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Related Experiment Video

Updated: Jul 31, 2025

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Reconfigurable Artificial Synapse Based on Ambipolar Floating Gate Memory.

Chengdong Yao1, Guangcheng Wu1, Mingqiang Huang2

  • 1Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan 410082, China.

ACS Applied Materials & Interfaces
|May 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel artificial synapse using tungsten selenide/hexagonal boron nitride/molybdenum telluride. This device mimics brain functions, enabling reconfigurable excitatory and inhibitory synaptic behaviors for efficient neuromorphic computing.

Keywords:
ambipolar floating gate memoryexcitatory and inhibitory responseheterostructure devicereconfigurable artificial synapsetwo-dimensional material

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computer Engineering

Background:

  • Artificial synapse networks offer potential for massively parallel computing and improved information processing efficiency.
  • Semiconductor devices acting as excitatory and inhibitory synapses are vital for intelligence systems, but achieving reconfigurability in a single transistor is challenging.

Purpose of the Study:

  • To develop a single artificial synapse device capable of mimicking both excitatory and inhibitory synaptic behaviors.
  • To explore the potential of two-dimensional (2D) materials in creating reconfigurable and bilingual synaptic functionalities.

Main Methods:

  • Fabrication of an artificial synapse using an ambipolar floating gate memory structure based on tungsten selenide (WSe2)/hexagonal boron nitride (h-BN)/molybdenum telluride (MoTe2).
  • Utilized WSe2 and MoTe2 as ambipolar semiconductors for the channel and floating gate, with h-BN as the tunneling barrier.
  • Employed positive and negative pulse amplitude modulations at the control gate to induce distinct resistance states.

Main Results:

  • The WSe2/h-BN/MoTe2 device demonstrated bipolar channel conduction and achieved eight distinct resistance states.
  • Experimentally projected capability of 490 memory states (210 hole-resistance states + 280 electron-resistance states).
  • Successfully mimicked reconfigurable excitatory and inhibitory synaptic plasticity within a single device.
  • A convolution neural network utilizing these synaptic devices achieved >92% accuracy in handwritten digit recognition.

Conclusions:

  • The study highlights the unique properties of 2D material-based heterostructure devices for neuromorphic computing.
  • The developed artificial synapse demonstrates significant potential for advanced recognition tasks and efficient information processing.