# Interlayer Dzyaloshinskii–Moriya Interaction in Synthetic Ferrimagnets for Spiking Neural Networks

## TL;DR

This paper explores how magnetic interactions in synthetic ferrimagnets can be used to create energy-efficient spiking neural networks for artificial intelligence.

## Contribution

The first integration of synthetic ferrimagnets and interlayer Dzyaloshinskii–Moriya interaction for neuromorphic computing is introduced.

## Key findings

- IL‐DMI-induced effective field increases twentyfold in synthetic ferrimagnets.
- Analog-like switching behavior is achieved through IL‐DMI, spin-orbit torque, and thermal effects.
- A non-probabilistic leaky-integrate-fire neuron device is developed for spiking neural networks.

## Abstract

Interlayer Dzyaloshinskii–Moriya interaction (IL‐DMI) in synthetic magnetic structures has attracted extensive interest for greatly facilitating deterministic spin‐orbit torque (SOT)‐driven information writing and topologically non‐trivial 3D magnetic Hopfion forming. However, its distinct role in synthetic ferrimagnets (SFi) remains unexplored, where the conjunction of asymmetric magnetic moments and antisymmetric nature of IL‐DMI leads to more diverse spin configurations and applications. Here, we reveal the unidirectional and chiral nature of IL‐DMI in SFi, further unlocking application directions of IL‐DMI in neuromorphic computing. Particularly, the IL‐DMI‐induced effective field increases approximately twentyfold while interacting with two asymmetric antiparallel‐aligned moments, greatly facilitating future IL‐DMI detection. Unlike previous digital‐like switching, we find that the interplay of IL‐DMI, SOT, and thermal effect gives rise to an analog‐like switching behavior. Leveraging this, we develop an SOT‐based non‐probabilistic leaky‐integrate‐fire neuron device utilizing the micromagnetic analog‐like switching model. Compared to probabilistic neurons, this provides a hardware support Spiking neural network, interlayer Dzyaloshinskii–Moriya interaction, spin‐orbit torque, synthetic ferrimagnetsfor ultralow power, high‐sparsity, and high‐accuracy spiking neural networks.

This work introduces a groundbreaking integration of asymmetric magnetic structures (synthetic ferrimagnets) and antisymmetric magnetic interaction (interlayer Dzyaloshinskii–Moriya interaction) for the first time. It addresses the critical challenge of IL‐DMI detection and shows the discovery of unprecedented analog‐like spin‐orbit torque switching. The analog switching naturally matches the neuron characteristics in spiking neural networks, promising for ultralow‐power, high‐sparsity, and high‐accuracy artificial intelligence hardware.