Opportunities toward Scalable Domain-Wall-Based Spintronic Devices.
- 1Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle (Saale), Germany.
Abstract
Since its initial proposal in 2004, racetrack memory, which utilizes current-induced motion of domain walls within a magnetic nanowire, has evolved from a conceptual spintronic device into a mature research platform supported by major advances in materials engineering, nanotechnology, domain-wall physics, and spin-torque mechanisms. Yet, practical racetrack memory or logic remains challenging. The field has entered a new state in which the dominant challenges are no longer associated with driving domain walls or understanding the fundamental mechanisms behind them, but with achieving reliable device operation and fabrication of practical nanoscale devices. Key bottlenecks include the electrical readout of nanoscopic racetrack devices, the integration of magnetic tunnel junctions, the control of stochastic domain-wall behavior, positioning accuracy, and scalability toward three-dimensional (3D) architectures. We briefly summarize the current status of the field and discuss how these challenges redefine research priorities while creating opportunities for advances in electrical readout, 3D device integration, and novel domain-wall-based computing architectures. By highlighting emerging directions in device engineering and advanced characterization, we outline pathways toward translating laboratory-scale demonstrations into scalable, manufacturable spintronic memory and logic technologies.
Related
- Integrated Photonic Devices in Thin-Film Barium Titanate: Opportunities and Challenges
- Classification of chiral domain-wall dynamics through symmetry analysis
- Non-reciprocity and exchange-spring delay of domain-wall Walker breakdown in magnetic nanowires with azimuthal magnetization
- Domain-wall-to-domain interconversion in a polar conducting oxide
- Crossover from universal depinning to free domain-wall dynamics in thin iron garnet films
BibTeX
@article{jeon2026opportunities,
title = {{Opportunities toward Scalable Domain-Wall-Based Spintronic Devices.}},
author = {Jae-Chun Jeon and Lukas Fischer and Yung-Cheng Li and Renata Lei and Stuart S P Parkin},
journal = {ACS nanoscience Au},
year = {2026},
month = sep,
doi = {10.1021/acsnanoscienceau.6c00113},
url = {https://europepmc.org/article/MED/42921100},
note = {CC BY},
}
RIS
TY - JOUR
TI - Opportunities toward Scalable Domain-Wall-Based Spintronic Devices.
AU - Jae-Chun Jeon
AU - Lukas Fischer
AU - Yung-Cheng Li
AU - Renata Lei
AU - Stuart S P Parkin
PY - 2026
DA - 2026/09/09
JO - ACS nanoscience Au
DO - 10.1021/acsnanoscienceau.6c00113
UR - https://europepmc.org/article/MED/42921100
AB - Since its initial proposal in 2004, racetrack memory, which utilizes current-induced motion of domain walls within a magnetic nanowire, has evolved from a conceptual spintronic device into a mature research platform supported by major advances in materials engineering, nanotechnology, domain-wall physics, and spin-torque mechanisms. Yet, practical racetrack memory or logic remains challenging. The field has entered a new state in which the dominant challenges are no longer associated with driving domain walls or understanding the fundamental mechanisms behind them, but with achieving reliable device operation and fabrication of practical nanoscale devices. Key bottlenecks include the electrical readout of nanoscopic racetrack devices, the integration of magnetic tunnel junctions, the control of stochastic domain-wall behavior, positioning accuracy, and scalability toward three-dimensional (3D) architectures. We briefly summarize the current status of the field and discuss how these challenges redefine research priorities while creating opportunities for advances in electrical readout, 3D device integration, and novel domain-wall-based computing architectures. By highlighting emerging directions in device engineering and advanced characterization, we outline pathways toward translating laboratory-scale demonstrations into scalable, manufacturable spintronic memory and logic technologies.
SN - 2694-2496
ER -