macrospins at the wafer scale, which can exhibit intrinsic interaction-driven collective dynamics in response to voltage pulses. This network integrates three key ingredients: the strong dipole-dipole interaction, giant voltage control of coercivity over nearly 1000-fold, and a network topology with a frustrated Ising-like energy landscape. As a result, the network, when stimulated by ∼1 V pulses, transitions from a high-coercivity memory regime into a low-coercivity regime in which internal dipolar fields alone trigger collective magnetic reconfiguration. In this regime, the network exhibits emergent behaviors absent at the level of isolated macrospins, including spontaneous demagnetization, greatly enhanced magnetization modulation, reversible "freeze and resume" evolution, and stochastic convergence toward low-energy magnetic configurations. Furthermore, as a conceptual illustration, micromagnetic simulation of such a strongly dipolar-coupled network shows that the resulting high-dimensional collective dynamics can support temporal information processing, such as accurate chaotic Mackey-Glass prediction and multiclass drone-signal classification. Our work suggests a conceptually distinct route toward scalable, energy-efficient neuromorphic computing, one rooted in local physical interaction-driven emergent dynamics at the network level rather than merely mimicking individual neurons and synapses.
Ye et al. (Mon,) studied this question.