A van der Waals Synaptic Transistor Based on Ferroelectric Hf0.5Zr0.5O2 and 2D Tungsten Disulfide

Abstract Neuromorphic computing on the hardware level is promising for performing ever‐increasing data‐centric tasks owing to its superiority to conventional von Neumann architecture in terms of energy efficiency and learning ability. One key aspect to its implementation is the development of artifi...

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Main Authors: Li Chen, Lin Wang, Yue Peng, Xuewei Feng, Soumya Sarkar, Sifan Li, Bochang Li, Liang Liu, Kaizhen Han, Xiao Gong, Jingsheng Chen, Yan Liu, Genquan Han, Kah‐Wee Ang
Format: Article
Language:English
Published: Wiley-VCH 2020-06-01
Series:Advanced Electronic Materials
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Online Access:https://doi.org/10.1002/aelm.202000057
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Summary:Abstract Neuromorphic computing on the hardware level is promising for performing ever‐increasing data‐centric tasks owing to its superiority to conventional von Neumann architecture in terms of energy efficiency and learning ability. One key aspect to its implementation is the development of artificial synapses that can effectively emulate the multiple functionalities exhibited by their biological counterparts. Here, building on an inorganic ferroelectric gate stack integrated with a 2D layered semiconductor (WS2), a new type of ferroelectricity‐based synaptic transistor that differs from those relying on interface traps or floating gate configuration is reported. By virtue of a 6 nm thick ferroelectric hafnium zirconium oxide by atomic layer deposition and postannealing treatment, the device shows a channel resistance change ratio above 105 corresponding to opposite ferroelectric polarization direction. Furthermore, by applying electrical stimulus to the gate, it demonstrates good capability to mimic various synaptic behaviors including long‐term potentiation, long‐term depression, spike‐amplitude‐dependent plasticity, and spike‐rate‐dependent plasticity. Given the inherent compatibility of the ferroelectric gate stack with existing fabrication technology, and the reliability of ferroelectricity engineering, this work paves the way toward practical implementation of synaptic devices in neuromorphic circuits.
ISSN:2199-160X