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In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition

Ojun Kwon1, Seyoung Oh1, Heejeong Park1

  • 1Department of Advanced Material Engineering, Chungbuk National University, Chungdaero 1, Cheongju, 28644, Korea (the Republic of).

Nanotechnology
|February 9, 2022
PubMed
Summary

This study introduces a novel Indium Gallium Zinc Oxide (IGZO) synaptic device for neuromorphic systems. Optimized pulse conditions achieved a 94.06% accuracy in handwritten digit recognition, highlighting the importance of parameter tuning.

Keywords:
IGZO floating gate synaptic transistorMNIST pattern recognition accuracycycle variationlinearitysynaptic parameters

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Reliable conductance modulation in synaptic devices is crucial for high-performance neuromorphic computing.
  • Floating gate Indium Gallium Zinc Oxide (IGZO) synaptic devices with an aluminum trapping layer are proposed.

Purpose of the Study:

  • To investigate the correlation between electrical parameters of the IGZO synaptic device and pattern recognition accuracy.
  • To demonstrate fundamental synaptic properties and the impact of pulse tuning conditions on conductance update.

Main Methods:

  • Fabrication of a floating gate IGZO synaptic transistor with an aluminum trapping layer.
  • Systematic investigation of pulse tuning conditions (voltage, interval, duration, cycling number) on conductance.
  • Evaluation of synaptic properties including excitatory postsynaptic current, paired pulse facilitation, and memory effects.
  • Utilizing an artificial neural network simulator to assess pattern recognition accuracy on MNIST handwritten digits.

Main Results:

  • Demonstrated key synaptic functionalities: excitatory postsynaptic current, paired pulse facilitation, short/long-term memory, and long-term potentiation/depression.
  • Identified that both conductance update nonlinearity and cycle-to-cycle variation significantly impact pattern recognition accuracy.
  • Achieved a highest recognition rate of 94.06% for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset under optimized pulse conditions.

Conclusions:

  • A systematic study of synaptic parameters is essential for optimizing the performance of developed synapse devices.
  • The proposed IGZO synaptic device shows promise for neuromorphic applications, with performance highly dependent on precise control of electrical parameters.
  • Careful consideration of conductance nonlinearity and device-to-device variability is necessary for reliable pattern recognition in neuromorphic systems.