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ReS2 Charge Trapping Synaptic Device for Face Recognition Application.

Ze-Hui Fan1, Min Zhang1, Lu-Rong Gan1

  • 1State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433, China.

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PubMed
Summary
This summary is machine-generated.

Researchers developed a novel synaptic device using rhenium disulfide (ReS2) to emulate neural functions. This device achieved 100% accuracy in face recognition tasks, demonstrating its potential for artificial neural networks.

Keywords:
Artificial neural networkCharge trapping memorySynaptic deviceTwo-dimension material

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Synaptic devices are crucial for developing smarter and more efficient systems.
  • The demand for advanced computing systems necessitates novel synaptic device architectures.

Purpose of the Study:

  • To construct a synaptic device using anisotropic rhenium disulfide (ReS2) as the channel material.
  • To emulate synaptic behaviors like long-term potentiation/depression.
  • To evaluate the device's performance in large-scale artificial neural network (ANN) applications, specifically face recognition.

Main Methods:

  • Anisotropic rhenium disulfide (ReS2) was employed as the channel material for synaptic device fabrication.
  • The device's ability to emulate long-term potentiation/depression was demonstrated.
  • A three-layer ANN with over 10^5 weights was trained using 120 images from the Yale Face database.
  • 120 modulated conductance states were used to replace the ANN weights for face recognition testing on the remaining 45 images.

Main Results:

  • The ReS2-based synaptic device successfully emulated synaptic plasticity (long-term potentiation/depression).
  • The device facilitated a face recognition task using an ANN.
  • An outstanding 100% recognition rate was achieved with only 120 discrete conductance states.

Conclusions:

  • The anisotropic ReS2 synaptic device shows high potential for artificial neural network applications.
  • The device's performance indicates its suitability for large-scale neural network systems.
  • The study validates the use of ReS2 in developing efficient and high-performance neuromorphic computing hardware.