All-metal oxide synaptic transistor with modulatable plasticity

• Published in: Nanotechnology 2020-01, Vol.31 (6), p.65201-065201
• Main Authors: Lv, Dongxu, Yang, Qian, Chen, Qizhen, Chen, Jinwei, Lai, Dengxiao, Chen, Huipeng, Guo, Tailiang
• Format: Article
• Language: English
• Subjects: metal oxide transistors; oxygen vacancy; solid state electrolyte; synaptic transistors
• ISSN: 0957-4484; 1361-6528
• DOI: 10.1088/1361-6528/ab5080

The artificial neural system has attracted tremendous attention in the field of artificial intelligence due to operate mode of parallel computation which is superior to traditional Von Neumann computers in processing complex sensory data and real-time situations with extremely low power dissipation. Remarkable progress has been made in the hardware-based electric-double-layer synaptic transistors as its modulation by ion movement is similar to biological synapse for the past few years. Unfortunately, long-term potentiation (LTP) timescale is still a big challenge in hardware-based electric-double-layer synaptic transistors which is essential to processing capacity and memory formation. Meanwhile, the effect of ion concentration on the synaptic plasticity has rarely been reported. Here, a solid state electrolyte-gated transistor using Ta2O5 as dielectric layer with unique ionic composition was demonstrated and the regulation of synaptic weight was realized by changing ion concentration. Both the potentiation and depression of synaptic weight such as excitatory post-synaptic current, inhibitory response (IPSC), paired pulse facilitation as well as LTP were successfully simulated. More importantly, oxygen vacancy content was tuned for the first time to modulate synaptic plasticity by varying film thickness and gas ratio, through which the intensity and duration of memory were enhanced with appropriate vacancy concentration. It indicated that appropriate vacancy concentration avoided the effects of internal electric field induced by ion excess, leading to a long-term memory. These results reveal a promising path to improve memory capacity of artificial synapse via ion modulation.