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Logic-in-memory based on an atomically thin semiconductor

Author

Listed:
  • Guilherme Migliato Marega

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Yanfei Zhao

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Ahmet Avsar

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Zhenyu Wang

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Mukesh Tripathi

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Aleksandra Radenovic

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Andras Kis

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

The growing importance of applications based on machine learning is driving the need to develop dedicated, energy-efficient electronic hardware. Compared with von Neumann architectures, which have separate processing and storage units, brain-inspired in-memory computing uses the same basic device structure for logic operations and data storage1–3, thus promising to reduce the energy cost of data-centred computing substantially4. Although there is ample research focused on exploring new device architectures, the engineering of material platforms suitable for such device designs remains a challenge. Two-dimensional materials5,6 such as semiconducting molybdenum disulphide, MoS2, could be promising candidates for such platforms thanks to their exceptional electrical and mechanical properties7–9. Here we report our exploration of large-area MoS2 as an active channel material for developing logic-in-memory devices and circuits based on floating-gate field-effect transistors (FGFETs). The conductance of our FGFETs can be precisely and continuously tuned, allowing us to use them as building blocks for reconfigurable logic circuits in which logic operations can be directly performed using the memory elements. After demonstrating a programmable NOR gate, we show that this design can be simply extended to implement more complex programmable logic and a functionally complete set of operations. Our findings highlight the potential of atomically thin semiconductors for the development of next-generation low-power electronics.

Suggested Citation

  • Guilherme Migliato Marega & Yanfei Zhao & Ahmet Avsar & Zhenyu Wang & Mukesh Tripathi & Aleksandra Radenovic & Andras Kis, 2020. "Logic-in-memory based on an atomically thin semiconductor," Nature, Nature, vol. 587(7832), pages 72-77, November.
  • Handle: RePEc:nat:nature:v:587:y:2020:i:7832:d:10.1038_s41586-020-2861-0
    DOI: 10.1038/s41586-020-2861-0
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    Citations

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    Cited by:

    1. Rong Bao & Shuiyuan Wang & Xiaoxian Liu & Kejun Tu & Jingquan Liu & Xiaohe Huang & Chunsen Liu & Peng Zhou & Shen Liu, 2024. "Neuromorphic electro-stimulation based on atomically thin semiconductor for damage-free inflammation inhibition," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Jun Yu & Han Wang & Fuwei Zhuge & Zirui Chen & Man Hu & Xiang Xu & Yuhui He & Ying Ma & Xiangshui Miao & Tianyou Zhai, 2023. "Simultaneously ultrafast and robust two-dimensional flash memory devices based on phase-engineered edge contacts," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Xinyu Chen & Yufeng Xie & Yaochen Sheng & Hongwei Tang & Zeming Wang & Yu Wang & Yin Wang & Fuyou Liao & Jingyi Ma & Xiaojiao Guo & Ling Tong & Hanqi Liu & Hao Liu & Tianxiang Wu & Jiaxin Cao & Sitong, 2021. "Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Chungryeol Lee & Changhyeon Lee & Seungmin Lee & Junhwan Choi & Hocheon Yoo & Sung Gap Im, 2023. "A reconfigurable binary/ternary logic conversion-in-memory based on drain-aligned floating-gate heterojunction transistors," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Zhuiri Peng & Lei Tong & Wenhao Shi & Langlang Xu & Xinyu Huang & Zheng Li & Xiangxiang Yu & Xiaohan Meng & Xiao He & Shengjie Lv & Gaochen Yang & Hao Hao & Tian Jiang & Xiangshui Miao & Lei Ye, 2024. "Multifunctional human visual pathway-replicated hardware based on 2D materials," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Seokhyeong Lee & Ruoming Peng & Changming Wu & Mo Li, 2022. "Programmable black phosphorus image sensor for broadband optoelectronic edge computing," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    7. Lu Li & Qinqin Wang & Fanfan Wu & Qiaoling Xu & Jinpeng Tian & Zhiheng Huang & Qinghe Wang & Xuan Zhao & Qinghua Zhang & Qinkai Fan & Xiuzhen Li & Yalin Peng & Yangkun Zhang & Kunshan Ji & Aomiao Zhi , 2024. "Epitaxy of wafer-scale single-crystal MoS2 monolayer via buffer layer control," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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