Skyrmion-based synaptic element with strain-mediated plasticity

Abstract We present a novel skyrmion-based synaptic device featuring a multilayer structure of Ferromagnetic (FM)/Heavy metal (HM)/Ferroelectric (FE)/HM/FM, specifically (Co/Pt) n /(011)PMN-PT/(Co/Pt) n . The FE layer sandwiched between the two FM layers hosting the skyrmions enables electric field-induced strain-mediated modulation of perpendicular magnetic anisotropy in the FM layers. This mechanism facilitates tunable skyrmion sizes, achieving continuous non-volatile conductance states due to remnant strain in the FE layer. The proposed device exhibits both synaptic potentiation and depression with the aid of differential MTJ readout unlike the prior skyrmion-based synaptic implementations and exhibits superior energy-efficiency compared to the other emerging non-volatile memory-based synaptic devices. Furthermore, a VGG-8 convolutional neural network utilizing the proposed synaptic element as weights achieves an accuracy of ∼90.39% after training on the CIFAR-10 dataset.

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Publication Details

Journal
Neuromorphic Computing and Engineering
Published
2025-08-08
DOI
https://doi.org/10.1088/2634-4386/adf992
Citations
1
Primary Topic
Polydiacetylene-based materials and applications
Type
article
Field-Weighted Citation Impact
0.94
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article

Skyrmion-based synaptic element with strain-mediated plasticity

Sneh Saurabh, Shubham Sahay, Jasmine Kaur
1 citations
Neuromorphic Computing and Engineering
Polydiacetylene-based materials and applications
0.94
article

Skyrmion-based synaptic element with strain-mediated plasticity

Sneh Saurabh, Shubham Sahay, Jasmine Kaur
article en
1 citations

Abstract

Abstract We present a novel skyrmion-based synaptic device featuring a multilayer structure of Ferromagnetic (FM)/Heavy metal (HM)/Ferroelectric (FE)/HM/FM, specifically (Co/Pt) n /(011)PMN-PT/(Co/Pt) n . The FE layer sandwiched between the two FM layers hosting the skyrmions enables electric field-induced strain-mediated modulation of perpendicular magnetic anisotropy in the FM layers. This mechanism facilitates tunable skyrmion sizes, achieving continuous non-volatile conductance states due to remnant strain in the FE layer. The proposed device exhibits both synaptic potentiation and depression with the aid of differential MTJ readout unlike the prior skyrmion-based synaptic implementations and exhibits superior energy-efficiency compared to the other emerging non-volatile memory-based synaptic devices. Furthermore, a VGG-8 convolutional neural network utilizing the proposed synaptic element as weights achieves an accuracy of ∼90.39% after training on the CIFAR-10 dataset.

Neuromorphic Computing and EngineeringVol. 5(3)
Indraprastha Institute of Information Technology Delhi (IN), Indian Institute of Technology Delhi (IN)
Openalex Percentile: Top 23%
Polydiacetylene-based materials and applications
0.94
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