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Physical Reservoir Computing Using Tellurium-Based Gate-Tunable Artificial Photonic Synapses.

Hyerin Jo1, Jiseong Jang2, Hyeon Jung Park3

  • 1Department of Physics and Integrative Institute of Basic Sciences, Soongsil University, Seoul 06978, Republic of Korea.

ACS Nano
|October 24, 2024
PubMed
Summary

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This summary is machine-generated.

Tellurium thin-film photonic synapses demonstrate advanced physical reservoir computing for digit recognition and solving nonlinear equations. These devices utilize persistent photoconductivity for efficient, low-cost neuromorphic applications.

Area of Science:

  • Optoelectronics
  • Materials Science
  • Neuromorphic Computing

Background:

  • Artificial synapses are crucial for developing advanced computing systems.
  • Tellurium (Te) thin films offer unique optoelectronic properties.
  • Persistent photoconductivity (PPC) in materials can be leveraged for memory functions.

Purpose of the Study:

  • To fabricate and characterize tellurium thin-film-based artificial photonic synapses.
  • To investigate the application of these photonic synapses in physical reservoir computing (PRC).
  • To demonstrate the device's effectiveness in complex computational tasks like digit recognition and solving nonlinear equations.

Main Methods:

  • Fabrication of Te thin-film photonic synapses using sputtering and spray-coating techniques.
Keywords:
MXenepersistent photocurrentphotonic synapsephysical reservoir computingtellurium

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  • Investigation of field-dependent persistent photoconductivity (PPC) and its response to gate bias.
  • Demonstration of synaptic characteristics (excitatory postsynaptic current, paired-pulse facilitation) under light stimulus.
  • Implementation of PRC for handwritten digit classification and solving second-order nonlinear equations using historical nodes.

    • Main Results:

    • Te-based photonic synapses exhibited excellent synaptic characteristics, including linear potentiation-depression and gate-tunable PPC.
    • Successful application in PRC for handwritten digit recognition with reduced computational cost.
    • High prediction accuracy in solving second-order nonlinear equations by exploiting nonlinear and fading memory properties.
    • Demonstrated synergetic integration of Te optoelectronic devices with neuromorphic computing principles.

    Conclusions:

    • Tellurium thin films are promising materials for artificial photonic synapses.
    • The developed photonic synapses enable efficient and effective physical reservoir computing.
    • This work advances the integration of optoelectronic devices with neuromorphic computing paradigms.