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April 16, 2026Advanced Composites and Hybrid Materials1 citationsOpen Access

All-MoS2 double floating-gate synaptic transistors for non-volatile memory and brain-inspired computing

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MNMuhammad Jawad NasimHKHonggyun KimDKDr. Muhammad Imran Khan

Key Points

  • The aim is to develop MoS2-based double floating-gate transistors for non-volatile memory and neuromorphic computing applications.
  • Developed a van der Waals multi-heterostructure of MoS2 for floating gate transistors.
  • Utilized oxygen plasma surface oxidation confirmed by transmission electron microscopy.
  • Reconfigured transistors into double-floating gate, single-floating gate, and non-floating gate architectures.
  • Conducted comparative studies on memory performance metrics and artificial synaptic behaviors using ANN simulations.
  • DFG devices showed a memory window greater than 150 V.
  • Achieved high current ON-OFF ratio around 10^6.
  • Demonstrated long retention time exceeding 6000 seconds and endurance over 5000 cycles.
  • Identified accuracy of ~94% on the MNIST dataset under ANN simulations.

Abstract

In recent years, the demand for efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two- dimensional (2D) materials. Nonetheless, floating gate devices have drawn widespread attention by virtue of their great potential for non-volatile memory. Herein, we present a van der Waals (vdW) multi-heterostructure of MoS2/MoS2/MoS2 for floating gate transistor to emulate its synaptic features. This architecture is established by surface oxidation via oxygen plasma which confirm by transmission electron microscopy (TEM). However, our devices are distinctly reconfigured to function as without (SiO2/MoS2), single-floating gate (SiO2/MoS2/MoOx/MoS2) and double-floating gate (SiO2/MoS2/MoOx/MoS2/MoOx/MoS2) transistors. While on comparative study, our double-floating gate (DFG) devices exhibit promising non-volatile memory performance metrics of memory window (> 150 V), high current ON-OFF ratio (∼106), long retention time (> 6000 s), and excellent endurance (> 5000 cycles) which attributed to the increased charge-storage capacity and spatial redistribution. Moreover, DFG devices are explored for artificial synaptic behavior such as LTP, LTD, STDP, SNDP, SADP, and PPF, enabling its applications in brain-inspired computing. In addition, by using the MNIST dataset, we achieved identification accuracy ~ 94% under ANN simulations. Our work provides the avenue for multi-mode neuromorphic computing devices to address the recent challenges of complex integration.

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Cite This Study

Nasim et al. (2026) studied this question.

synapsesocial.com/papers/69e07bc12f7e8953b7cbd6b2https://doi.org/10.1007/s42114-026-01762-2
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