Enhanced Switching Properties in TaOx Memristors Using Diffusion Limiting Layer for Synaptic Learning

IEEE Journal of the Electron Devices Society

卷:8

Sridhar Chandrasekaran¹  Sailesh Rajasekaran²  Debashis Panda³  Pei-Yu Jung⁴  Tseung-Yuen Tseng⁴ 

[1] Department of EECS, National Chiao Tung University, Hsinchu, Taiwan;

[2] Department of Materials Science and Engineering, National Chiao Tung University, Hsinchu, Taiwan;

[3] Department of Physics, National Institute of Science and Technology, Berhampur, India;

[4] Institute of Electronics, National Chiao Tung University, Hsinchu, Taiwan;

关键词: Memristors;    synapse;    neuromorphic computing;   

DOI  :  10.1109/JEDS.2020.2966799

来源: DOAJ

【 摘 要 】

To move towards a new generation powerful computing system, brain-inspired neuromorphic computing is expected to transform the architecture of the conventional computer, where memristors are considered to be potential solutions for synapses part. We propose and demonstrate a novel approach to achieve remarkable improvement of analog switching linearity in TaN/Ta/TaO_x/Al₂O₃/Pt/Si memristors by varying Al₂O₃ layer thickness. Presence of the Al₂O₃ layer is confirmed from the Auger Electron Spectroscopy study. Good analog switching ratio of about 100× and superior switching uniformity are observed for the 1 nm Al₂O₃ based device. Multilevel capability of the memristive devices is also explored for prospective use as a synapse. More than 10⁴ and 4 × 10⁴ cycles nondegradable dc and ac endurances, respectively, along with 10⁴ second retention are achieved for the optimized device. Improved linearities of 2.41 and -2.77 for potentiation and depression, respectively are obtained for such 1 nm Al₂O₃-based devices. The property of gradual resistance changed by pulse amplitudes confirms that the TaO_x memristors can be potentially used as an electronic synapse.

【 授权许可】

Unknown