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Charge-domain content addressable memory based on ferroelectric capacitive memory for reliable and energy-efficient one-shot learning

Author

Listed:
  • Zuopu Zhou

    (National University of Singapore)

  • Hongtao Zhong

    (Tsinghua University)

  • Leming Jiao

    (National University of Singapore)

  • Zijie Zheng

    (National University of Singapore)

  • Huazhong Yang

    (Tsinghua University)

  • Thomas Kämpfe

    (Fraunhofer IPMS)

  • Kai Ni

    (University of Notre Dame)

  • Xueqing Li

    (Tsinghua University)

  • Xiao Gong

    (National University of Singapore
    Technology and Research)

Abstract

Non-volatile content addressable memories (NV-CAMs) accelerate memory augmented neural networks (MANNs) for brain-like efficient learning from a few examples or even one example. However, most existing NV-CAMs operate in current domain, posing challenges in reliable, low-power, and sensing-friendly Hamming distance (HD) computation. To address these challenges, this work proposes transferring the computation to charge domain using ferroelectric capacitive memory (FCM). For the first time, a charge-domain 2FCM CAM based on the inversion-type FCM is reported. By storing data as device capacitance, this CAM structure directly outputs HD as linear multi-level voltages, enabling simplified sensing processes and reduced peripheral costs. Its differential nature further exhibits immunity to device variation, ensuring accuracy in the computation of long data vectors. Parallel 16-bit HD computation using a fabricated 16 × 16 2FCM CAM array is experimentally demonstrated with record performance at array level, evidencing the superiority of charge-domain computation and showcasing tremendous potential for in-memory-search applications.

Suggested Citation

  • Zuopu Zhou & Hongtao Zhong & Leming Jiao & Zijie Zheng & Huazhong Yang & Thomas Kämpfe & Kai Ni & Xueqing Li & Xiao Gong, 2025. "Charge-domain content addressable memory based on ferroelectric capacitive memory for reliable and energy-efficient one-shot learning," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63190-y
    DOI: 10.1038/s41467-025-63190-y
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    References listed on IDEAS

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    1. Yijun Li & Jianshi Tang & Bin Gao & Jian Yao & Anjunyi Fan & Bonan Yan & Yuchao Yang & Yue Xi & Yuankun Li & Jiaming Li & Wen Sun & Yiwei Du & Zhengwu Liu & Qingtian Zhang & Song Qiu & Qingwen Li & He, 2023. "Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Ruibin Mao & Bo Wen & Arman Kazemi & Yahui Zhao & Ann Franchesca Laguna & Rui Lin & Ngai Wong & Michael Niemier & X. Sharon Hu & Xia Sheng & Catherine E. Graves & John Paul Strachan & Can Li, 2022. "Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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