Issue 34, 2024

Dynamic memristor array with multiple reservoir states for training efficient neuromorphic computing

Abstract

In this study, we evaluated the performance of a Pt/Al/TiOy/TiOx/Al2O3/Pt RRAM array device in synaptic and reservoir computing applications. The device exhibited excellent switching characteristics and consistent set processes, along with verifying 100 cycles of DC endurance and cell-to-cell properties. Furthermore, over 104 retention time, the device displayed gradual current decay leading back to its initial high-resistance state, revealing the presence of short-term memory characteristics. Additionally, by leveraging potentiation and depression, paired-pulse facilitation, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, spike-rate-dependent plasticity, and Pavlovian conditioning, we replicated the mechanisms of the biological brain in terms of both short- and long-term memory within our memristor array technology. We also implemented a 4-bit reservoir computing system by leveraging the nonlinear dynamics of the device, adding to its computer-favorable applications. Finally, through analyzing the temporal changes based on a stimulus frequency in a 5 × 5 synaptic arr ay image training process, we concluded that the Pt/Al/TiOy/TiOx/Al2O3/Pt device is suitable for application in neuromorphic systems.

Graphical abstract: Dynamic memristor array with multiple reservoir states for training efficient neuromorphic computing

Supplementary files

Article information

Article type
Paper
Submitted
05 Jun 2024
Accepted
25 Jul 2024
First published
26 Jul 2024

J. Mater. Chem. C, 2024,12, 13516-13524

Dynamic memristor array with multiple reservoir states for training efficient neuromorphic computing

M. Noh, D. Ju and S. Kim, J. Mater. Chem. C, 2024, 12, 13516 DOI: 10.1039/D4TC02324B

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