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Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide.

Premlal Balakrishna Pillai1, Maria Merlyne De Souza1

  • 1Department of Electronic and Electrical Engineering, University of Sheffield-North Campus , S3 7HQ Sheffield, United Kingdom.

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Summary

Transparent artificial synaptic thin film transistors (TFTs) using zinc oxide and tantalum oxide demonstrate self-learning and signal transmission. These devices offer robust synaptic properties for advanced artificial neural networks.

Keywords:
memory TFTsoxygen vacanciessynaptic thin film transistorstantalum oxidezinc oxide

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Area of Science:

  • Materials Science
  • Neuroscience Engineering
  • Electronics

Background:

  • Artificial synaptic devices are crucial for developing brain-inspired computing.
  • Existing technologies face challenges in integrating signal transmission with learning capabilities and require high operating voltages.

Purpose of the Study:

  • To demonstrate artificial synaptic thin film transistors (TFTs) capable of simultaneous signal transmission and self-learning.
  • To utilize transparent, eco-friendly inorganic materials for enhanced synaptic device performance.

Main Methods:

  • Fabrication of TFTs using transparent zinc oxide (ZnO) and high κ tantalum oxide as gate insulator.
  • Investigation of gate polarity induced oxygen vacancy motion for synaptic behavior emulation.
  • Measurement of memory retention, memory window, and synaptic functions at low programming voltages.

Main Results:

  • Devices exhibit pronounced memory retention with a >4 V memory window using <6 V operating voltage.
  • Independent control of synaptic weight demonstrated without hampering source-drain signal transmission.
  • Synaptic functions emulated using a low programming voltage (200 mV), significantly lower than conventional technologies.
  • Robust synaptic properties achieved using fully transparent, eco-friendly inorganic materials.

Conclusions:

  • The developed transparent TFTs show promise for scalable synaptic devices, outperforming organic and liquid electrolyte-gated technologies.
  • Strong coupling between the gate and channel through ionic charge enables artificial neural networks with complex learning capabilities.
  • These advancements offer a pathway to alleviate component density requirements in brain-like systems.