Engineered Inclined Energy Landscapes Enabling Free Flow of Magnetic Microstructures for Artificial Neuron Applications

TL;DR

This paper presents a novel engineered energy landscape for magnetic microstructures that enables free flow and emulation of neuron functions, significantly reducing energy consumption for neuromorphic computing applications.

Contribution

The authors introduce a sawtooth-type energy landscape design that improves microstructure dynamics, reliability, and energy efficiency in spintronic neuromorphic devices.

Findings

Achieved free flow of magnetic microstructures with engineered landscapes.

Emulated integrate-and-fire neuron behavior with low energy per spike.

Reduced energy consumption to 23.66 fJ per spike.

Abstract

Spintronic-based brain-inspired neuromorphic computing has recently attracted significant attention due to the exceptional properties of magnetic microstructures, including nanoscale dimensions, high stability, and low energy consumption. Despite these advantages, the practical integration of such microstructures into functional devices remains challenging. Fabrication processes are often complex and prone to stochastic effects, such as unwanted pinning and thermal-induced instabilities, which limit device reliability and scalability. Addressing these challenges is crucial for advancing spintronic neuromorphic architectures toward real-world applications. Thus, to reduce these effects we have proposed a design which is experimentally feasible and require less energy as compared to existing one. By engineering the system anisotropy into a sawtooth-type energy landscape, we have achieved free flow of these microstructures and successfully emulated integrate and fire (IF) function of biological neuron. Thus, proposed design presents an experimentally reliable and energy efficient external stimuli approach for tailoring magnetic microstructures dynamic behaviours, resulting in low energy consumption of 23.66 fJ per spike paving the way for the development of skyrmion-based futuristic neuromorphic computing device applications.