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<p>The fast development of artificial intelligence has called for high-efficiency neuromorphic computing hardware. While two-dimensional floating-gate memories show promise, their limited state numbers and stability hinder practical use. Here, we report gate-injection-mode two-dimensional floating-gate memories as a candidate for large-scale neural network accelerators. Through a coplanar device structure design and a bi-pulse state programming strategy, 8-bit states with intervals larger than three times of the standard deviations and stability over 10,000 s are achieved at 3 V. The cycling endurance is over 10<sup>5</sup> and the fabricated 256 devices show a yield of 94.9%. Leveraging this, we carry out experimental image convolutions and 38,592 kernels transplanting on an integrated 9 × 2 array that exhibits results matching well with simulations. We also show that fix-point neural networks with 8-bit precision have inference accuracies approaching the ideal values. Our work validates the potential of gate-injection-mode two-dimensional floating-gate memories for high-efficiency neuromorphic computing hardware.</p>

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8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月18日

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  • Yuchen Cai,
  • Jia Yang,
  • Yutang Hou,
  • Feng Wang,
  • Lei Yin,
  • Shuhui Li,
  • Yanrong Wang,
  • Tao Yan,
  • Shan Yan,
  • Xueying Zhan,
  • Jun He,
  • Zhenxing Wang

摘要

The fast development of artificial intelligence has called for high-efficiency neuromorphic computing hardware. While two-dimensional floating-gate memories show promise, their limited state numbers and stability hinder practical use. Here, we report gate-injection-mode two-dimensional floating-gate memories as a candidate for large-scale neural network accelerators. Through a coplanar device structure design and a bi-pulse state programming strategy, 8-bit states with intervals larger than three times of the standard deviations and stability over 10,000 s are achieved at 3 V. The cycling endurance is over 105 and the fabricated 256 devices show a yield of 94.9%. Leveraging this, we carry out experimental image convolutions and 38,592 kernels transplanting on an integrated 9 × 2 array that exhibits results matching well with simulations. We also show that fix-point neural networks with 8-bit precision have inference accuracies approaching the ideal values. Our work validates the potential of gate-injection-mode two-dimensional floating-gate memories for high-efficiency neuromorphic computing hardware.