HfOx/AlOy Superlattice‐Like Memristive Synapse

• Published in: Advanced science Vol. 9; no. 21; pp. e2201446 - n/a
• Main Authors: Wang, Chengxu, Mao, Ge‐Qi, Huang, Menghua, Huang, Enming, Zhang, Zichong, Yuan, Junhui, Cheng, Weiming, Xue, Kan‐Hao, Wang, Xingsheng, Miao, Xiangshui
• Format: Journal Article
• Language: English
• Published: Weinheim John Wiley & Sons, Inc 01.07.2022; John Wiley and Sons Inc; Wiley
• Subjects: analog switching; conductive filaments; memristive synaptic device; neuromorphic computing; Retention; superlattice‐like; Transmission electron microscopy
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202201446

The adjustable conductance of a two‐terminal memristor in a crossbar array can facilitate vector‐matrix multiplication in one step, making the memristor a promising synapse for efficiently implementing neuromorphic computing. To achieve controllable and gradual switching of multi‐level conductance, important for neuromorphic computing, a theoretical design of a superlattice‐like (SLL) structure switching layer for the multi‐level memristor is proposed and validated, refining the growth of conductive filaments (CFs) and preventing CFs from the abrupt formation and rupture. Ti/(HfOx/AlOy)SLL/TiN memristors are shown with transmission electron microscopy , X‐ray photoelectron spectroscopy , and ab initio calculation findings corroborate the SLL structure of HfOx/AlOy film. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factor α = 1.06), and the convolutional neural network based on the SLL memristive synapse improves the handwritten digit recognition accuracy to 94.95%. Meanwhile, this improved synaptic device has a fast operating speed (30 ns), a long data retention time (≥ 104 s at 85 ℃), scalability, and CMOS process compatibility. Finally, its physical nature is explored and the CF evolution process is characterized using nudged elastic band calculations and the conduction mechanism fitting. In this work, as an example the HfOx/AlOy SLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing. A superlattice‐like (SLL) switching layer design is presented to achieve a high‐performance analog‐type memristive synapse for neuromorphic computing. HfOx/AlOy SLL memristors exhibit high conductance modulation linearity, fast operation speed, and long‐term data retention. AlOy layers are set as migration barriers of oxygen vacancy periodically to enhance the controllability of oxygen vacancy and avoid the abrupt formation/rupture of conductive filaments.