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Reconfigurable Neuron and Synapse Operations in a Steep-Switching Nonvolatile Transistor.

Jongmin Noh1,2, Yeong Kwon Kim3, Seongkweon Kang4

  • 1SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, South Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel transistor for neuromorphic computing, enabling both neuron and synapse functions in a single device. This integrated approach enhances efficiency and scalability for artificial intelligence applications.

Keywords:
ferroelectric gating effectreconfigurable neuron‐synapsespiking neural networkspiking neuronsteep switching non‐volatile field‐effect transistor

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Neuromorphic systems aim to mimic biological neural networks but face integration challenges due to device dissimilarities.
  • Current architectures are often complex and inefficient, hindering scalability.

Purpose of the Study:

  • To develop a single device capable of both neuron and synapse emulation for improved neuromorphic system integration.
  • To overcome the limitations of current heterogeneous device approaches in neuromorphic computing.

Main Methods:

  • A steep-switching nonvolatile field-effect transistor based on a CuInP2S6/h-BN/WSe2 heterostructure was fabricated.
  • Electrostatically modulating the channel's carrier density and Fermi level enabled reconfigurable neuron and synapse modes.
  • Ferroelectric-gating effects were utilized to enhance channel chemical potential and reduce operating bias.

Main Results:

  • The device successfully demonstrated leaky-integrate-and-fire (LiF) neuron operation by controlling the Fermi level.
  • Synaptic mode was achieved by shifting the Fermi level towards the valence band, enabling weight-modulated functionality.
  • Device-to-system simulations showed 95.83% accuracy in human face recognition using a single integrated neuron-synapse system.

Conclusions:

  • The proposed heterostructure transistor offers a promising solution for cointegrated and scalable neuromorphic computing.
  • This technology facilitates efficient emulation of neural functions, paving the way for advanced AI hardware.