AI Article Synopsis
- Researchers explored a virtual oscillator network using a single spintronic oscillator to address issues like inconsistencies in conventional neural networks based on real oscillators.
- The proposed solution involves using a ferromagnet controlled by voltage-controlled magnetic anisotropy (VCMA) to minimize energy dissipation, reducing Joule heating from electric currents.
- The study successfully demonstrated associative memory operations for recognizing alphabet patterns by linking colors in patterns to the sign of a magnetic anisotropy coefficient, which can be manipulated through the VCMA effect.
Article Abstract
Recently, an associative memory operation by a virtual oscillator network, consisting of a single spintronic oscillator, was examined to solve issues in conventional, real oscillators-based neural networks such as inhomogeneities between the oscillators. However, the spintronic oscillator still carries issues dissipating large amount of energy because it is driven by electric current. Here, we propose to use a single ferromagnet manipulated by voltage-controlled magnetic anisotropy (VCMA) effect as a fundamental element in a virtual neural network, which will contribute to significantly reducing the Joule heating caused by electric current. Instead of the oscillation in oscillator networks, magnetization relaxation dynamics were used for the associative memory operation. The associative memory operation for alphabet patterns is successfully demonstrated by giving correspondences between the colors in a pattern recognition task and the sign of a perpendicular magnetic anisotropy coefficient, which could be either positive or negative via the VCMA effect.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11002033 | PMC |
| http://dx.doi.org/10.1038/s41598-024-58556-z | DOI Listing |
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