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Enhancing LiAlOX synaptic performance by reducing the Schottky barrier height for deep neural network applications

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Abstract

Although good performance has been reported in shallow neural networks, the application of memristor synapses towards realistic deep neural networks has met more stringent requirements on the synapse properties, particularly the high precision and linearity of the synaptic analog weight tuning. In this study, a LiAlOX memristor synapse was fabricated and optimized to address these demands. By delicately tuning the initial conductance states, 120-level continuously adjustable conductance states were obtained and the nonlinearity factor was substantially reduced from 8.96 to 0.83. The significant enhancements were attributed to the reduced Schottky barrier height (SBH) between the filament tip and the electrode, which was estimated from the measured IV curves. Furthermore, a deep neural network for realistic action recognition task was constructed, and the recognition accuracy was found to be increased from 15.1% to 91.4% on the Weizmann video dataset by adopting the above-described device optimization method.

Graphical abstract: Enhancing LiAlOX synaptic performance by reducing the Schottky barrier height for deep neural network applications

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Article information


Submitted
25 Jun 2020
Accepted
22 Sep 2020
First published
24 Sep 2020

Nanoscale, 2020, Advance Article
Article type
Paper

Enhancing LiAlOX synaptic performance by reducing the Schottky barrier height for deep neural network applications

Y. Fu, B. Dong, W. Su, C. Lin, K. Zhou, T. Chang, F. Zhuge, Y. Li, Y. He, B. Gao and X. Miao, Nanoscale, 2020, Advance Article , DOI: 10.1039/D0NR04782A

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