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https://doi.org/10.1002/inf2.12230
Title: | Band-tailored van der Waals heterostructure for multilevel memory and artificial synapse | Authors: | Wang, Yanan Zheng, Yue Gao, Jing Jin, Tengyu Li, Enlong Lian, Xu Pan, Xuan Han, Cheng Chen, Huipeng Chen, Wei |
Issue Date: | 13-Jul-2021 | Publisher: | Blackwell Publishing Ltd | Citation: | Wang, Yanan, Zheng, Yue, Gao, Jing, Jin, Tengyu, Li, Enlong, Lian, Xu, Pan, Xuan, Han, Cheng, Chen, Huipeng, Chen, Wei (2021-07-13). Band-tailored van der Waals heterostructure for multilevel memory and artificial synapse. InfoMat 3 (8) : 917-928. ScholarBank@NUS Repository. https://doi.org/10.1002/inf2.12230 | Rights: | Attribution 4.0 International | Abstract: | Two-dimensional (2D) van der Waals heterostructure (vdWH)-based floating gate devices show great potential for next-generation nonvolatile and multilevel data storage memory. However, high program voltage induced substantial energy consumption, which is one of the primary concerns, hinders their applications in low-energy-consumption artificial synapses for neuromorphic computing. In this study, we demonstrate a three-terminal floating gate device based on the vdWH of tin disulfide (SnS2), hexagonal boron nitride (h-BN), and few-layer graphene. The large electron affinity of SnS2 facilitates a significant reduction in the program voltage of the device by lowering the hole-injection barrier across h-BN. Our floating gate device, as a nonvolatile multilevel electronic memory, exhibits large on/off current ratio (~105), good retention (over 104 s), and robust endurance (over 1000 cycles). Moreover, it can function as an artificial synapse to emulate basic synaptic functions. Further, low energy consumption down to ~7 picojoule (pJ) can be achieved owing to the small program voltage. High linearity (<1) and conductance ratio (~80) in long-term potentiation and depression (LTP/LTD) further contribute to the high pattern recognition accuracy (~90%) in artificial neural network simulation. The proposed device with attentive band engineering can promote the future development of energy-efficient memory and neuromorphic devices. (Figure presented.). © 2021 The Authors. InfoMat published by UESTC and John Wiley & Sons Australia, Ltd. | Source Title: | InfoMat | URI: | https://scholarbank.nus.edu.sg/handle/10635/233153 | ISSN: | 2567-3165 | DOI: | 10.1002/inf2.12230 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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