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A 2D material-based floating gate device with linear synaptic weight update.

Eunpyo Park1, Minkyung Kim, Tae Soo Kim

  • 1Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul, 02792, South Korea. jykwak@kist.re.kr.

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|December 15, 2020
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Summary
This summary is machine-generated.

Researchers developed a novel transistor-type synaptic device using 2D materials for neuromorphic computing. This device shows linear weight updates and emulates spike timing-dependent plasticity, crucial for artificial intelligence.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Neuromorphic computing aims to overcome the limitations of traditional von Neumann architectures.
  • Synaptic devices are essential for neuromorphic systems, requiring linear and symmetric weight updates.
  • Transistor-type devices offer separated read/write operations, preventing sneak path currents in synaptic functions.

Purpose of the Study:

  • To fabricate and characterize a transistor-type synaptic device for neuromorphic applications.
  • To demonstrate linear and repeatable synaptic weight updates using 2D materials.
  • To emulate spike-timing-dependent plasticity (STDP) for artificial neural network learning.

Main Methods:

  • Fabrication of a top-gated flash memory device utilizing MoS2 as the channel and graphene as the floating gate.
  • Incorporation of Al2O3 and HfO2 dielectric layers to enhance tunneling efficiency.
  • Experimental demonstration of weight update linearity and repeatability using electrical pulsing.

Main Results:

  • The fabricated device exhibited linear and symmetric weight updates in response to applied voltage pulses.
  • Repeatable synaptic weight modulation was achieved through repeated application of negative and positive pulses.
  • Successful emulation of spike-timing-dependent plasticity (STDP) learning rules was demonstrated.

Conclusions:

  • The developed 2D material-based synaptic device shows promise for efficient neuromorphic computing.
  • The device's linear weight updates and STDP emulation are critical for building advanced spiking neural networks.
  • This work contributes to the advancement of hardware for next-generation artificial intelligence systems.