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Dielectric Engineered Two-Dimensional Neuromorphic Transistors.

Du Xiang1,2, Tao Liu3, Xumeng Zhang1

  • 1Frontier Institute of Chip and System, Fudan University, Shanghai 200438, China.

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|April 9, 2021
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Summary
This summary is machine-generated.

Researchers developed a 2D transition metal dichalcogenide synaptic array on a silicon nitride substrate. This scalable neuromorphic hardware achieves 91% accuracy on the MNIST dataset, advancing 2D materials for computing.

Keywords:
ANN simulationTMDC-based synaptic arrayanalogue nonvolatile memorylow cycle-to-cycle and device-to-device variabilitysilicon-rich silicon nitride substrate

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Two-dimensional (2D) materials offer planar-wafer compatibility for neuromorphic computing.
  • Current 2D artificial synapses are limited to single-device implementations, hindering scalable array development.
  • Complementary metal-oxide-semiconductor (CMOS) compatibility is crucial for integrating 2D materials into neuromorphic hardware.

Purpose of the Study:

  • To develop a scalable 2D synaptic array with CMOS compatibility.
  • To demonstrate the performance and reliability of 2D artificial synapses for neuromorphic applications.
  • To explore the potential of 2D transition metal dichalcogenides in next-generation computing architectures.

Main Methods:

  • Fabrication of a 2D transition metal dichalcogenide-based synaptic array on a silicon-rich silicon nitride (sr-SiN) substrate.
  • Characterization of array performance, including analogue on/off ratio, conductance linearity, and variability.
  • Evaluation of the synaptic array's performance on the MNIST handwritten dataset for pattern recognition.

Main Results:

  • The fabricated array exhibited uniform performance with a high analogue on/off ratio and linear conductance update.
  • Low cycle-to-cycle (1.5%) and device-to-device (5.3%) variability were achieved, essential for neuromorphic hardware.
  • The artificial synapses demonstrated a 91% recognition accuracy on the MNIST handwritten dataset.

Conclusions:

  • A simple and industry-compatible approach for fabricating 2D synaptic arrays using sr-SiN dielectric was presented.
  • The developed 2D synaptic array shows significant promise for implementing large-scale neuromorphic hardware.
  • This work establishes a new paradigm for utilizing 2D materials in advanced neuromorphic computing systems.