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Volatile and Nonvolatile Programmable Iontronic Memristor with Lithium Imbued TiO for Neuromorphic Computing

Rabiul Islam1,2, Yu Shi1,2, Gabriel Vinicius de Oliveira Silva1,2

  • 1Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada.

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Summary

This study presents a novel lithium-infused titanium dioxide iontronic device that mimics synaptic short-term plasticity for neuromorphic computing. The device offers tunable memory and high accuracy in voice recognition tasks.

Keywords:
Li-ion doped TiOxartificial synapsesiontronicsneuromorphic computingreservoir computingvolatile memristors

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Neuromorphic computing aims to mimic brain functionalities.
  • Existing devices often require complex fabrication or operational conditions.
  • Developing efficient hardware for simulating neural plasticity is crucial.

Purpose of the Study:

  • To demonstrate a lithium-infused titanium dioxide iontronic device with inherent synapse-like short-term plasticity.
  • To explore the device's potential for neuromorphic computing applications.
  • To investigate the tunability and stability of the device's memory characteristics.

Main Methods:

  • Fabrication of a lithium (Li) imbued TiO2 iontronic device using LiPON solid-state electrolyte.
  • Characterization of short-term plasticity phenomena (paired pulse facilitation, post-tetanic potentiation).
  • Evaluation of device performance in a voice recognition task.

Main Results:

  • The device exhibits volatile, synapse-like short-term plasticity without a forming process or compliance current.
  • Tunable memory time scales and self-relaxation characteristics were observed.
  • Achieved 94.4% accuracy in voice recognition using the device as a neuromorphic training platform.

Conclusions:

  • The Li-infused TiO2 iontronic device provides a versatile platform for neuromorphic computing, enabling both volatile reservoir and nonvolatile memory functions.
  • The device's inherent plasticity and tunable characteristics are suitable for processing temporal information.
  • This technology offers a promising approach for efficient hardware implementation of artificial neural networks.