Near-surface defect detection in ultrasonic testing using domain-knowledge-informed self-supervised learning.

Recently, significant research efforts have been made to enhance ultrasonic testing (UT) by employing artificial intelligence (AI). However, collecting an extensive amount of labeled data across various testing environments to train the AI model poses significant challenges. Moreover, conventional UT typically focuses on detecting deep-depth defects, which limits the effectiveness of such methods in detecting near-surface defects.

The dependency of tunnel magnetoresistance ratio on nanoscale thicknesses of Co2Fe6B2 free and pinned layers for Co2Fe6B2/MgO-based perpendicular-magnetic-tunnel-junctions.

The tunnel magnetoresistance (TMR) ratio of a cobalt-iron-boron (CoFeB)-based perpendicular-magnetic-tunnel-junction (p-MTJ) spin valve is extremely sensitive to both nanoscale Co2Fe6B2 free- and pinned-layer thicknesses. The TMR ratio peaks at a Co2Fe6B2 free-layer thickness of 1.05 nm, while it peaks at a Co2Fe6B2 pinned-layer thickness of 1.