Work

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

Year: 2020

Type: article

Abstract: 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/TaOx/Al2O3/Pt/Si memristors by varying Al2O3layer thickness. Presence of the Al2O3layer 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 Al2O3based device. Multilevel capability of the memristive devices is also explored for prospective use as a synapse. More than 104and 4 × 104cycles nondegradable dc and ac endurances, respectively, along with 104second 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 TaOxmemristors can be potentially used as an electronic synapse.

Source: IEEE Journal of the Electron Devices Society

DOI: 10.1109/jeds.2020.2966799

Authors: Pei-Yu Jung, Debashis Panda, Sridhar Chandrasekaran, Sailesh Rajasekaran, Tseung-Yuen Tseng

Institutions: National Yang Ming Chiao Tung University, National Institute of Science and Technology

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,350

Funders: Department of Science and Technology, Ministry of Science and Technology, India, Ministry of Science and Technology, Taiwan, Science and Engineering Research Board