Sungkyu Kim, Hyeonji Lee, Changhyeon Lee, Woonggi Hong, Jun Hwe Cha, Mingu Kang, Jun Hyup Jin, Sung Gap Im, Min Ju Kim, Sung Yool Choi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Electrolyte-gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi-state storage capabilities. To demonstrate high-density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D-based EGSTs has challenges in achieving large-area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large-scale process-compatible, all-solid-state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub-30 nm organic-inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD-based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system-level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%.

Original languageEnglish
Article number2308847
JournalAdvanced Science
Volume11
Issue number23
DOIs
StatePublished - 19 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Advanced Science published by Wiley-VCH GmbH.

Keywords

  • artificial synapse
  • deep neural network
  • electrolyte gated transistor
  • initiated chemical vapor deposition
  • molybdenum disulfide

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