Adjustable short-term memory of SiOx:Ag-based memristor for reservoir computing

Ruiyi Li; Haozhang Yang; Yizhou Zhang; Nan Tang; Ruiqi Chen; Zheng Zhou; Lifeng Liu; Jinfeng Kang; Peng Huang

doi:10.1088/1361-6528/acfb0a

Adjustable short-term memory of SiO_x:Ag-based memristor for reservoir computing

Nanotechnology. 2023 Oct 9;34(50). doi: 10.1088/1361-6528/acfb0a.

Authors

Ruiyi Li^{1

2}, Haozhang Yang^{1

2}, Yizhou Zhang^{1

2}, Nan Tang^{1

2}, Ruiqi Chen^{1

2}, Zheng Zhou^{1

2}, Lifeng Liu^{1

2}, Jinfeng Kang^{1

2}, Peng Huang^{1

2}

Affiliations

¹ School of Integrated Circuits, Peking University, Beijing 100871, People's Republic of China.
² Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, People's Republic of China.

PMID: 37812619
DOI: 10.1088/1361-6528/acfb0a

Abstract

Temporal information processing is critical for a wide spectrum of applications, such as finance, biomedicine, and engineering. Reservoir computing (RC) can efficiently process temporal information with low training costs. Various memristors have been explored to demonstrate RC systems leveraging the short-term memory and nonlinear dynamic behaviours. However, the short-term memory is fixed after the device fabrication, limiting the applications to diverse temporal analysis tasks. In this work, we propose the approaches to modulating the short-term memory of Pt/SiO_x:Ag/Pt memristor for the performance improvement of the RC systems. By controlling the read voltage, pulse amplitude and pulse width applied to the devices, the obtainable range of the characteristic time reaches three orders of magnitude from microseconds to around milliseconds. Based on the fabricated memristor, the classification of 4-bit pulse streams is demonstrated. Memristor-based RC systems with adjustable short-term memory are constructed for time-series prediction and pattern recognition tasks with different requirements for the characteristic times. The simulation results show that low normalized root mean square error of 0.003 (0.27) in Hénon map (Mackey-Glass time series) and excellent classification accuracy of 99.6% (91.7%) in spoken-digit recognition (MNIST image recognition) are achieved, which outperforms most memristor-based RC systems recently reported. Furthermore, the RC networks with diverse short-term memories are constructed to address more complicated tasks with low prediction errors. This work proves the high controllability of memristor-based RC systems to handle multiple temporal processing tasks.

Keywords: adjustable short-term memory; memristor; nonlinear dynamic behaviour; reservoir computing.