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Heterogeneous integration of 2D memristor arrays and silicon selectors for compute-in-memory hardware in convolutional neural networks

Author

Listed:
  • Samarth Jain

    (National University of Singapore)

  • Sifan Li

    (National University of Singapore)

  • Haofei Zheng

    (National University of Singapore)

  • Lingqi Li

    (National University of Singapore)

  • Xuanyao Fong

    (National University of Singapore)

  • Kah-Wee Ang

    (National University of Singapore)

Abstract

Memristor crossbar arrays (CBAs) based on two-dimensional (2D) materials have emerged as a potential solution to overcome the limitations of energy consumption and latency associated with conventional von Neumann architectures. However, current 2D memristor CBAs encounter specific challenges such as limited array size, high sneak path current, and lack of integration with peripheral circuits for hardware compute-in-memory (CIM) systems. In this work, we demonstrate a hardware CIM system leveraging heterogeneous integration of scalable 2D hafnium diselenide (HfSe2) memristors and silicon (Si) selectors, as well as their integration with peripheral control-sensing circuits. The 32 × 32 one-selector-one-memristor (1S1R) array mitigates sneak current, achieving 89% yield. The integrated CBA demonstrates an improvement of energy efficiency and response time comparable to state-of-the-art 2D materials-based memristors. To take advantage of low latency devices for achieving low energy systems, we use time-domain sensing circuits with the CBA, whose power consumption surpasses that of analog-to-digital converters (ADCs) by 2.5 folds. The implemented full-hardware binary convolutional neural network (CNN) achieves remarkable accuracy (97.5%) in a pattern recognition task. Additionally, in-built activation functions enhance the energy efficiency of the system. This silicon-compatible heterogeneous integration approach presents a promising hardware solution for artificial intelligence (AI) applications.

Suggested Citation

  • Samarth Jain & Sifan Li & Haofei Zheng & Lingqi Li & Xuanyao Fong & Kah-Wee Ang, 2025. "Heterogeneous integration of 2D memristor arrays and silicon selectors for compute-in-memory hardware in convolutional neural networks," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58039-3
    DOI: 10.1038/s41467-025-58039-3
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    1. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
    2. Lukas Mennel & Joanna Symonowicz & Stefan Wachter & Dmitry K. Polyushkin & Aday J. Molina-Mendoza & Thomas Mueller, 2020. "Ultrafast machine vision with 2D material neural network image sensors," Nature, Nature, vol. 579(7797), pages 62-66, March.
    3. Mingyi Rao & Hao Tang & Jiangbin Wu & Wenhao Song & Max Zhang & Wenbo Yin & Ye Zhuo & Fatemeh Kiani & Benjamin Chen & Xiangqi Jiang & Hefei Liu & Hung-Yu Chen & Rivu Midya & Fan Ye & Hao Jiang & Zhong, 2023. "Thousands of conductance levels in memristors integrated on CMOS," Nature, Nature, vol. 615(7954), pages 823-829, March.
    4. Linfeng Sun & Yishu Zhang & Gyeongtak Han & Geunwoo Hwang & Jinbao Jiang & Bomin Joo & Kenji Watanabe & Takashi Taniguchi & Young-Min Kim & Woo Jong Yu & Bai-Sun Kong & Rong Zhao & Heejun Yang, 2019. "Self-selective van der Waals heterostructures for large scale memory array," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    5. Seungchul Jung & Hyungwoo Lee & Sungmeen Myung & Hyunsoo Kim & Seung Keun Yoon & Soon-Wan Kwon & Yongmin Ju & Minje Kim & Wooseok Yi & Shinhee Han & Baeseong Kwon & Boyoung Seo & Kilho Lee & Gwan-Hyeo, 2022. "A crossbar array of magnetoresistive memory devices for in-memory computing," Nature, Nature, vol. 601(7892), pages 211-216, January.
    6. Weier Wan & Rajkumar Kubendran & Clemens Schaefer & Sukru Burc Eryilmaz & Wenqiang Zhang & Dabin Wu & Stephen Deiss & Priyanka Raina & He Qian & Bin Gao & Siddharth Joshi & Huaqiang Wu & H.-S. Philip , 2022. "A compute-in-memory chip based on resistive random-access memory," Nature, Nature, vol. 608(7923), pages 504-512, August.
    7. Baoshan Tang & Hasita Veluri & Yida Li & Zhi Gen Yu & Moaz Waqar & Jin Feng Leong & Maheswari Sivan & Evgeny Zamburg & Yong-Wei Zhang & John Wang & Aaron V-Y. Thean, 2022. "Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    8. Can Li & Lili Han & Hao Jiang & Moon-Hyung Jang & Peng Lin & Qing Wu & Mark Barnell & J. Joshua Yang & Huolin L. Xin & Qiangfei Xia, 2017. "Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors," Nature Communications, Nature, vol. 8(1), pages 1-9, August.
    9. Peng Yao & Huaqiang Wu & Bin Gao & Jianshi Tang & Qingtian Zhang & Wenqiang Zhang & J. Joshua Yang & He Qian, 2020. "Fully hardware-implemented memristor convolutional neural network," Nature, Nature, vol. 577(7792), pages 641-646, January.
    10. Xingyuan Xu & Mengxi Tan & Bill Corcoran & Jiayang Wu & Andreas Boes & Thach G. Nguyen & Sai T. Chu & Brent E. Little & Damien G. Hicks & Roberto Morandotti & Arnan Mitchell & David J. Moss, 2021. "11 TOPS photonic convolutional accelerator for optical neural networks," Nature, Nature, vol. 589(7840), pages 44-51, January.
    11. S. S. Teja Nibhanupudi & Anupam Roy & Dmitry Veksler & Matthew Coupin & Kevin C. Matthews & Matthew Disiena & Ansh & Jatin V. Singh & Ioana R. Gearba-Dolocan & Jamie Warner & Jaydeep P. Kulkarni & Gen, 2024. "Ultra-fast switching memristors based on two-dimensional materials," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
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