Performance Optimization of Atomic Layer Deposited HfO<sub>x</sub> Memristor by Annealing With Back-End-of-Line Compatibility
Hong Chen, Lianzheng Li, Jinbin Wang, Guangchao Zhao, Yida Li, Jun Lan, Beng Kang Tay, Gaokuo Zhong, Jiangyu Li, Mingqiang Huang
Abstract
Hafnium oxide (HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> ) 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 HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> 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.