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A van der Waals Synaptic Transistor Based on Ferroelectric Hf 0.5 Zr 0.5 O 2 and 2D Tungsten Disulfide
Author(s) -
Chen Li,
Wang Lin,
Peng Yue,
Feng Xuewei,
Sarkar Soumya,
Li Sifan,
Li Bochang,
Liu Liang,
Han Kaizhen,
Gong Xiao,
Chen Jingsheng,
Liu Yan,
Han Genquan,
Ang KahWee
Publication year - 2020
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.202000057
Subject(s) - ferroelectricity , materials science , neuromorphic engineering , synaptic weight , optoelectronics , transistor , atomic layer deposition , nanotechnology , computer science , voltage , thin film , electrical engineering , artificial neural network , dielectric , machine learning , engineering
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 (WS 2 ), 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 10 5 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.