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Reservoir Computing with Charge-Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering.

Matteo Farronato1, Piergiulio Mannocci1, Margherita Melegari1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano and IUNET, piazza L. da Vinci 32, Milano, 20133, Italy.

Advanced Materials (Deerfield Beach, Fla.)
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Summary

Novel two-dimensional (2D) semiconductor memory devices offer low-power, high-speed in-memory computing. Molybdenum disulfide (MoS2) charge-trap memory demonstrates promising neuromorphic applications, including pattern recognition with high accuracy.

Keywords:
2D semiconductorscharge-trap memoryneural networksneuromorphic engineeringreservoir computing

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Area of Science:

  • Materials Science and Engineering
  • Electrical Engineering
  • Computer Science

Background:

  • Conventional complementary metal-oxide-semiconductor (CMOS) processors face limitations in power consumption and speed for in-memory computing.
  • Two-dimensional (2D) semiconductors, like molybdenum disulfide (MoS2), offer atomic thickness, low-current operation, and 3D integration potential for advanced semiconductor devices.
  • Neuromorphic engineering and in-memory computing require novel memory devices that are fast, accurate, and energy-efficient.

Purpose of the Study:

  • To present a novel charge-trap memory (CTM) device utilizing a MoS2 channel for advanced computing applications.
  • To demonstrate the transistor operation, memory characteristics, and synaptic plasticity of the MoS2-based CTM.
  • To evaluate the device's potential for neuromorphic computing through pattern recognition tasks.

Main Methods:

  • Fabrication of a charge-trap memory (CTM) device with a MoS2 channel.
  • Characterization of transistor performance, memory retention, and endurance.
  • Demonstration of synaptic potentiation and depression using controlled electrical pulses.
  • Implementation of reservoir computing for pattern recognition using the MoS2-based CTMs.

Main Results:

  • The MoS2-based CTM exhibits memory operation through electron trapping/detrapping at interface states.
  • Outstanding linearity in synaptic potentiation was observed with applied drain pulses of equal amplitude.
  • Successful pattern recognition was achieved via reservoir computing, processing input patterns through MoS2-CTM stimulation.
  • The device demonstrated good accuracy, low current operation, and robustness against input random bit flips.

Conclusions:

  • The MoS2-based CTM is a promising technology for future high-density neuromorphic computing.
  • The device's characteristics are well-suited for low-power, fast, and accurate in-memory computing applications.
  • This work highlights the potential of 2D materials in advancing next-generation computing paradigms.