Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917446811648000 |
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| author | Menshawy, M. Sanz-Hernández, D. Mazza, L. Puliafito, V. Finocchio, G. Jenkins, A. Ferreira, R. Benetti, L. Grollier, J. Mizrahi, F. A. |
| author_facet | Menshawy, M. Sanz-Hernández, D. Mazza, L. Puliafito, V. Finocchio, G. Jenkins, A. Ferreira, R. Benetti, L. Grollier, J. Mizrahi, F. A. |
| contents | Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we demonstrate remote programming of synaptic weights in series-connected chains of 11 vortex-based magnetic tunnel junctions using broadcast radiofrequency signals applied through a shared strip line. The programming relies on frequency-selective reversal of the vortex-core polarity and therefore does not require individual access lines or selector devices. By reconfiguring the binary states of these chains, we reshape the weighted sums they perform on frequency-multiplexed RF inputs. Using a 22-synapse network composed of two such chains, we remotely reconfigure the same hardware to perform two distinct tasks: handwritten-digit classification and drone RF-signature identification. The digit-optimized configuration reaches 94.91 +/- 0.26% accuracy on handwritten digits but only 13.17 +/- 0.47% on drone RF signatures, whereas the drone-optimized configuration reaches 97.33 +/- 0.62% on drones but only 47.59 +/- 1.5% on digits. Broadcast RF programming thus provides a compact and scalable route to rapidly reconfigurable spintronic neuromorphic hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24561 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal Menshawy, M. Sanz-Hernández, D. Mazza, L. Puliafito, V. Finocchio, G. Jenkins, A. Ferreira, R. Benetti, L. Grollier, J. Mizrahi, F. A. Emerging Technologies Mesoscale and Nanoscale Physics Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we demonstrate remote programming of synaptic weights in series-connected chains of 11 vortex-based magnetic tunnel junctions using broadcast radiofrequency signals applied through a shared strip line. The programming relies on frequency-selective reversal of the vortex-core polarity and therefore does not require individual access lines or selector devices. By reconfiguring the binary states of these chains, we reshape the weighted sums they perform on frequency-multiplexed RF inputs. Using a 22-synapse network composed of two such chains, we remotely reconfigure the same hardware to perform two distinct tasks: handwritten-digit classification and drone RF-signature identification. The digit-optimized configuration reaches 94.91 +/- 0.26% accuracy on handwritten digits but only 13.17 +/- 0.47% on drone RF signatures, whereas the drone-optimized configuration reaches 97.33 +/- 0.62% on drones but only 47.59 +/- 1.5% on digits. Broadcast RF programming thus provides a compact and scalable route to rapidly reconfigurable spintronic neuromorphic hardware. |
| title | Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal |
| topic | Emerging Technologies Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2604.24561 |