Convolutions with Radio-Frequency Spin-Diodes
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909467013021696 |
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| author | Plouet, Erwann Singh, Hanuman Sethi, Pankaj Mizrahi, Frank A. Sanz-Hernandez, Dedalo Grollier, Julie |
| author_facet | Plouet, Erwann Singh, Hanuman Sethi, Pankaj Mizrahi, Frank A. Sanz-Hernandez, Dedalo Grollier, Julie |
| contents | The classification of radio-frequency (RF) signals is crucial for applications in robotics, traffic control, and medical devices. Spintronic devices, which respond to RF signals via ferromagnetic resonance, offer a promising solution. Recent studies have shown that a neural network of nanoscale magnetic tunnel junctions can classify RF signals without digitization. However, the complexity of these junctions poses challenges for rapid scaling. In this work, we demonstrate that simple spintronic devices, known as metallic spin-diodes, can effectively perform RF classification. These devices consist of NiFe/Pt bilayers and can implement weighted sums of RF inputs. We experimentally show that chains of four spin-diodes can execute 2x2 pixel filters, achieving high-quality convolutions on the Fashion-MNIST dataset. Integrating the hardware spin-diodes in a software network, we achieve a top-1 accuracy of 88 \% on the first 100 images, compared to 88.4 \% for full software with noise, and 90 \% without noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convolutions with Radio-Frequency Spin-Diodes Plouet, Erwann Singh, Hanuman Sethi, Pankaj Mizrahi, Frank A. Sanz-Hernandez, Dedalo Grollier, Julie Signal Processing Emerging Technologies Applied Physics The classification of radio-frequency (RF) signals is crucial for applications in robotics, traffic control, and medical devices. Spintronic devices, which respond to RF signals via ferromagnetic resonance, offer a promising solution. Recent studies have shown that a neural network of nanoscale magnetic tunnel junctions can classify RF signals without digitization. However, the complexity of these junctions poses challenges for rapid scaling. In this work, we demonstrate that simple spintronic devices, known as metallic spin-diodes, can effectively perform RF classification. These devices consist of NiFe/Pt bilayers and can implement weighted sums of RF inputs. We experimentally show that chains of four spin-diodes can execute 2x2 pixel filters, achieving high-quality convolutions on the Fashion-MNIST dataset. Integrating the hardware spin-diodes in a software network, we achieve a top-1 accuracy of 88 \% on the first 100 images, compared to 88.4 \% for full software with noise, and 90 \% without noise. |
| title | Convolutions with Radio-Frequency Spin-Diodes |
| topic | Signal Processing Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2501.16204 |