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<p> <?tk 4?>Ferroelectric capacitive memories (FCMs) utilize ferroelectric polarization to modulate device capacitance for data storage, providing a new technological pathway to achieve two-terminal non-destructive-read ferroelectric memory. In contrast to the conventional resistive memories, the unique capacitive operation mechanism of FCMs transfers the memory reading and in-memory computing to charge domain, offering ultra-high energy efficiency, better compatibility to large-scale array, and negligible read disturbance. In recent years, extensive research has been conducted on FCMs. Various device designs were proposed and experimentally demonstrated with progressively enhanced performance, showing remarkable potential of the novel technology. This article summarizes several typical FCM devices by introducing their mechanisms, comparing their performance, and discussing their limitations. We further investigate the capacitive crossbar array operation and review the recent progress in the FCM integration and array-level demonstrations. In addition, we present the computing-in-memory applications of the FCMs to realize ultra-low-power machine learning acceleration for future computing systems.</p>

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Ferroelectric capacitive memories: devices, arrays, and applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月22日

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  • Zuopu Zhou,
  • Leming Jiao,
  • Zijie Zheng,
  • Yue Chen,
  • Kaizhen Han,
  • Yuye Kang,
  • Dong Zhang,
  • Xiaolin Wang,
  • Qiwen Kong,
  • Chen Sun,
  • Jiawei Xie,
  • Xiao Gong

摘要

Ferroelectric capacitive memories (FCMs) utilize ferroelectric polarization to modulate device capacitance for data storage, providing a new technological pathway to achieve two-terminal non-destructive-read ferroelectric memory. In contrast to the conventional resistive memories, the unique capacitive operation mechanism of FCMs transfers the memory reading and in-memory computing to charge domain, offering ultra-high energy efficiency, better compatibility to large-scale array, and negligible read disturbance. In recent years, extensive research has been conducted on FCMs. Various device designs were proposed and experimentally demonstrated with progressively enhanced performance, showing remarkable potential of the novel technology. This article summarizes several typical FCM devices by introducing their mechanisms, comparing their performance, and discussing their limitations. We further investigate the capacitive crossbar array operation and review the recent progress in the FCM integration and array-level demonstrations. In addition, we present the computing-in-memory applications of the FCMs to realize ultra-low-power machine learning acceleration for future computing systems.