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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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HZO/HSO Superlattice ReFET Array Integrating Optical Sensing for Neuromorphic Vision Computing.

Bingjie Dang1, Kaixuan Sun1,2,3, Hanxin Su1,2,3

  • 1Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore.

Advanced Materials (Deerfield Beach, Fla.)
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel photonic resistive-gate field-effect transistor (ReFET) array for neuromorphic vision systems. The device integrates optical sensing, memory, and computation, overcoming limitations of conventional artificial synapses for enhanced in-sensor computing.

Keywords:
ReFETin‐sensor computingmemristorneuromorphic computingvision transformer

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Neuromorphic vision systems need artificial synapses for precise sensing, memory, and computation.
  • Conventional memristors and transistors have limitations in stability, endurance, and integration for these tasks.

Purpose of the Study:

  • To develop a novel device that integrates optical sensing, memory, and analog computation for neuromorphic applications.
  • To overcome the limitations of existing artificial synapse technologies.

Main Methods:

  • A photonic resistive-gate field-effect transistor (ReFET) array was fabricated using a Hf0.5Zr0.5O2/Hf0.95Sr0.05O2 (HZO/HSO) superlattice gate and an amorphous InGaZnO (IGZO) channel.
  • The device array was characterized for its memory states, endurance, retention, and ON/OFF ratios.
  • The array's performance as an in-sensor optical convolutional layer performing multiply-accumulate (MAC) operations was evaluated using the Fashion-MNIST dataset.

Main Results:

  • The ReFET array demonstrated 272 stable multilevel conductance states (>8-bit) with ON/OFF ratios >10⁶, endurance >1010 cycles, and retention >10⁶ s.
  • The array successfully performed in-sensor optical convolutional layer operations, achieving 94.45% accuracy on Fashion-MNIST with 8-bit quantized weights.
  • The platform exhibited high energy efficiency for neuromorphic computing tasks.

Conclusions:

  • The developed ReFET array offers a scalable, high-precision, and energy-efficient platform for photonic neuromorphic computing.
  • This integrated architecture successfully combines optical sensing, memory, and computation in a single device, advancing in-sensor computing capabilities.