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<p>The explosion of artificial intelligence and edge devices has exposed a critical bottleneck in traditional hardware: the slow data transfer between memory and processing. Content-addressable memories offer a promising solution by processing information directly within the memory, but existing implementations using static random-access memory and, more recently, those using emerging non-volatile memories are constrained by the performance of silicon transistors. Here we introduce an analogue content-addressable memory utilizing atomically thin two-dimensional MoS<sub>2</sub> flash memories with semimetal antimony contacts. Our device achieves a high read-out current (60 μA μm<sup>−1</sup>) and large ON/OFF ratios (&gt;10<sup>9</sup>) in two-dimensional flash memories. These breakthroughs have led to very low energy consumption (under 0.1 fJ per search per cell) and latency (36 ps) during analogue in-memory search operations within our 8 × 16 analogue content-addressable memory array, featuring 256 MoS<sub>2</sub> flash memory devices. We have also successfully demonstrated analogue Hamming distance computing for <i>k</i>-nearest neighbour classification, showcasing high accuracy, high energy efficiency and low latency for machine learning applications. This research highlights the transformative potential of two-dimensional materials in overcoming current hardware limitations, enabling more efficient and scalable computing solutions in intelligent edge devices.</p>

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Sb-contacted MoS2 flash memory for analogue in-memory searches

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Guoyun Gao,
  • Bo Wen,
  • Ni Yang,
  • Zhiyuan Du,
  • Mingrui Jiang,
  • Ruibin Mao,
  • Rui Qiu,
  • Yingnan Cao,
  • Hongxia Xue,
  • Deng Zou,
  • Pak San Yip,
  • Qihan Liu,
  • Yi Wan,
  • Dong-Keun Ki,
  • Jinyao Tang,
  • Paddy K. L. Chan,
  • Hao Jiang,
  • Han Wang,
  • Lain-Jong Li,
  • Can Li

摘要

The explosion of artificial intelligence and edge devices has exposed a critical bottleneck in traditional hardware: the slow data transfer between memory and processing. Content-addressable memories offer a promising solution by processing information directly within the memory, but existing implementations using static random-access memory and, more recently, those using emerging non-volatile memories are constrained by the performance of silicon transistors. Here we introduce an analogue content-addressable memory utilizing atomically thin two-dimensional MoS2 flash memories with semimetal antimony contacts. Our device achieves a high read-out current (60 μA μm−1) and large ON/OFF ratios (>109) in two-dimensional flash memories. These breakthroughs have led to very low energy consumption (under 0.1 fJ per search per cell) and latency (36 ps) during analogue in-memory search operations within our 8 × 16 analogue content-addressable memory array, featuring 256 MoS2 flash memory devices. We have also successfully demonstrated analogue Hamming distance computing for k-nearest neighbour classification, showcasing high accuracy, high energy efficiency and low latency for machine learning applications. This research highlights the transformative potential of two-dimensional materials in overcoming current hardware limitations, enabling more efficient and scalable computing solutions in intelligent edge devices.