Related Experiment Video
Updated: Jun 10, 2026
09:54
Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons
Published on: July 14, 2021
Voltage-Triggered Emergent Dynamics in Strongly Coupled Nanomagnet Networks for Neuromorphic Computing
Xinglong Ye1,2, Zhibo Zhao3,4, Qian Wang1
1School of Physics, Shandong University, Jinan 250100, China.
ACS Nano
|June 8, 2026
Summary
Artificial magnetic networks exhibiting emergent dynamics were created using voltage-controlled SmCo5 macrospins. These networks show collective behaviors for energy-efficient neuromorphic computing.
Area of Science:
- Condensed Matter Physics
- Materials Science
- Complex Systems
Background:
- Emergent dynamics are crucial in complex systems but challenging to engineer in artificial materials with low-energy stimuli.
- Dipole-dipole interactions are usually minimized in magnetic storage, hindering their use for emergent phenomena.
Purpose of the Study:
- To engineer artificial materials exhibiting emergent dynamics driven by local interactions.
- To leverage amplified dipole-dipole interactions and voltage control for novel collective magnetic behaviors.
- To explore the potential of these networks for energy-efficient neuromorphic computing.
Main Methods:
- Fabrication of a wafer-scale network of strongly dipolar-coupled SmCo5 macrospins.
- Utilizing giant voltage control of coercivity (nearly 1000-fold) and a frustrated Ising-like energy landscape.
- Stimulating the network with low-voltage pulses (∼1 V) to induce transitions in magnetic regimes.
Main Results:
- The network transitions from a high-coercivity memory state to a low-coercivity state where internal dipolar fields drive collective reconfiguration.
- Observed emergent behaviors include spontaneous demagnetization, enhanced magnetization modulation, reversible state evolution, and stochastic convergence to low-energy states.
- Micromagnetic simulations demonstrated the network's capacity for temporal information processing, including chaotic prediction and signal classification.
Conclusions:
- A novel approach to creating emergent dynamics in artificial materials using amplified dipole-dipole interactions and voltage control.
- The developed strongly dipolar-coupled network exhibits unique collective behaviors and supports temporal information processing.
- This work presents a new pathway for scalable and energy-efficient neuromorphic computing based on network-level emergent dynamics.
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