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Issue 29, 2016
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Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing

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Abstract

Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution.

Graphical abstract: Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing

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Article information


Submitted
19 Jan 2016
Accepted
20 Apr 2016
First published
21 Apr 2016

Nanoscale, 2016,8, 14015-14022
Article type
Paper

Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing

Z. Wang, M. Yin, T. Zhang, Y. Cai, Y. Wang, Y. Yang and R. Huang, Nanoscale, 2016, 8, 14015
DOI: 10.1039/C6NR00476H

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