A Magnetoelastic Effect-Guided Design for Micro Stress/Strain Detector Using Deep Learning

Stress/strain detectors with high accuracy, low hysteresis, and wide range are important requirements in many application fields, such as healthcare, microfabrication, and human–machine interfaces. Here, we develop a stress/strain detection method based on deep learning (DL) to efficiently estimate the value of the external stress/strain field from the magnetic domain configuration in the ferromagnetic Tb0.27Dy0.73Fe2 with strong magnetoelastic couplings. By using micromagnetic simulation, it is observed that the stress-driven magnetic response in Tb0.27Dy0.73Fe2 possesses characteristics of small hysteresis, which helps the design of high-performance sensors for dynamic detection. More importantly, our DL model can accurately distinguish different magnetic domains under the strain variation of
$\sim 1.25\times 10^{-{5}}$
, realizing the measurement of microstrains on the order of ~10−5. Further analyses of feature maps demonstrate that our network can effectively extract subtle discrepancies among the different magnetic domains, thereby accurately inferring the relationship between magnetic domains and stresses/strains. This work provides a new design approach for stress detection, which is desirable for remote and nano-micrometer detection of stress/strain in special scenarios.