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A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing.

Jingon Jang1, Sanggyun Gi2, Injune Yeo2

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Uniform titanium oxide (TiOx) memristor arrays enable energy-efficient neuromorphic computing. This in situ training method accelerates AI processing, reducing iterations and energy consumption for high-accuracy classification.

Keywords:
artificial synapseshardware implementationmemristorsneuromorphic computinguniformity

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Neuromorphic hardware systems are crucial for energy-efficient big data processing and AI.
  • Memristor-based systems offer a promising approach for hardware neural networks.
  • Titanium oxide (TiOx) memristors are key components for these systems.

Purpose of the Study:

  • To fabricate uniform and reliable TiOx memristor array devices.
  • To design and implement an in situ convolutional neural network hardware system using these memristors.
  • To demonstrate efficient in situ training with learning rate modulation.

Main Methods:

  • Fabrication of multiple 25 × 25 TiOx memristor arrays.
  • Development of a global constant voltage programming scheme for crossbar arrays.
  • Implementation of in situ training with direct memristor operation and learning rate modulation.

Main Results:

  • Achieved high device uniformity (threshold uniformity ≈2.7%) and yield (>99%).
  • Demonstrated superior memristor performance including repetitive stability (≈3000 spikes) and ambient stability (6 months).
  • Enabled fast-converging in situ training with five times fewer iterations and reduced energy consumption, achieving ≈95.2% classification accuracy.

Conclusions:

  • Uniform TiOx memristor arrays are suitable for energy-efficient neuromorphic hardware.
  • The developed in situ training method significantly reduces training iterations and energy.
  • This approach advances the realization of practical AI hardware systems.