Enhancement of spin–orbit torque in sputtered BiSb-based perpendicular magnetic tunnel junctions for neuromorphic computing applications

S S. Wu G G. J. Lim (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) F F. N. Tan (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) T T. L. Jin (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) C C. C. I. Ang (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) E E. K. Koh (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) S S. H. Lee (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) K K. J. Cheng (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,) W W. S. Lew (School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,)

Abstract

Topological insulators offer unique properties for generating high spin–orbit torque (SOT), promising to revolutionize magnetoresistive random-access memory with a low power consumption. In this work, BiSb is integrated into perpendicular magnetic tunnel junctions (pMTJs) to enable efficient SOT switching. By optimizing the BiSb thickness and introducing a Ta buffer layer, a threefold enhancement in damping-like SOT efficiency and a 60% reduction in switching current are achieved compared to the BiSb-free sample. X-ray diffraction measurements confirm the improved crystalline quality with increasing BiSb thickness, contributing to the enhanced spin current generation. The fabricated BiSb-pMTJs exhibit key neuromorphic functionalities, including gradual long-term potentiation/depression and sigmoidal resistance modulation under pulsed current. Utilizing these features, a three-layer artificial neural network is implemented based on experimentally extracted device behavior, achieving over 90% accuracy in handwritten digit recognition.

Article Details

Volume / Issue Vol. 127, Issue 13
Published September 30, 2025
ISSN 0003-6951
Publisher American Institute of Physics

Journal Info

Applied Physics Letters

American Institute of Physics

ISSN: 0003-6951 Physical Sciences

Authors (9)

S

S. Wu

G

G. J. Lim

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

F

F. N. Tan

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

T

T. L. Jin

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

C

C. C. I. Ang

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

E

E. K. Koh

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

S

S. H. Lee

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

K

K. J. Cheng

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,

W

W. S. Lew

School of Physical and Mathematical Sciences, Nanyang Technological University , 21 Nanyang Link, Singapore 637371,