材料科学
铁电性
神经形态工程学
突触重量
光电子学
晶体管
原子层沉积
纳米技术
计算机科学
薄膜
电压
电气工程
人工神经网络
电介质
机器学习
工程类
作者
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
标识
DOI:10.1002/aelm.202000057
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI