Synchronization properties and reservoir computing capability of hexagonal spintronic oscillator arrays

Chęciński, Jakub

doi:10.1016/j.jmmm.2020.167251

Cited by 7 publications

(1 citation statement)

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“…Nano-constriction spin-Hall nano-oscillators (NC-SHNOs) require low drive current and are a promising solution for compact and tuneable microwave signal sources [5]- [7]. In order to achieve narrow linewidth and higher output power, synchronization via a propagating spin wave has been demonstrated in various configurations, including multi-constriction NC-SHNO [8]- [10] and NC-SHNO arrays [11], [12]. For these multiple oscillator configurations, the frequency output stability and variability of each individual oscillator are crucial, to guarantee the phase synchronization within the array.…”

Section: Introductionmentioning

confidence: 99%

Impact of Random Grain Structure on Spin-Hall Nano-Oscillator Modal Stability

Capriata

Jiang

Åkerman

et al. 2022

IEEE Electron Device Lett.

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Section: Introductionmentioning

confidence: 99%

Impact of Random Grain Structure on Spin-Hall Nano-Oscillator Modal Stability

Capriata

Jiang

Åkerman

et al. 2022

IEEE Electron Device Lett.

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Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO₂ Memcapacitive Synapse Arrays

et al. 2023

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Hardware implementations tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but haven't been experimentally exploited yet. Here we report an oxide‐based memcapacitive synapse (OMC) based on Zr‐doped HfO2 (HZO) for a power‐efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could be originated from the capacitively coupled polarization switching and charge trapping of hafnium oxide‐based devices. The power consumption (∼113.4 fJ/spike) and temporal processing versatility outperform most resistive reservoirs. Our system has been verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof‐of‐concept, a touchless user interface for virtual shopping is demonstrated based on our OMC‐based reservoir computing system, benefiting from its interference‐robust acoustic and electrophysiological perception. Our results would shed light on the development of high power‐efficient human‐machine interfaces and machine‐learning platforms.This article is protected by copyright. All rights reserved

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Reservoir Computing with Nanowire Exchange-Coupled Spin Torque Oscillator Arrays

Dale

Evans

Sebald

et al. 2023

Lecture Notes in Computer Science

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Synchronization properties and reservoir computing capability of hexagonal spintronic oscillator arrays

Cited by 7 publications

References 32 publications

Impact of Random Grain Structure on Spin-Hall Nano-Oscillator Modal Stability

Impact of Random Grain Structure on Spin-Hall Nano-Oscillator Modal Stability

Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO₂ Memcapacitive Synapse Arrays

Reservoir Computing with Nanowire Exchange-Coupled Spin Torque Oscillator Arrays

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Synchronization properties and reservoir computing capability of hexagonal spintronic oscillator arrays

Cited by 7 publications

References 32 publications

Impact of Random Grain Structure on Spin-Hall Nano-Oscillator Modal Stability

Impact of Random Grain Structure on Spin-Hall Nano-Oscillator Modal Stability

Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO2 Memcapacitive Synapse Arrays

Reservoir Computing with Nanowire Exchange-Coupled Spin Torque Oscillator Arrays

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Product

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Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO₂ Memcapacitive Synapse Arrays