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Visible-light modulated ferroelectric optoelectronic field-effect transistor for superior neuromorphic computing.

Zihang Zhu1, Jinyu Song2, Wenshuo Wu2

  • 1College of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China, Qingdao, 266071, China.

Nanotechnology
|July 6, 2026
PubMed
Summary

This study introduces a simple, visible-light-activated ferroelectric optoelectronic field-effect transistor (Fe-OFET) for neuromorphic computing. The device enables efficient visible-light-driven synaptic simulation and multilevel storage, advancing bionic vision hardware.

Keywords:
artificial synapsesferroelectric field-effect transistorsneuromorphic computingvisible-light modulation

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • High-performance neuromorphic computing and bionic vision demand simpler, low-power devices.
  • Traditional oxide semiconductors are limited to ultraviolet light, hindering visible-light applications.

Purpose of the Study:

  • To develop a visible-light-modulated ferroelectric optoelectronic field-effect transistor (Fe-OFET) for neuromorphic synapse simulation and multilevel storage.
  • To improve visible-light response and absorption efficiency in optoelectronic devices.

Main Methods:

  • An all-solution method was used to prepare a TiO2/IGZO/Al2O3/PZT based Fe-OFET.
  • The device's optoelectronic performance and ferroelectric non-volatility were characterized.
  • Synaptic plasticity functions like excitatory postsynaptic current (EPSC) and paired-pulse facilitation (PPF) were simulated.

Main Results:

  • The TiO2 layer extended the device's light current response to the red light band, improving overall visible-light absorption.
  • Stable ferroelectric non-volatility and optoelectronic performance were achieved.
  • High accuracies were obtained in neuromorphic tasks: 95.5% for target motion recognition (CNN) and 90.0% for MNIST recognition (hybrid ANN with MHA).

Conclusions:

  • The developed Fe-OFET offers a process-simple, visible-light-compatible solution for neuromorphic hardware.
  • This work provides significant support for integrating visual neuromorphic hardware and computing.