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Integrated Artificial Neural Network with Trainable Activation Function Enabled by Topological Insulator-Based

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Summary
This summary is machine-generated.

This study demonstrates brain-like synaptic and neuronal functions in a novel Bi2Te3/CrTe2 heterostructure device. This innovation enables faster artificial neural network training and simplifies hardware design for complex algorithms.

Keywords:
anomalous Hall effectartificial neural networkspin−orbit torquetopological insulatorvan der Waals ferromagnet

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Area of Science:

  • Materials Science and Engineering
  • Device Physics
  • Computational Neuroscience

Background:

  • Nonvolatile memristors are promising for artificial neural networks (ANNs).
  • Integrating diverse functional and algorithmic blocks into single hardware systems presents significant challenges.
  • Existing ANN hardware often relies on complex software implementations for essential functions.

Purpose of the Study:

  • To demonstrate brain-like synaptic (SOT-S) and neuronal (SOT-N) functions in a single device.
  • To leverage these functions for efficient ANN hardware implementation.
  • To reduce system complexity and improve training performance in ANNs.

Main Methods:

  • Fabrication of a Bi2Te3/CrTe2 heterostructure-based spin-orbit torque (SOT) device.
  • Characterization of the SOT-S unit for synaptic plasticity (long-term potentiation/depression).
  • Integration of the SOT-N cell for neuronal activation functions.
  • Implementation of a serial-connected, voltage-mode sensing ANN architecture.
  • Training the MNIST dataset using the developed hardware.

Main Results:

  • The SOT-S unit exhibited highly linear and symmetrical potentiation/depression, achieving >90% accuracy on the MNIST dataset.
  • The SOT-N cell's inherent Sigmoid-shape transition replaced software activation functions, simplifying the ANN architecture.
  • The serial-connected ANN architecture enhanced signal strength with low reading error (0.61%) and simplified peripheral circuitry.
  • The trainable activation function of SOT-N enabled integrated Batch Normalization and activation operations, improving training performance.

Conclusions:

  • The Bi2Te3/CrTe2 heterostructure SOT device successfully integrates synaptic and neuronal functionalities.
  • This integrated approach significantly reduces ANN hardware complexity and enhances training efficiency.
  • The developed device platform offers a promising pathway for next-generation neuromorphic computing hardware.