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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.

Jiachao Zhou1, Hanxi Li1, Ming Tian2

  • 1School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.

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|July 26, 2022
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Summary

Researchers developed a novel multiresponsive synapse device combining artificial synaptic and optical-sensing functions. This brain-inspired neuromorphic device achieves high precision in handwritten digit recognition, paving the way for integrated sensing and learning systems.

Keywords:
artificial synapsedendritic integrationheterostructurephotoswitching logictwo-dimensional materials

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Neuromorphic computing requires advanced artificial synaptic devices capable of complex functions beyond simple operations.
  • Integrating biomimetic sensing with synaptic plasticity is a critical research frontier for next-generation intelligent systems.

Purpose of the Study:

  • To develop and characterize a multiresponsive synapse device that merges synaptic and optical-sensing functionalities.
  • To demonstrate the device's potential for applications in brain-inspired intelligent systems and neuromorphic computing.

Main Methods:

  • Fabrication of vertically stacked graphene/h-BN/WSe2 heterostructures for synaptic and optical sensing.
  • Characterization of synaptic plasticity, response time, and optical responsivity.
  • Simulation of handwritten digit recognition using an unsupervised spiking neural network (SNN).

Main Results:

  • The developed synapse device exhibits excellent synaptic plasticity and a rapid response time of 3 μs.
  • Achieved high optical responsivity of 10^5 A/W.
  • Demonstrated 90.89% precision in handwritten digit recognition using an SNN, comparable to state-of-the-art results.
  • Successfully mimicked dendritic integration and photoswitching logic in multiterminal devices.

Conclusions:

  • The multiresponsive synapse device successfully integrates synaptic and optical-sensing functions, validating multifunctional capabilities.
  • This research supports the potential fusion of sensing and self-learning within neuromorphic networks.
  • The device shows promise for advancing brain-inspired intelligent systems and neuromorphic computing applications.