记忆电阻器
原子层沉积
材料科学
神经形态工程学
退火(玻璃)
生产线后端
光电子学
CMOS芯片
相容性(地球化学)
电导
电子工程
晶体管
纳米技术
计算机科学
电气工程
人工神经网络
薄膜
电压
工程类
冶金
电介质
复合材料
物理
凝聚态物理
机器学习
作者
Hong Chen,Lianzheng Li,Jinbin Wang,Guangchao Zhao,Ying Li,Jun Lan,Beng Kang Tay,Gaokuo Zhong,Jiangyu Li,Mingqiang Huang
出处
期刊:IEEE Electron Device Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-07-01
卷期号:43 (7): 1141-1144
标识
DOI:10.1109/led.2022.3177774
摘要
Hafnium oxide (HfOx) memristor has attracted enormous attention due to its high performance and back-end-of-line (BEOL) compatibility, thus providing a novel approach to implementing artificial intelligence neural networks. In this work, great performance optimization of HfOx memristor has been achieved by using atomic layer deposition (ALD) method and post-metal annealing (PMA) process, in which both procedures are with low-temperature budget (< 300 °C) and are compatible with CMOS BEOL process. The device exhibits forming-free, high yield, good linearity, fast speed and non-volatile characteristics. Besides, the device conductance can be well modulated by using the most desired pulse protocol, namely the identical pulse with same pulse amplitude and width. More than 3bit stable conductance states have been obtained, indicating its great potential in practical memristor neuromorphic computing system.
科研通智能强力驱动
Strongly Powered by AbleSci AI