AI-driven modeling of modulated magnetoelectric magnetic field sensors

Modulated magnetoelectric (ME) sensors enable the detection of low-frequency, small-amplitude magnetic fields. Their performance is governed by a high-dimensional parameter space that combines the nonlinear coupling of magnetic, electric, and mechanical properties with the system electronics and multiple operating conditions. This complexity makes many tasks challenging, such as application-specific sensor design, systematic performance analysis, and identifying ideal operating conditions. Here, we take the first step toward navigating this complexity by introducing AI-driven surrogate models that systematize operating point selection and characterization workflows. Three artificial neural networks are trained on measured data of a modulated ME sensor based on the ΔE effect. The networks predict sensor admittance, noise spectral density (NSD), and signal output based on fundamental operating parameters, such as the excitation amplitude, excitation frequency, and the magnetic bias flux density. The models achieve accurate performance with sub-percent errors for sensor admittance, percent-level error for the NSD, and a 90th percentile error with 5% for the signal model. Based on the trained surrogates, the limit of detection can be reproduced reliably over most of the operating regime. A data-efficiency analysis further examines how model performance depends on the amount of training data. Finally, the surrogate models are used to solve three different operating point optimization problems of increasing complexity. Using a ΔE-effect sensor as a representative example, this work demonstrates that data-driven surrogate modeling can support characterization and optimization of modulated ME sensors. The approach may be extended to other ME sensor concepts and ultimately to device and array optimization.