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Updated: Sep 27, 2025

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Ferroelectric thin-film transistor (FeTFT) synaptic arrays offer efficient compute-in-memory (CIM) for convolutional neural networks (CNNs). These three-terminal devices overcome limitations of two-terminal CIM, enabling accurate image recognition with high accuracy.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Convolutional neural networks (CNNs) excel at image recognition using convolution operations.
  • Conventional computing struggles with the intensive multiplication-accumulation tasks of CNNs.
  • Existing compute-in-memory (CIM) solutions using two-terminal devices face challenges like leakage current and high power consumption.

Purpose of the Study:

  • To develop an efficient CIM architecture for CNNs using ferroelectric thin-film transistors (FeTFTs).
  • To address the limitations of two-terminal CIM devices in parallel data processing for image recognition.

Main Methods:

  • Demonstration of integrated FeTFT synaptic arrays for parallel programming and data processing.
  • Utilizing the selective control of polarization in the ferroelectric layer for accurate operations.
  • Employing three-terminal FeTFTs as both nonvolatile memory and access devices.

Main Results:

  • FeTFT synaptic arrays enable efficient parallel programming and data processing for CNNs.
  • The three-terminal FeTFT design mitigates issues associated with two-terminal devices.
  • The integrated array successfully performed convolution operations for image feature extraction.

Conclusions:

  • Integrated FeTFT synaptic arrays provide a promising solution for efficient CIM in CNNs.
  • This approach enhances image recognition accuracy while potentially reducing power consumption.
  • FeTFTs offer a viable path towards next-generation AI hardware acceleration.