Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition

Muhammad Zubair; Yi Dong; Bin Cai; Xiao Fu; Hailu Wang; Tangxin Li; Jinjin Wang; Shuning Liu; Mengjia Xia; Qixiao Zhao; Runzhang Xie; Hangyu Xu; Xiaoyong Jiang; Shuhong Hu; Bo Song; Xiaolong Chen; Jiadong Zhou; Lixin Dong; Jinshui Miao

doi:10.1063/5.0157710

Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition

Muhammad Zubair, Yi Dong, Bin Cai, Xiao Fu^*, Hailu Wang, Tangxin Li, Jinjin Wang, Shuning Liu, Mengjia Xia, Qixiao Zhao, Runzhang Xie, Hangyu Xu, Xiaoyong Jiang, Shuhong Hu, Bo Song, Xiaolong Chen, Jiadong Zhou, Lixin Dong, Jinshui Miao^*

^*Corresponding author for this work

School of Physics

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Two-dimensional (2D) materials with reconfigurable properties show potential in neuromorphic hardware applications. However, most 2D materials-based neuromorphic hardware is volatile, which needs large energy to accomplish perception functions. Here, we report on nonvolatile floating gate photo-memory devices based on ReS₂/h-BN/SnS₂ van der Waals heterostructures. The devices exhibit a large memory window of ∼60 V, a high program/erase current ratio of ∼10⁷ with excellent retention characteristics, a low off-state current of 7.4 × 10⁻¹³ A, and a high detectivity of 1.98 × 10¹³ cm Hz^1/2 W⁻¹, allowing for multi-bit information storage. For the multi-level storage capacity, 27 photo-memory states are obtained by pulsed laser illumination. Moreover, a neuromorphic computing network is also constructed based on the photo-memory devices with a maximum recognition accuracy of up to 90%. This work paves the way for miniaturization and high-density integration of future optoelectronics for neuromorphic hardware applications.

Original language	English
Article number	051102
Journal	Applied Physics Letters
Volume	123
Issue number	5
DOIs	https://doi.org/10.1063/5.0157710
Publication status	Published - 31 Jul 2023

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Zubair, M., Dong, Y., Cai, B., Fu, X., Wang, H., Li, T., Wang, J., Liu, S., Xia, M., Zhao, Q., Xie, R., Xu, H., Jiang, X., Hu, S., Song, B., Chen, X., Zhou, J., Dong, L., & Miao, J. (2023). Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition. Applied Physics Letters, 123(5), Article 051102. https://doi.org/10.1063/5.0157710

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title = "Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition",

abstract = "Two-dimensional (2D) materials with reconfigurable properties show potential in neuromorphic hardware applications. However, most 2D materials-based neuromorphic hardware is volatile, which needs large energy to accomplish perception functions. Here, we report on nonvolatile floating gate photo-memory devices based on ReS2/h-BN/SnS2 van der Waals heterostructures. The devices exhibit a large memory window of ∼60 V, a high program/erase current ratio of ∼107 with excellent retention characteristics, a low off-state current of 7.4 × 10−13 A, and a high detectivity of 1.98 × 1013 cm Hz1/2 W−1, allowing for multi-bit information storage. For the multi-level storage capacity, 27 photo-memory states are obtained by pulsed laser illumination. Moreover, a neuromorphic computing network is also constructed based on the photo-memory devices with a maximum recognition accuracy of up to 90%. This work paves the way for miniaturization and high-density integration of future optoelectronics for neuromorphic hardware applications.",

author = "Muhammad Zubair and Yi Dong and Bin Cai and Xiao Fu and Hailu Wang and Tangxin Li and Jinjin Wang and Shuning Liu and Mengjia Xia and Qixiao Zhao and Runzhang Xie and Hangyu Xu and Xiaoyong Jiang and Shuhong Hu and Bo Song and Xiaolong Chen and Jiadong Zhou and Lixin Dong and Jinshui Miao",

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day = "31",

doi = "10.1063/5.0157710",

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volume = "123",

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T1 - Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition

AU - Zubair, Muhammad

AU - Dong, Yi

AU - Cai, Bin

AU - Fu, Xiao

AU - Wang, Hailu

AU - Li, Tangxin

AU - Wang, Jinjin

AU - Liu, Shuning

AU - Xia, Mengjia

AU - Zhao, Qixiao

AU - Xie, Runzhang

AU - Xu, Hangyu

AU - Jiang, Xiaoyong

AU - Hu, Shuhong

AU - Song, Bo

AU - Chen, Xiaolong

AU - Zhou, Jiadong

AU - Dong, Lixin

AU - Miao, Jinshui

PY - 2023/7/31

Y1 - 2023/7/31

N2 - Two-dimensional (2D) materials with reconfigurable properties show potential in neuromorphic hardware applications. However, most 2D materials-based neuromorphic hardware is volatile, which needs large energy to accomplish perception functions. Here, we report on nonvolatile floating gate photo-memory devices based on ReS2/h-BN/SnS2 van der Waals heterostructures. The devices exhibit a large memory window of ∼60 V, a high program/erase current ratio of ∼107 with excellent retention characteristics, a low off-state current of 7.4 × 10−13 A, and a high detectivity of 1.98 × 1013 cm Hz1/2 W−1, allowing for multi-bit information storage. For the multi-level storage capacity, 27 photo-memory states are obtained by pulsed laser illumination. Moreover, a neuromorphic computing network is also constructed based on the photo-memory devices with a maximum recognition accuracy of up to 90%. This work paves the way for miniaturization and high-density integration of future optoelectronics for neuromorphic hardware applications.

AB - Two-dimensional (2D) materials with reconfigurable properties show potential in neuromorphic hardware applications. However, most 2D materials-based neuromorphic hardware is volatile, which needs large energy to accomplish perception functions. Here, we report on nonvolatile floating gate photo-memory devices based on ReS2/h-BN/SnS2 van der Waals heterostructures. The devices exhibit a large memory window of ∼60 V, a high program/erase current ratio of ∼107 with excellent retention characteristics, a low off-state current of 7.4 × 10−13 A, and a high detectivity of 1.98 × 1013 cm Hz1/2 W−1, allowing for multi-bit information storage. For the multi-level storage capacity, 27 photo-memory states are obtained by pulsed laser illumination. Moreover, a neuromorphic computing network is also constructed based on the photo-memory devices with a maximum recognition accuracy of up to 90%. This work paves the way for miniaturization and high-density integration of future optoelectronics for neuromorphic hardware applications.

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Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition

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