A voltage-responsive strongly dipolar-coupled macrospin network with emergent dynamics for computing

TL;DR

This paper demonstrates a macrospin network with voltage-controlled dipolar interactions that exhibits emergent collective dynamics, enabling low-energy computing tasks such as chaotic prediction and signal classification.

Contribution

It introduces a strongly dipolar-coupled macrospin network with voltage-tunable interactions, revealing emergent behaviors for scalable, low-voltage computing.

Findings

Voltage pulses induce collective magnetic behaviors absent at single-magnet level.

The network enables chaotic Mackey-Glass prediction and multiclass signal classification.

Strong dipolar coupling and voltage control facilitate emergent dynamics in macrospin networks.

Abstract

Emergent behavior, which arises from local interactions between simple elements, is pervasive in nature. It underlies the energy-efficient computing in our brains. However, realizing such dynamics in artificial materials, particularly under low-energy stimuli, remains a fundamental challenge. While dipole-dipole interactions are typically suppressed in magnetic storage, here we harness and amplify them to construct a strongly dipolar-coupled network of SmCo5 macrospins at wafer scale, which can exhibit intrinsic interaction-driven collective dynamics in response to voltage pulses. The network combines three essential ingredients: strong dipolar coupling by large single-domain macrospin, giant voltage control of coercivity over nearly 1000-fold, and disordered network topology with frustrated Ising-like energy landscape. When stimulated by 1 V pulses, the network enters a regime where interaction-driven magnetic behaviors emerge, including spontaneous demagnetization, greatly enhanced magnetization modulation, reversible freeze and resume evolution and stochastic convergence toward low-energy magnetic configurations. All these behaviors are completely absent at the single-nanomagnet level. Furthermore, by constructing micromagnetic models of the strongly dipolar-coupled macrospin networks, we show that the resulting nonlinear, high-dimensional collective dynamics, intrinsic to strongly-interacting systems, can enable accurate chaotic Mackey-Glass prediction and multiclass drone-signal classification. Our work establishes the voltage-responsive strongly-coupled SmCo5 network as a mesoscopic platform for probing emergent magnetic dynamics previously inaccessible under ambient conditions. It also suggests a fundamental distinct route towards scalable, low-voltage computing, one rooted in native physical interaction-driven collective dynamics at the network level.