Vertical van der Waals Graphene/h-BN/SnS2 Top-Floating-Gate Memory for Optoelectronic Storage and Neuromorphic Computing

Nonvolatile memories based on two-dimensional (2D) van der Waals (vdW) heterostructures have attracted considerable attention because of their atomic-scale thickness, low energy operation, and prolonged data storage capability. However, most reported vdW floating-gate memories employ a bottom-floating-gate configuration, which often leads to a limited on/off ratio due to charge leakage and substrate-induced disturbance. Herein, a top-floating-gate memory based on a graphene (Gr)/h-BN/SnS2 vdW heterostructure is demonstrated, in which the top graphene layer serves as the charge-trapping layer. The device exhibits excellent nonvolatile memory performance, including an on/off ratio of 106, a memory window of 50.8 V, a low standby current of 1 pA, and an extrapolated retention time of 10 years. Owing to the low hole-tunneling barrier between SnS2 and h-BN, the device achieves more than 50 progressively modulated conductance levels under a weak optical pulse of 0.74 nW·mm-2. Moreover, stable long-term potentiation and depression characteristics can be regulated by optical and electrical pulses. Based on these synaptic behaviors, a three-layer multilayer perceptron neural network using the floating-gate memory achieved a classification accuracy of 96% for handwritten digit recognition using the Modified National Institute of Standards and Technology (MNIST) dataset. This work presents a top-floating-gate architecture for 2D-material memories, providing a feasible strategy for future logic-in-memory and neuromorphic computing applications.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-10-29
DOI
https://doi.org/10.1021/acsami.6c15978
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Vertical van der Waals Graphene/h-BN/SnS2 Top-Floating-Gate Memory for Optoelectronic Storage and Neuromorphic Computing

Yonghui Zhang, Gaoning Fan, Chun Hua Sun, Chao Fan et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Vertical van der Waals Graphene/h-BN/SnS2 Top-Floating-Gate Memory for Optoelectronic Storage and Neuromorphic Computing

Yonghui Zhang, Gaoning Fan, Chun Hua Sun, Chao Fan, Mengjiao Dong, Mingchen Xu, Shiao Feng
article en

Abstract

Nonvolatile memories based on two-dimensional (2D) van der Waals (vdW) heterostructures have attracted considerable attention because of their atomic-scale thickness, low energy operation, and prolonged data storage capability. However, most reported vdW floating-gate memories employ a bottom-floating-gate configuration, which often leads to a limited on/off ratio due to charge leakage and substrate-induced disturbance. Herein, a top-floating-gate memory based on a graphene (Gr)/h-BN/SnS2 vdW heterostructure is demonstrated, in which the top graphene layer serves as the charge-trapping layer. The device exhibits excellent nonvolatile memory performance, including an on/off ratio of 106, a memory window of 50.8 V, a low standby current of 1 pA, and an extrapolated retention time of 10 years. Owing to the low hole-tunneling barrier between SnS2 and h-BN, the device achieves more than 50 progressively modulated conductance levels under a weak optical pulse of 0.74 nW·mm-2. Moreover, stable long-term potentiation and depression characteristics can be regulated by optical and electrical pulses. Based on these synaptic behaviors, a three-layer multilayer perceptron neural network using the floating-gate memory achieved a classification accuracy of 96% for handwritten digit recognition using the Modified National Institute of Standards and Technology (MNIST) dataset. This work presents a top-floating-gate architecture for 2D-material memories, providing a feasible strategy for future logic-in-memory and neuromorphic computing applications.

ACS Applied Materials & Interfaces
China National Offshore Oil Corporation (China) (CN), Hebei University of Technology (CN)
National Natural Science Foundation of China, Hebei Province Science and Technology Support Program, Shanxi Provincial Key Research and Development Project
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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